In addition, automatic delineation of the device demonstrated equivalent performances

● On the “truthing” and data collection process

The contouring guidelines followed to produce the contours were confirmed with the centers

which provided the data. Our truthing process includes a mix of data created by different

delineators (clinical experts) and assessmentofintervariability,groundtruthcontoursprovided

by the centers and validated by a second expert of the center, and qualitative evaluationand

validation of the contours. This processensuresthatthedatausedfortrainingandtestingcan

be considered representative of the delineation practice across centers and following

● On clinical subgroups, confounders and equipment details

In general, confounding factors affecting health status present in the dataset could berelated

to patient clinical variables such as age, gender, ethnicity, economicalandeducationallevels.

As shown in “Demographic distribution including gender, age and ethnicity”, our datais

representative of the demographic cancer distribution in terms ofgenderandage.Inaddition,

our models when appropriate (i.e. for gender independent anatomies) are shared across

gender removing any further bias and augmenting substantially training cohorts.

Variables like ethnicity, economical and educational status that could be associated with

obesity are further confounding factors that could impact global patient's anatomy and

introduce bias in the performance of the obtained solution. To address this aspect, we have

adopted a strategy that projects a patient's specific anatomy to common, multiple, differentin

size, full-body female and male patient templates, allowing a direct harmonization of data

resulting in potential removal of bias of anatomical diversity across ethnic, economical and

educational groups. Please note that this information (ethnic group, educational/economical

level, etc.) is often not available in the pseudo-anonymised data and therefore performing

statistical tests and increasing thenumberofoperationsallowingtoseparatecorrelationsfrom

Regarding variables associated with treatment therapeutic and treatment implementation

factors as differences exist among CT and MR scanners manufacturers that could potentially

introduce bias. We have addressed this concern through a statistical analysis of the different

imaging vendors in EU & USA towards the creation of a data training, validation and testing

cohort that globally appropriately representsthemarketshareofthedifferentvendorsallowing

generalization and removing hardware specific bias. In terms of treatment implementation, it

should be noted that differentguidelinesexistanddependingonthetreatmentdevicedifferent

therapeutic constraints and guidelines are applied. This is reflected in our database since

different strategies and constraints are used depending on the choice of treatment (e.g

external radiotherapy vs stereotactic treatment). Our solution, due to its concept of removing

bias through projection to patient template anatomies as well asduetothecomponent-based

approach that is abletoaggregatetrainingdataacrossimagingandtreatmentvendors,isable

to address the maximum set of constraints. Therefore, we do not introduce any bias on the

treatment from manufactures such as Varian, Elekta, Accuray, GE, Siemens, ViewRay & Zap


Test Name
Test Description/Results
Results

This document is intended to document the
usability test results for the ART-Plan v1.10.0 for
compliance with IEC 62366-1:2015+AMD1:2020 -
Medical devices - Application of usability
engineering to medical devices.

The ART-Plan was assessed with regards to
usability for compliance with each section of IEC
62366

Usability - Testers qualification
(V1.10.0)

This table shows that European medical physicists
who have participated in the evaluation have at
least an equivalent expertise level compared to a
junior US medical physicist (MP), and
responsibilities in the radiotherapyclinicalworkflow
are equivalent

Literature Review and
Performance Criteria
Extraction Report for ART-Plan
(V1.9.0 and V1.10.0)

A literature review is performed to establish
acceptance criteria for performance of ART-Plan
modules using performance ranges observed from
benchmark devices and alternative technologies in
the literature. All measures of performance that
were established in this document were supported
by clinical evidence. It was also demonstrated,
from the clinical data, that ART-Plan has a clear

The ART-Plan was evaluated for its safety and effectiveness based on the following testing:


clinical relevance in accordance with the clinical
state of the art.

ART-Plan performance testing
- Overview
(V1.6.1-V1.10.0)

The document summarises all performance tests
that have been performed since the last FDA
cleared version (1.6.1). It also showswhichcriteria
have been met in each test for all modules of
ART-Plan. It demonstrates that all modules of
ART-Plan pass at least one performance
acceptance criterion and hence are clinically
acceptable for release.

Study Protocol and Report
Annotate Performances
Summary (V1.9.0)

The testing demonstrated that Annotate provides
acceptable contours for the concerned structures
on an image of a patient.

Abdo MRI auto-segmentation
performances according to
AAPM requirements (V1.8.0)

Mean DSC of each organ was compared with the
tolerance threshold of 0.8. After comparing the
contours of 3 different experts on the samepatient
the mean DSC was calculated, compared with the
auto-segmentation and was observed to be in
every case superior. It is concluded that the
auto-segmentation algorithm provides clinical
acceptable contours.

Testing protocol/report - Brain
MRI autosegmentation
performances according to
AAPM requirements
Qualitative validation of
autosegmentation
performances - Brain (V1.8.0)

In this test, some organs did not meet the
acceptance criteria. However, the value of 0.80 is
indicated by the AAPM as the “uncertainty of
contouring of the structure” which in fact can be
significantly below 0.80 depending on the organ.
Thus, we also decided to evaluate in parallel the
models with a qualitative evaluation of our
predictions (see additional qualitative test below).
All organs except left and right cochlea passed at
least one of the acceptance criteria demonstrating
that the Annotate module provides acceptable
contours on MR brain structures. The MR brain
model has been further improved, subjected to
further testing, and, after providing acceptable
contours for all structures (incl. the cochlea),
released in v1.10.0. (see Study Protocol and
Report- Autosegmentation performances against
inter-expert variability - Brain MR (V1.10.0)).

Qualitative validation of
auto-segmentation
performances - Gyneco
(V1.8.0)

The testing demonstrates that Annotate provides
acceptable contours for a specific list of
gynecological structures on a Female pelvis CT
image. Three testing methods are used: DICE
calculation, Inter-expert DICE calculation and


qualitative Indicator. All structures passed at least
one of the acceptance criteria and were released.

Pelvis MRI auto-segmentation
tool performances according to
AAPM requirements (V1.8.0)

The testing demonstrates that the
auto-segmentation algorithm for Pelvis MRIs
provides acceptable contours for the concerned
structures on an image of a patient.Allorgansmet
at least one of the acceptance criteria and
therefore were considered acceptable.

Qualitative & Quantitative
validation of fusion
performances (V1.9.0)

The study was developed to cover the major
clinical use cases in which fusions are used in the
radiotherapy workflow and split into as many
sub-studies as clinical use cases of fusion in
radiotherapy workflow. The results show that both
types of fusion algorithms (Rigid & Deformable) in
SmartFuse pass the performed tests, and provide
valid results for further clinical use in radiotherapy.

Study Protocol and Report for
qualitative validation of fusion
performances for
tCT_sCT_injected/PET
modality (V1.9.0)

The study evaluated the quality oftherigidandthe
deformable fusion algorithms of the SmartFuse
module for the following cases:
- CT injected image fuse towards CT image
- CT-PET image fuse towards CT image.
Both types of fusion algorithms, rigid and
deformable, provided clinically acceptable results
for the desired clinical uses.

Study Protocol & Report for
qualitative validation of ITV
calculation performances for
4D_CT modality (V1.9.0)

The testing evaluated the quality of ITVcalculation
algorithm of the Annotate module in the case of
4D-CT examinations. Acceptable results were
reached for the evaluation of contours propagation.

Study Protocol and Report for
validation of fusion
performances for tCT_sMR
modality (V1.9.0)

This testing evaluated the performances of the
SmartFuse module for the clinical caseoffusionof
an MRI towards a planning CT to aid in the
delineation. Acceptable results were reached for
this evaluation.

Study Protocol and Report for
qualitative validation of fusion
performances for tMR_sCT
modality (V1.9.0)

This study evaluated the performances of the
SmartFuse module for fusion of CTs towards
planning MRIs for the purpose of electron density
transfer. Acceptable results were reached for this
evaluation.

Study Protocol and Report for
qualitative & quantitative
validation of fusion
performances for tMR_sMR
modality (V1.9.0)

This study evaluated the performances of the
SmartFuse module on the clinical case of using
fusion for MRI replannification.Favorableresultsto
the established performance criteria for rigid and
deformable registrations, and for all organs, were
obtained.

Protocol for qualitative &
quantitative validation of fusion
performances for
tCT_sCT_replanning modality
(V1.9.0)

This study evaluated thequalityoftherigidandthe
deformable fusion algorithms of the SmartFuse
module for replanification of CT-based treatments.
Acceptable results were reached for this
evaluation.


Pilot study for sample size
estimation - literature review
(V1.9.0)

Theliteraturereviewwasperformedtoestimatethe
appropriate sample size of the testing data set
towards demonstrating the performance of the
image registration, segmentation and pseudo-CT
generation solutions on the basis of the most
recent and most relevant scientific literature.

Autoseg 2D regression test for
integration in ART-Plan V1.9.0

The objective of the test was to demonstrate
equivalence between the version (V1.9.0) and
previous versions of Annotate (V.1.8 and v1.6.1).
All organs passed at least one of the defined
criteria, and hence were accepted for release in
v1.9. Given that equivalence TheraPanacea
considers all tests (especially the qualitative ones)
performed on previous versions of the software to
be still relevant.

Study Protocol & Report
(SPR): External contour
non-regression protocol for
integration in ART-Plan
V1.10.0

This test demonstrates equivalence between the
version V.1.10 and V.1.9 of the external contours
and show that the new added anatomies in the
module Annotate provides clinically acceptable
contours. The external contour for all anatomies
passed the defined criteria, and hence were
accepted for release in the v1.10.

Testing Protocol/Report -
Autoseg CT, MR
non-regression test for
integration in ART-Plan
V1.10.0

This test demonstrates equivalence between the
version v.1.10 and v.1.9 of the auto-segmentation
models for all structures and shows that the
updated models provide clinically acceptable
contours. All organs passed the defined criteria,
and were hence accepted for release in the v1.10.

Study Protocol and Report
Qualitative Validation of
Annotate in ART-Plan V1.10.0
for Thorax

This test demonstrates that Annotate provides
acceptable contours for structures of the thorax
region: thoracic aorta and bronchial trees. This
qualitative test was performed as an addition to
Section 18.21 to ensure the contours are clinically
acceptable.

Study Protocol and Report-
Autosegmentation
performances against
inter-expert variability - Brain
MR (V1.10.0)

This test demonstrates that the Annotate provides
clinically acceptable (compared to inter-expert
variability) for all MR-T1 Brain structures. Existing
structures present in the previous version (v.1.8 -
see Section 18.8) were also re-evaluated since a
complete retraining of the model was done.

Study Protocol and Report
Qualitative Validation of
Annotate in ART-Plan V1.10.0
for Pelvis Truefisp model

This test demonstrates that the module Annotate
provides acceptable contours for 9 organs
evaluated on MR Truefisp images of patients. All
organs have passed the acceptance criterion of
reaching a percentage of at least 85% of A or B
(qualitative evaluation) and hence can be released
in v.1.10.0.

Study Protocol and Report
Qualitative Validation of
Annotate in ART-Plan V1.10.0
for H&N Lymph nodes

This test demonstrates that Annotate provides
acceptable contours for following cervical lymph
nodes levels: Ia, Ib right, VIIa left, VIIb right, IIleft,
III right, V left, IVb right, IVb left. All cervicallymph
nodes having reached a percentage of at least


85% of A or B, the performance of auto
segmentation is demonstrated, and hence all
structures were included in V1.10.

Study Protocol and Report
Qualitative Validation of
Annotate in ART-Plan V1.10.0

This test demonstrates that Annotate provides
clinically acceptable contours, following qualitative
measures, for the new version v.1.10. It serves as
an additional evaluation to Section 18.21, andwas
done on a set of organs of all anatomies for both
the CT and MR models. The benchmarking was
done not only against thequalitativeevaluationbut
also against the manual contours and a previously
validated version of the models. All contours can
be considered as acceptable as at least one
criterion was met for each of the included
structures.

Study Protocol and Report
Annotate Performances
Summary (V1.10.0)

The purpose of this document is todescribeallthe
testing protocols and testing results for validating
the performance of the Annotate module.
Since all organs added in v.1.10.0 of Annotate
have passed at least one test andmetatleastone
acceptance criteria, all organs have been released.

Testing: pseudo-CT clinical
performance and comparison
to predicates (pelvis)
(V1.10.0)

The evaluation demonstrated the non-inferiority of
using Annotate’s pseudo-CTfortreatmentplanning
in terms of dosimetric measures as compared to
CT-based treatment planning. Our pseudo-CT for
pelvis has shown to produce results thatmeetthe
acceptance criteria derived from clinical practice
and literature review as well as to perform at least
as good as two FDA cleared devices for
pseudo-CT generation.

Testing: pseudo-CT clinical
performance and comparison
to predicates (brain)
(V1.10.0)

Study Protocol & Report
(SPR): Testing:
Autosegmentation
performances against
predicates
(V1.10.0)

This evaluation demonstrated the non-inferiority of
using ART-Planv1.10.0forannotationoforgansas
compared to other devices which have been
cleared for use in the US. In addition, ART-Plan
v1.10.0 offers almost 3 times (2.72) more organs
than MIM/ContourProtege AI, which represents an
additionalbenefittotheusersascomparedtoother
devices.

In this test, Annotate demonstrated equivalent
performances between non-US and US population


performances on Thorax US
data
(V1.10.0)

for the Thorax localisation. Considering that this is
a worst case scenario of morphological
deformation due to factors such as age, gender or
weight in abdominal region, we claim that
considering any localisation included in the
intended use of ART-Plan, any autosegmentation
result demonstrated on a non-US population can
be generalized to a US population. Nonetheless,
TheraPanacea has performed an additional
evaluation on pediatric US-data covering all other
localisations included in the intended use of the
device.

Clinical evaluation of automatic
segmentation on Pediatric
images
(V1.10.0)

Considering the fact that all the structures have
reached a percentage of 90% (>=85%) of A or B
for the MR brain model, and that 11/15 structures
passed with success for the CT model,
Therapanacea claims that the performance ofauto
segmentation on said organs has been
demonstrated for the studied population.
These results highlight the high generalizability of
the commercial tool, initially made for adults, to
pediatric cases and its clinical implementation
feasibility. Note that this study served to
demonstrate that ART-Plan’s Annotate, trained on
European data, can be generalised to the US
population given that it would be clinically
acceptable even for pediatric cases where a more
prominent change in size is expected thantheone
between two adults from different countries. This
does not mean that TheraPanacea is claimingthat
ART-Plan should be used for pediatric patients.

System Verification and
Validation Testing

The system verification and validation testing was
performed to verify the software of the ART-Plan.


DHT8B: Division of Radiological Imaging Devices

Office of Product Evaluation and Quality


Attribute
Subject BrainInsight
Predicate BrainInsight (K202414)

BrainInsight is intended for automatic labeling,
spatial measurement, and volumetric
quantification of brain structures from a set of
low-field MR images and returns annotated and
segmented images, color overlays and reports.

Patient Population
Adult (≥ 18 years)
Same

● Automated measurement of brain tissue
volumes and structures of AI-reconstructed
low-field MR images
● Automatic segmentation and quantification
of brain structures of AI-reconstructed low-
field MR images using machine learning
tools

● Automated measurement of brain
tissue volumes and structures of
conventional low-field MR images
● Automatic segmentation and
quantification of brain structures of
conventional low-field MR images
using machine learning tools

MR images are automatically sent to
BrainInsight, and processed images are
automatically returned in ~7 minutes

User Interface /
Physical
Characteristics

● No software required
● Operates in a serverless cloud environment
● User interface through PACS (multiple
vendors)

Automated internal pipeline that performs:
● segmentation
● volume calculation
● distance measurement
● numerical information display

● MRI Scanner: Hyperfine Swoop FSE MRI
scans acquired with specified protocols
● Supports DICOM format as input

Provides volumetric measurements of brain
structures:
● Includes segmented color overlays and
morphometric reports

The table below compares the subject device to the predicate.


● Supports DICOM format as output of results
that can be displayed on DICOM
workstations and PACS

Automated quality control functions:
● Tissue contrast check
● Scan protocol verification
● Atlas alignment check
● Results must be reviewed by a trained
physician


(cid:72)(cid:3)(Select one or both, as applicable)


The Logicon Caries Detector is a
software device that is an aid in the
diagnosis of caries that have
penetrated into the dentin, on un-
restored proximal surfaces of
secondary dentition through
the statistical analysis of digital
intraoral radiographic imagery. The
device provides
additional information for the clinician
to use in his/her diagnosis of a tooth
surface suspected of being carious. It
is designed to work in conjunction
with an existing Carestream dental
RVG digital X-ray radiographic
system with dental imaging software
(dis) for Windows XP or higher.

Overjet’s Calculus Assist (OCalA)
software automatically detects
interproximal calculus on bitewing and
periapical radiographs. It is intended to aid
dentists in the detection of calculus. It
should not be used in lieu of full patient
evaluation or solely relied upon to make or
confirm a diagnosis. The system is to be
used by professionally trained and licensed
dentists.

Patients requiring dental services, all
sexes, no
age restriction

Patients requiring dental services, all sexes,
18 years of age or older.

Platform
Windows PC
Web - Edge, Chrome, Firefox

User Interface
Mouse, Keyboard
Mouse, Keyboard, Trackpad


Images can be scanned, loaded from
connected Carestream image solutions

Images imported from the radiographic
device, or from the practice management
system

JPEG, PNG, JFIF, JIF, TIFF, EOP, BMP,
DICOM

The software provides graphical
representation of the density change in
a tooth, by looking for a
pattern of density dips starting at the
tooth surface, penetrating the enamel
and going into the dentin. Enamel is
represented by 10 green lines and
dentin by 5 blue lines. If a pattern
suggestive of caries exists, the dips are
highlighted with red dots to warn the
dentist.

Three layers:
1 - The Network layer works with the
practice PACS or EMR to transmit the
image and meta-data to Overjet.
2 - The decision layer processes the image
to ensure it is the correct data type, and
then annotates it via the algorithm
3 - The presentation layer displays the
annotated image in a non-diagnostic
viewer. The dentist can filter, display, hide,
create and edit the annotations presented.

Bitewing radiographs acquired from
Carestream
dental RVG digital X-ray radiographic
system

Bitewing and periapical radiographs of at
least 500 x 500 pixels.

●Outline of suspected region
●Tooth Density
●Lesion (caries) probability

Calculus detection on radiograph resulting
in bounding box outline of suspected
calculus

Increase in dentist’s sensitivity of
approximately 20%

Superiority of aided reader versus unaided
reader performance


curve and associated AUC. Results are shown in the following table.


Predicate Device
FDA Clearance Number
Product
Manufacturer

are substantially equivalent to the following predicate devices:


Reference Device
FDA Clearance Number
Product
Manufacturer


Performance Test
Tested Hardware or Software
Source/Rationale for test

Transferred hardware and
software features

Guidance for the Content of
Premarket Submissions for
Software Contained in Medical
Devices / 21 CFR §820.30


Medical electrical equipment -
Part 2-33: Particular
requirements for the basic
safety and essential
performance of magnetic
resonance equipment for
medical diagnosis

Medical devices - Application of
risk management to medical
devices

Medical devices - Part 1:
Application of usability
engineering to medical devices

Medical device software -
Software life cycle processes
[Including Amendment 1 (2016)]

Digital Imaging and
Communications in Medicine
(DICOM) Set 03/16/2012
Radiology

Medical electrical equipment -
Part 1-2: General requirements
for basic safety and essential
performance - Collateral
Standard: Electromagnetic
disturbances - Requirements
and tests

Characterization of
Radiofrequency (RF) Coil
Heating in Magnetic Resonance
Imaging Systems

Acoustic Noise Measurement
Procedure for Diagnosing
Magnetic Resonance Imaging
Devices

Standards Publication
Characterization of Phased
Array Coils for Diagnostic
Magnetic Resonance Images


215 Brighton Avenue, Suite 203
Boston, MA 02134

Yervant Chijian
Quality and Regulatory Consultant
Pharmalex Pty Ltd

Email: Yervant.Chijian@pharmalex.com
Phone: +61 (0)2 9906 2984


Classification Name
System, Image Processing, Radiological

Automated Radiological Image Processing Software


EchoMD Automated Ejection Fraction Software

Picture archiving and communications system


Libby™ Echo:Prio is software that
is used to process previously
acquired DICOM-compliant

The Bay Labs, Inc. EchoMD
Automated Ejection Fraction
software is used to process

Table 1: Libby™ Echo:Prio Device Comparison.


cardiac ultrasound images, and to
make measurements on these
images in order to provide
automated estimation of several
cardiac measurements. The data
produced by this software is
intended to be used to support
qualified cardiologists,
sonographers, or other licensed
professional healthcare
practitioners for clinical decision-
making.
Libby™ Echo:Prio is indicated for
use in adult patients.

previously acquired transthoracic
cardiac ultrasound images, to store
images, and to manipulate and
make measurements on images
using a personal computer or a
compatible DICOM-compliant
PACS system in order to provide
automated estimation of left
ventricular ejection fraction. This
measurement can be used to assist
the clinician in a cardiac
evaluation. The EchoMD
Automated Ejection Fraction
Software is indicated for use in
adult patients.

Intended User
Cardiologists and sonographers
Cardiologists and sonographers
Same

Intended Location
Medical facility
Medical facility
Same

The Libby™ Echo:Prio is an image
post-processing analysis software
device used for viewing and
quantifying cardiovascular
ultrasound images.

EchoMD software is process
acquired transthoracic cardiac
ultrasound images, to analyze and
make measurements on images in
order to provide automated
estimation of left ventricular
ejection fraction

Automated measurements
LV Ejection fraction (EF)
Left ventricular ejection fraction
Same

Fully automated, including clip
selection

Qualitative user feedback on
transthoracic cardiac
ultrasound image quality

Qualitative and quantitative user
feedback on transthoracic cardiac
ultrasound image quality

Single plane & biplane (with
segmentation and endocardial
trace)

Biplane (non-segmentation/ non-
endocardial trace)

LIBBY™ ECHO:PRIO Traditional 510(k) Premarket Notification


Offline EF evaluation using
Yes
Yes
Same

Automated Ejection Fraction
Yes
Yes
Same

User Confirmation/rejection
Yes
Yes
Same

Software package that operates on
off-the-shelf hardware

Not equivalent
but no issues
with safety and
efficacy.

The software processes DICOM
compliant image data

The software is designed for use
within a web browser on a personal
computer.

The software is designed for use
on personal computer or a
compatible DICOM-compliant
PACS system.


Deep Learning Image
Reconstruction
(Proposed Device)

Utilizes a dedicated Deep Neural
Network (DNN) designed specifically
to generate high quality CT images

Select recon type and strength (Low,
Medium, High)

Using the same Reference protocols
provided on the Revolution CT
family systems for ASiR-V

Using the same Reference protocols
provided on the Discovery CT750
HD family CT systems for ASiR-V

Image noise, low contrast
detectability, spatial resolution, and
low signal artifact suppression as
good or better than ASiR-V on
Revolution CT family systems

Image noise, low contrast
detectability, spatial resolution, and
low signal artifact suppression as
good or better than ASiR-V on
Discovery CT750 HD family CT
systems

predicate device and the proposed device:


AAMI ANSI ES60601-1:2005/(R)2012 and A1:2012,
C1:2009/(R)2012 and A2:2010/(R)2012 (Consolidated Text) Medical
electrical equipment - Part 1: General requirements for basic safety
and essential performance (IEC 60601-1:2005, MOD)

IEC60601-1-2: 2014(4th Edition) , Medical electrical equipment -
Part 1-2: General requirements for basic safety and essential
performance - EMC

IEC 60601-2-37 Edition 2.0 2007, Medical electrical equipment –
Part 2-37: Particular requirements for the basic safety and essential
performance of ultrasonic medical diagnostic and monitoring
equipment

AAMI / ANSI / ISO 10993-1:2009/(R)2013, Biological evaluation of
medical devices – Part 1: Evaluation and testing within a risk
management process

ISO 14971:2007, Medical devices - Application of risk management
to medical devices

NEMA UD 2-2004 (R2009) Acoustic Output Measurement Standard
for Diagnostic Ultrasound Equipment Revision 3


Number of correctly detected frames
×100
Total number of frames with nerve

1000
Average latency time of each frame (msec)


Validation Type
Average
Standard Deviation
95% CI

Summary Performance data, Standard Deviations & Confidence Intervals:


Aquilion Precision
(TSX-304A/1 and /2)
V8.8 with AiCE

JAK:
System, X-ray,
Tomography,
Computed


D
evice Name,
Aquilion Precision (TSX‐
Aquilion Precision (TSX‐

Model Number
304A/4) V10.10 with AiCE
304A/1 and /2) V8.8 with AiCE

• Scan Regions
Abdomen and Pelvis / Chest
Abdomen and Pelvis

-Helical scan
(Not applicable to tilt scans)
- Volume scan
(Not applicable to W-Volume
tilt scans)
- vHP*

Tilt scans are applicable
with AiCE BODY SHARP,
LUNG and CARDIAC. For
BODY parameters, ECG-
gated scan, respiratory-
gated scan, W-Volume
tilt scan and tilt helical
scan cannot be applied.
*: Option

• ECG-gated scan
Available in BODY SHARP,

*: Excluding volume scan
and dynamic volume
scan.

• Magnified
reconstruction -
Available
magnification
Size (D-FOV)

±30°
Axial and helical scanning
• Tilt scans are applicable
with AiCE BODY SHARP,
LUNG and CARDIAC
• For BODY parameters,
ECG-gated scan,
respiratory-gated scan,
W-Volume tilt scan and
tilt helical scan cannot be
applied

±30°
Axial and helical scanning
• Helical and W-Volume tilt
scans are not applicable
with AiCE


AiCE/FIRST (CCRS-003B) can
be installed in the console
unit

Separate unit, I-REC BOX, was
used as the AiCE/FIRST (CCRS-
002A) server


Manufacturer
Device Name
Application No.


The Imbio RV/LV Software device is
designed to measure the maximal
diameters of the right and left ventricles of
the heart from a volumetric CTPA
acquisition and report the ratio of those
measurements. RV/LV analyzes cases
using an artificial intelligence algorithm to
identify the location and measurements of
the ventricles. The RV/LV software
provides the user with annotated images
showing ventricular measurements. Its
results are not intended to be used on a
stand-alone basis for clinical decision-
making or otherwise preclude clinical
assessment of CTPA cases.

The Viz RV/LV Software device is
designed to measure the maximal
diameters of the right and left ventricles of
the heart from a volumetric CTPA
acquisition and report the ratio of those
measurements. Viz RV/LV analyzes cases
using an artificial intelligence algorithm to
identify the location and measurements of
the ventricles. The Viz RV/LV software
provides the user with annotated images
showing ventricular measurements. Its
results are not intended to be used on a
stand-alone basis for clinical decision-
making or otherwise preclude clinical
assessment of CTPA cases.

Non-gated, CT Pulmonary Angiography
(CTPA)

Table 1 Technological Characteristic Comparison


Feature
Subject Device
Predicate Devices

Loading of a series of
appropriate CT from the patient
database

Acquisition Part
ECG-gated / ECG-triggered
ECG-gated / ECG-triggered

Assignment of a probability of a
candidate being a coronary
calcification based on location
within the heart, density, shape and
similar properties: if the probability
of a candidate is higher than a
predefined threshold, the candidate
is labelled as a calcification.
Each calcification is labeled
according to one of four coronary
arteries it most probably belongs to.
In addition, results of the evaluation
can be sent via Rapid Results
Technology to any generic DICOM
viewer.
Comparison to the predicate
device:
The algorithm was re-trained on a
larger database.
A configuration option was added so
the user can disable the automated
calcium scoring evaluation.

Browsing, selecting, and
displaying images for searching
calcium regions/lesions

Interactive definition of ROIs and
assignment of the four major
coronary arteries (LM, LAD,CRC
and RCA) to the lesions

Automatic definition of ROIs and
assignment of a generic calcium
label to the lesions

Calculation and display of the
2D-Agatston score/factor or
other metric on the defined ROIs

Interactive definition of ROIs (for
example noise) to disqualify the
region from participation in the
score

Displaying the score in form of
result tables/reports on paper
and/or film


Digital Imaging and Communications in
Medicine (DICOM) Set; PS 3.1 – 3.20

Medical Device Software –Software Life Cycle
Processes; 62304:2015-06 (Edition 1.1)

Medical devices – Application of risk
management to medical devices; 14971 Third
Edition 2019-12

Medical devices - Part 1: Application of
usability engineering to medical devices
IEC 62366-1:2015 +AMD1:2020

Medical devices - Symbols to be used with
medical device labels, labelling, and
information to be supplied - Part 1: General
requirements 15223-1:2016

covering electrical and mechanical safety listed below, prior to introduction into interstate commerce:


BriefCase is a radiological computer
aided triage and notification software
indicated for use in the analysis of non-
enhanced head CT images. The device
is intended to assist hospital networks
and appropriately trained medical
specialists in workflow triage by
flagging and communication of
suspected positive findings of
pathologies in head CT images, namely
Intracranial Hemorrhage (ICH).
BriefCase uses an artificial intelligence
algorithm to analyze images and
highlight cases with detected ICH on a
standalone desktop application in
parallel to the ongoing standard of care
image interpretation. The user is
presented with notifications for cases
with suspected ICH findings.
Notifications include compressed
preview images that are meant for
informational purposes only and not
intended for diagnostic use beyond
notification. The device does not alter
the original medical image and is not
intended to be used as a diagnostic
device.
The results of BriefCase are intended to
be used in conjunction with other
patient information and based on
professional judgment, to assist with
triage/prioritization of medical images.
Notified clinicians are responsible for
viewing full images per the standard of
care.

BriefCase is a radiological computer
aided triage and notification software
indicated for use in the analysis of non-
enhanced head CT images in adults or
transitional adolescents aged 18 and
older. The device is intended to
assist hospital networks and
appropriately trained medical
specialists in workflow triage
by flagging and communication of
suspected positive findings of
Intracranial Hemorrhage (ICH)
pathologies.
BriefCase uses an artificial intelligence
algorithm to analyze images and
highlight cases with detected findings
on a standalone desktop application in
parallel to the ongoing standard of care
image interpretation. The user is
presented with for cases with
suspected ICH findings. Notifications
include compressed preview images
that are meant for informational
purposes only and not intended for
diagnostic use
beyond notification. The device does
not alter the original medical image
and is not intended to be used as a
diagnostic device.
The results of BriefCase are intended
to be used in conjunction with other
patient information and based on their
professional judgment, to assist with
triage/prioritization of medical images.
Notified clinicians are responsible for
viewing full images per the standard of
care.

Hospital networks and appropriately
trained medical specialists

Non-enhanced CT scan of the head or
neck


No. No cases are removed from
Worklist or deprioritized.

No. No cases are removed from
desktop app or deprioritized

Inclusion/
Exclusion
criteria for
clinical
performance
testing

Inclusion criteria
Non-enhanced head or neck CT
●
protocol with a 64-slice scanner or
higher.
Scans performed on adults/
●
transitional adults ≥ 18 years of age.
Slice thickness 0.625mm – 5.0mm.
●
Exclusion Criteria
All scans that are technically
●
inadequate, including motion
artifacts, severe metal artifacts,
suboptimal bolus timing or an
inadequate field of view.

Inclusion criteria
Non-enhanced head or neck CT
●
protocol with a 64-slice scanner or
higher.
Scans performed on adults/
●
transitional adults ≥ 18 years of age.
Slice thickness 0.625 mm – 5.0 mm.
●
Exclusion Criteria
All scans that are technically
●
inadequate, including motion
artifacts, severe metal artifacts,
suboptimal bolus timing or an
inadequate field of view.

Artificial intelligence algorithm with
database of images.

● AHS module (image acquisition);
● ACS module (image processing));
● Aidoc Worklist application for
workflow integration (worklist and
non-diagnostic Image Viewer).

● AHS module ( image acquisition);
● ACS module (image processing));
● Aidoc Desktop application for
workflow integration (non-
diagnostic Image Viewer).


Time -to-notification
Mean
95% Lower
95% Upper
Median
IQR

Table 2. Time-to- notification comparison for BriefCase devices


892.2080, Radiological
computer aided triage and
notification software

A comparison of the subject and predicate device is provided in the table below.


QFM, Radiological
Computer-Assisted
Prioritization Software
For Lesions

Target Anatomy
Chest/Lung
Chest/Lung
Identical

Image Modality
Frontal Chest X-ray
Frontal Chest X-ray
Identical

The DrAid™ for
Radiology v1 is a
radiological computer-
assisted triage &
notification software
product designed to aid
the clinical assessment of
adult Chest X-Ray cases
with features suggestive
of pneumothorax in
medical care
environment. DrAid™
analyzes cases using an
artificial intelligence
algorithm to features
suggestive of suspected
findings. It makes case-
level output available to a
PACS for worklist
prioritization or triage.
As a passive notification
for prioritization-only
software tool with
standard of care
workflow, DrAid™ does
not send a proactive alert
directly to appropriately
trained medical
specialists. DrAid™ is
not intended to direct
attention to specific
portions or anomalies of
an image. Its results are
not intended to be used
on a stand-alone basis for
clinical decision-making
nor is it intended to rule

The Zebra Pneumothorax
device is a software
workflow tool designed to
aid the clinical assessment
of adult Chest X-Ray cases
with features suggestive of
Pneumothorax in the
medical care environment.
HealthPNX analyzes cases
using an artificial
intelligence algorithm to
identify suspected findings.
It makes case-level output
available to a
PACS/workstation for
worklist prioritization or
triage. HealthPNX is not
intended to direct attention
to specific portions or
anomalies of an image. Its
results are not intended to
be used on a stand-alone
basis for clinical decision-
making nor is it intended to
rule out Pneumothorax or
otherwise preclude clinical
assessment of X-Ray
cases.

Similar; different only
in semantics but not
substance.


out pneumothorax or
otherwise preclude
clinical assessment of X-
Ray cases.

Notification-only,
parallel workflow
tool

DrAid is designed as a
software module that can
be deployed on several
computing and X-ray
imaging platforms such
as radiological imaging
equipment, PACS, On
Premise or On Cloud

HealthPNX is designed as
a software module that can
be deployed on PACS and
Standalone desktop
application, Zebra
Worklist.

Independent of
standard of care
workflow

Aids prompt
identification of
cases with
indicated findings

Performance level
– Timing of
notification

Passive notification is
visible upon transfer to
the PACS with a delay of
about 3.83 minutes for
image transfer to the
cloud, computation, and
results transfer.

Passive notification is
visible upon transfer to the
PACS with a delay of
about 22.1 seconds for
image transfer to the cloud,
computation, and
results transfer.


Positive Pneumothorax:
354 cases
Negative Pneumothorax:
496 cases

Positive Pneumothorax:
146 cases
Negative Pneumothorax:
442 cases


Ethnicity
Representative of the US Population

A summary of the NIH data set characteristics are provided in the table below:


A summary of the Vietnamese data set characteristics are provided in the table below:


The aggregate results for both the NIH and Vietnamese data sets are provided in the table below:


Rapid LVO is a radiological
computer aided triage and
notification software indicated for
use in the analysis of CTA head
images. The device is intended to
assist hospital networks and trained
radiologists in workflow triage by
flagging and communication of
suspected positive Large Vessel
Occlusion (LVO) findings in head
CTA images.
Rapid LVO uses a software
algorithm to analyze images and
highlight cases with suspected
LVO on a server or standalone
desktop application in parallel to
the ongoing standard of care image
interpretation. The user is
presented with notifications for
cases with suspected LVO findings.
Notifications include compressed
preview images, that are meant for
informational purposes only and
not intended for diagnostic use
beyond notification. The device
does not alter the original medical
image and is not intended to be
used as a diagnostic device.
The results of Rapid LVO are
intended to be used in conjunction
with other patient information and
based on professional judgment, to
assist with triage /prioritization of
medical images. Notified clinicians
are responsible for viewing full
images per the standard of care.

Rapid LVO is a radiological
computer aided triage and
notification software indicated for
use in the analysis of CTA head
images. The device is intended to
assist hospital networks and trained
radiologists in workflow triage by
flagging and communication of
suspected positive ICA or MCA-M1
Large Vessel Occlusion (LVO)
findings in head CTA images.
Rapid LVO uses a software
algorithm to analyze images and
highlight cases with suspected LVO
on a server or standalone desktop
application in parallel to the ongoing
standard of care image
interpretation. The user is presented
with notifications for cases with
suspected LVO findings.
Notifications include compressed
preview images. These are meant
for informational purposes only and
are not intended for diagnostic use
beyond notification. The device
does not alter the original medical
image and is not intended to be used
as a diagnostic device.
The results of Rapid LVO are
intended to be used in conjunction
with other patient information and
based on professional judgment, to
assist with triage /prioritization of
medical images. Notified clinicians
are responsible for viewing full
images per the standard of care.

Stroke/Head
Intracranial Stroke/Head
Same

LVO that is the subject of this Special 510(k) submission.


Standard off-the-shelf PC
workstation/server

Acquires medical image data
from DICOM compliant imaging
devices and modalities

Notification Message of Suspected
LVO.
Presentation of a compressed preview
of the study for initial assessment not
meant for diagnostic purposes.
The device operates in parallel with
the standard of care, which remains.

SoC Workflow
In parallel to the SoC
Same


Location
Location
Measure
Measure
Estimate
Lower 95%
Upper 95% CI
Upper 95% CI


Location
Measure
Estimate
Lower 95% CI
Upper 95% CI


Age Groups
Measure
Estimate
Lower 95% CI
Upper 95% CI


Brand
Measure
Estimate
Lower 95% CI
Upper 95% CI


Thickness(mm)
Measure
Estimate
Lower 95% CI
Upper 95% CI


Subject Device
Predicate Device
Reference Device

AI-Rad Companion
Organs RT
(SW Version VA40)

AI-Rad Companion
Organs RT
(SW Version VA20)

AI-Rad Companion
Organs RT is a post-
processing software
intended to
automatically contour
DICOM CT imaging
data using deep-
learning-based
algorithms.
Contours that are
generated by AI-Rad
Companion Organs RT
may be used as input
for clinical workflows
including external
beam radiation therapy
treatment planning.
AI-Rad Companion
Organs RT must be
used in conjunction
with appropriate
software such as
Treatment Planning
Systems and
Interactive Contouring
applications, to review,

AI-Rad Companion
Organs RT is a post-
processing software
intended to
automatically contour
DICOM CT imaging
data using deep-
learning-based
algorithms.
Contours that are
generated by AI-Rad
Companion Organs RT
may be used as input
for clinical workflows
including external
beam radiation therapy
treatment planning. AI-
Rad Companion
Organs RT must be
used in conjunction
with appropriate
software such as
Treatment Planning
Systems and
Interactive Contouring
applications, to review,

Trained medical
professionals use
Contour ProtégéAI as a
tool to assist in the
automated processing
of digital medical
images of modalities
CT and MR, as
supported by
ACR/NEMA DICOM
3.0. In addition,
Contour ProtégéAI
supports the following
indications:
• Creation of
contours using
machine-learning
algorithms for
applications
including, but not
limited to,
quantitative
analysis, aiding
adaptive therapy,
transferring
contour to radiation


edit, and accept
contours generated by
AI-Rad Companion
Organs RT.
The output of AI-Rad
Companion Organs RT
in the format of
RTSTRUCT objects
are intended to be used
by trained medical
professionals.
The software is not
intended to
automatically detect or
contour lesions. Only
DICOM images of
adult patients are
considered to be valid
input.

therapy treatment
planning systems,
and archiving
contours for patient
follow-up and
management.
• Segmenting normal
structures across a
variety of CT
anatomical
locations
• And segmenting
normal structures
of the prostate,
seminal vesicles,
and urethra within
T2-weighted MR
images.
Appropriate image
visualization software
must be used to review
and, if necessary, edit
results automatically
generated by Contour
ProtégéAI.

Algorithm
Deep Learning
Deep Learning
Marchine-learning

Segmentation of
Organ at Risk in
the Anatomic
Regions

Head & Neck, Thorax,
Abdomen & Pelvis
Head & Neck lymph
nodes
(108 OAR)

Head & Neck, Thorax,
Abdomen & Pelvis
(79 OAR)

Head & Neck,
Prostate, Thorax,
Abdomen, Lungs &
Liver, MRT structures
(spleen, pelvic lymph
nodes, descending
aorta, bone)

No Limitation on
scanner model,
DICOM compliance
required.


No Limitation on TPS
model, DICOM
compliance required.

Contraindications
Adult use only
Adult use only
Adult use only

AI-Rad Companion
Organs RT is designed
for use only in adult
populations.
AI-Rad Companion
Organs RT is designed
for any patient for
whom relevant
modality scans are
available. More
specifically, the
software is validated
on previously acquired
CT DICOM volumes
for radiation therapy
treatment planning,
including, head and
neck, thorax, abdomen,
and pelvis.

Clinical
condition the
device is
intended to
diagnose, treat or
manage

Limited to patients
previously selected for
Radiation Therapy.

AI-Rad Companion
(Engine) architecture
enabling the
deployment of AI Rad
Companion Organs RT
using Edge and in the
Cloud. The UI is
provided using a web-
based interface.

AI-Rad Companion
(Engine) architecture
enabling the
deployment of AI Rad
Companion Organs RT
in the Cloud. The UI is
provided using a web-
based interface.

Server-based
application supporting
Linux-based OS and
Local deployment on
Windows or Mac

Creating, editing and
deletion of organ
templates. Customize


predefined structure
database with mapping
to international
nomenclature schemes.

AI-Rad Companion
Organs RT
automatically
processes input image
data and sends the
results as DICOM-RT
Structure Sets to a
user-configurable
target node.

Automatic contouring
working using
machine-learning

Contour
visualization and
editing feature

AI-Rad Companion
Organs RT provides
basic result preview of
automatic
segmentation results,
and no editing feature
of the automatic
segmented contour.

The target performance
was validated using
113 cases distributed
to two cohorts. Cohort
A is clinical routine
treatment planning CT
and it is split into two
sub-cohort and Cohort
B is PET-CT data. To
objectively evaluate
the target performance,
the DICE coefficient,
the absolute symmetric
surface distance
(ASSD) and the fail
rate was evaluated.
The segmentation
performance of the
subject and reference
device were equivalent
as well as the overall
performance compared
to the predicate device.

The target performance
was validated using
113 cases distributed to
two cohorts. Cohort
A-Clinical Routine
Treatment Planning
CT (Siemens; Head
and Neck, Thorax and
Abdomen Pelvis) and
Cohort B-Multi
Vendor Coverage (GE
and Phillips; Head and
Neck).
To objectively evaluate
the target performance,
the DICE coefficient,
the absolute symmetric
surface distance
(ASSD) and the fail
rate was evaluated.
The segmentation
performance of the
subject and reference

739 CT Images from
12 clinical sites were
used for testing. The
mean and standard
deviation Dice
coefficients, along with
the lower 95th
percentile confidence
bound were calculated.


device were equivalent
as well as the overall
performance compared
to the predicate device.

User Interface –
Results Preview
(Confirmation)

Basic visualization
functionality of
original data and
generated contours

Results send to
Confirmation UI &
Optional bypassing of
Confirmation UI to
TPS

Design to be used by
trained clinicians.

Designed to be used by
trained clinicians


Medical Devices – Application
of usability engineering to
medical devices [including
Corrigendum 1 (2016)]

Medical Devices – application
of risk management to
medical devices


Medical device software –
software life cycle processes
[Including Amendment 1
(2016)]

Digital Imaging and
Communications in Medicine
(DICOM) Set

Information Technology –
Digital Compression and
coding of continuous -tone
still images: Requirements
and Guidelines [including:
Technical Corrigendum
1(2005)]

Medical devices – symbols to
be used with information to
be supplied by the
manufacturer – Part 1:
General Requirements

Health software – Part 1:
General requirements for
product safety


Validation Testing Subject
Acceptance Criteria

• All the organs segmented in the predicate
device are also segmented in the subject
device
• The lower bound of 95th percentile CI of
the segmentation is greater than 0.1 Dice
lower than the mean of the predicate
device segmentation

• The overall fail rate of each
organ/anatomical structures is smaller
than 15%
• The lower bound of 95th percentile CI of
the segmentation is greater than 0.1 Dice
lower than the mean of the reference
device segmentation


Median
95% CI (Bootstrap)
Median
95% CI (Bootstrap)

Table 3: Acceptance Criteria of AIRC Organs RT VA40


AI-Rad Companion Organs RT VA40
(Head and Neck lymph node class)

Contour ProtégéAI from MIM
Software Inc
(Pelvic lymph node class)

Table 4: Performance comparison between subject device and predicate device


>40: 7
Unknown: 66
*unknown due to data
minimization on customer site

Head & Neck: 24
Thorax: 19
Abdomen Pelvis: 30

Table 5: Performance comparison between subject device and reference device


Stanford (US): 15
NNord (DE): 4
UKH (DE): 25
HCG (IND): 116

<30 : 1
30 – 50: 3
50 – 70: 2
>= 70: 3
Unknown: 152*
*unknown due to data minimization on customer
site


Predicate Device
Aidoc BriefCase for LVO triage (K203508)

Subject Device
Aidoc BriefCase for M1-LVO triage (K221314)

BriefCase is a radiological computer aided
triage and notification software indicated
for use in the analysis of head CTA images.
The device is intended to assist hospital
networks and appropriately trained
medical specialists in workflow triage
by flagging and communication of
suspected positive findings of Large Vessel
Occlusion (LVO) pathologies.
BriefCase uses an artificial intelligence
algorithm to analyze images and highlight
cases with detected findings on a
standalone desktop application in parallel
to the ongoing standard of care image
interpretation. The user is presented
with notifications for cases with suspected
findings. Notifications include compressed
preview images that are
meant for informational purposes only
and not intended for diagnostic use
beyond notification. The device does not
alter the original medical image and is not
intended to be used as a diagnostic device.
The results of BriefCase are intended to be
used in conjunction with other
patient information and based on their
professional judgment, to assist with
triage/prioritization of medical images.
Notified clinicians are responsible for
viewing full images per the standard of
care.

BriefCase is a radiological computer aided
triage and notification software indicated for
use in the analysis of head CTA images in
adults or transitional adolescents aged 18 and
older. The device is intended to assist hospital
networks and appropriately trained medical
specialists in workflow triage by flagging and
communication of M1 Large Vessel Occlusion
(M1-LVO) pathologies.
BriefCase uses an artificial intelligence
algorithm to analyze images and highlight
cases with detected findings on a standalone
desktop application in parallel to the ongoing
standard of care image interpretation. The
user is presented with notifications for cases
with suspected findings. Notifications include
compressed preview images that are meant
for informational purposes only and not
intended for diagnostic use beyond
notification. The device does not alter the
original medical image and is not intended to
be used as a diagnostic device.
The results of BriefCase are intended to be
used in conjunction with other patient
information and based on their
professional judgment, to assist with
triage/prioritization of medical images.
Notified clinicians are responsible for
viewing full images per the standard of care.

Hospital networks and appropriately
trained medical specialists


Notification-only
(/notification
alerts), parallel
workflow tool

No. No cases are removed from
Worklist or deprioritized.

No. No cases are removed from
desktop app or deprioritized

Inclusion/
Exclusion criteria
for clinical
performance
testing

Inclusion criteria
Head CTA protocol with a 64-slice
●
scanner or higher;
Scans performed on
●
adults/transitional adults ≥ 18 years of
age;
Slice thickness 0.5 mm – 1.0 mm.
●
Exclusion Criteria
All studies that are technically
●
inadequate, including studies with
motion artifacts, severe metal
artifacts, sub or an inadequate field of
view.

Inclusion criteria
Head CTA protocol with a 64-slice scanner
●
or higher;
Scans performed on adults/transitional
●
adults ≥ 18 years of age;
Slice thickness 0.5 mm – 1.0 mm.
●
Exclusion Criteria
All studies that are technically
●
inadequate, including studies with motion
artifacts, severe metal artifacts, sub or an
inadequate field of view.

Artificial intelligence algorithm with
database of images.

- AHS module (image acquisition);
- ACS module (image processing));
- Aidoc Worklist application for
workflow integration (worklist and
non-diagnostic Image Viewer).

- AHS module ( image acquisition);
- ACS module (image processing));
- Aidoc Desktop application for workflow
integration (non-diagnostic Image
Viewer).


Table 2. Time-to-Notification Comparison for BriefCase Devices


Table 4. Frequency Distribution of Gender


Table 5. Frequency Distribution of Manufacturer


Predicate Device
Aidoc Briefcase for iPE Triage
(K203508)

Subject Device
Aidoc Briefcase for Vertically
Malpositioned ETT in Relation to the
Carina Triage (K221330)

BriefCase is a radiological computer
aided triage and notification software
indicated for use in the analysis of
contrast enhanced chest CTs (but not
dedicated CTPA protocol). The device
is intended to assist hospital networks
and trained medical specialists in
workflow triage by flagging and
communication of suspect positive
cases of incidental Pulmonary
Embolism (iPE) pathologies.
BriefCase uses an artificial intelligence
algorithm to analyze images and flag
suspect cases on a standalone desktop
application in parallel to the ongoing
standard of care image interpretation.
The user is presented with notifications
for suspect cases. Notifications include
compressed preview images that are
meant for informational purposes only
and not intended for diagnostic use
beyond notification. The device does
not alter the original medical image and
is not intended to be used as a
diagnostic device.
The results of BriefCase are intended to
be used in conjunction with other
patient information and based on their
professional judgment, to assist with
triage/prioritization of medical images.
Notified clinicians are responsible for

BriefCase is a radiological computer-
aided triage and notification software
indicated for use in the analysis of
frontal chest X-ray (CXR) images in
adults or transitional adolescents aged
18 and older. The device is intended to
assist hospital networks and
appropriately trained medical
specialists in workflow triage by
flagging and communicating suspected
positive cases of vertically
malpositioned endotracheal tube (ETT)
in relation to the carina.
Findings are flagged when the ETT
distal tip is assessed as being more
than 5 cm above the carina, less than 2
cm above the carina, or when it is below
the carina (i.e in the right or left
mainstem bronchus).
The device assesses solely the vertical
position of the ETT distal tip relative to
the carina, does not factor patient
positioning, and cannot detect
esophageal intubation. The device
does not provide results when the
carina is not well-visualized on the x-ray
image. The device does not
discriminate types of ETTs, as such a
properly positioned double lumen ETT
may trigger a false prioritization alert.
BriefCase uses an artificial intelligence
algorithm to analyze images and


highlight cases with detected findings
on a standalone application in parallel
to the ongoing standard of care image
interpretation. The user is presented
with notifications for cases with
suspected findings. Notifications
include compressed preview images
that are meant for informational
purposes only and not intended for
diagnostic use beyond notification. The
device does not alter the original
medical image and is not intended to be
used as a diagnostic device.
The results of BriefCase are intended to
be used in conjunction with other
patient information and based on the
user’s professional judgment, to assist
with triage/prioritization of medical
images. Notified clinicians are
responsible for viewing full images per
the standard of care.

Hospital networks and appropriately
trained medical specialists

Contrast-enhanced chest CTs (but not
dedicated CTPA protocol)

Notification-
only
(/notification
alerts), parallel
workflow tool


No. No cases are removed from
Worklist or deprioritized.

No. No cases are removed from
desktop app or deprioritized

Inclusion criteria
● Contrast-enhanced chest CTs (but
not dedicated CTPA protocol.
● Single energy exams.
● Scans performed with a 64 slice or
greater number of detectors.
● Scans performed on adults/
transitional adults ≥ 18 years of age.
● Slice thickness: 0.5mm – 2.0mm
axial.
Exclusion Criteria
● All studies that are technically
inadequate, including studies with
motion artifacts, severe metal
artifacts, or inadequate field of view.

Inclusion criteria
● Frontal Chest X-ray protocol.
● Images performed on adults/
transitional adults ≥ 18 years of age.
Exclusion Criteria
● All x-ray images that have
inadequate field of view.The device
does not provide results when the
carina is not well-visualized on the
x-ray image.

Artificial intelligence algorithm with
database of images.

● AHS module (image acquisition);
● ACS module (image processing);
● Aidoc Worklist application for
workflow integration (worklist and
non-diagnostic Image Viewer).

● AHS module (orchestrator, image
acquisition);
● ACS module (image processing);
● Aidoc Desktop application for
workflow integration (feed and non-
diagnostic Image Viewer).


Table 4. Frequency Distribution of Gender *


Radiological Computer Assisted Detection and Diagnosis
Software

Classification regulation
21 CFR 892.2090


Legal Manufacturer
ScreenPoint Medical B.V.


d) An exam score which categorizes exams on a scale of 1-10 with increasing

likelihood of cancer. The score is calibrated in such a way that approximately 10

percent of mammograms in a population of mammograms without cancer falls in

be utilized to enhance user interfaces and workflow.


Standard ID
Standard Title
FDA Recognition #

Medical devices - Part 1: Application of usability
engineering to medical devices [Including
CORRIGENDUM 1 (2016)]

Clinical investigation of medical devices for
human subjects - Good clinical practice

Medical Devices - Application Of Risk
Management To Medical Devices

Medical Device Software - Software Life Cycle
Processes

Health software - Part 1: General requirements
for product safety

voluntary FDA recognized standards and guidelines:


Table 1: Data used for evaluation of stand-alone performance.


Specification
Subject Swoop® System
Predicate Swoop® System (K212456)

The Swoop® Portable MR Imaging
System is a bedside magnetic
resonance imaging device for
producing images that display the
internal structure of the head where
full diagnostic examination is not
clinically practical. When interpreted
by a trained physician, these images
provide information that can be
useful in determining a diagnosis.

Adult and pediatric patients (≥ 0
years)

At the point of care in medical
facilities, including emergency rooms,
critical care units, hospital or
rehabilitation rooms.

Energy Used and/or delivered:
Magnetic Resonance
Same

Physical Dimensions
835 mm x 630 mm x 652 mm
Same

Field Strength
63.3 mT permanent magnet
Same

Computer Display
Hyperfine-supplied tablet
Same

The table below compares the subject device to the predicate.


Inner Dimensions (mm)
205 mm x 240 mm
Same

T1W
- T1-Standard
- T1-Gray/White Contrast

Advanced Gridding
(T1-Gray/White Contrast only)

- Advanced Denoising (applies to
T1W, T2W, and FLAIR only)
- Image orientation transform
- Geometric distortion correction
- Receive coil intensity correction
- DICOM output


Test
Test Description
Applicable Standard(s)

Testing to verify that the advanced reconstruction
models do not alter image features or introduce
artifacts.

● IEC 62304:2006
● FDA Guidance, “Guidance for the
Content of Premarket Submissions
for Software Contained in Medical
Devices”

Testing to verify that image quality with advanced
reconstruction is acceptable.

Testing to verify basic software functionality is
unchanged between releases.

NESSUS scan test to verify any vulnerabilities and
serve as a security baseline.

accordance with internal requirements and applicable standards to support substantial equivalence.


Testing to verify image performance with advanced
reconstruction meets all image quality criteria.

● NEMA MS 1-2008 (R2020)
● NEMA MS 3-2008 (R2020)
● NEMA MS 9-2008 (R2020)
● NEMA MS 12-2016
● American College of Radiology (ACR)
Phantom Test Guidance for Use of
the Large MRI Phantom for the ACR
MRI Accreditation Program
● American College of Radiology
standards for named sequences

Validation studies to ensure the device meets user
needs and performs as intended.

● FDA Guidance, “Guidance for the
Content of Premarket Submissions
for Software Contained in Medical
Devices”

Testing to verify cybersecurity controls and
management.

● Cybersecurity as recommended in
FDA guidance, “Content of
Premarket Submissions for
Management of Cybersecurity in
Medical Devices”


Biocompatibility testing of patient-contacting
materials.

Cleaning and disinfection validation of patient-
contacting materials.

● FDA Guidance, “Reprocessing
Medical Devices in Health Care
Settings: Validation Methods and
Labeling”
● ISO 17664:2017
● ASTM F3208-17

Electrical Safety, EMC, and Essential Performance
testing.

Characterization of the Specific Absorption Rate for
Magnetic Resonance Imaging Systems.

modifications did not introduce a new worst-case configuration or scenario for testing.


Automated Radiological Image Processing Software

The predicate device to which substantial equivalence is claimed:


Subject Device
Hologic, Inc.
Genius AI Detection 2.0

Predicate Device
Hologic, Inc.
Genius AI Detection

21 CFR 892.2090 /
Radiological Computer Assisted Detection
and Diagnosis Software

A radiological computer assisted
detection and diagnostic software is an
image processing device intended to aid
in the detection, localization, and
characterization of fracture, lesions, or
other disease specific findings on
acquired medical images (e.g.
radiography, MR, CT). The device
detects, identifies and characterizes
findings based on features or
information extracted from images, and
provides information about the
presence, location, and characteristics
of the findings to the user. The analysis
is intended to inform the primary
diagnostic and patient management
decisions that are made by the clinical
user. The device is not intended as a
replacement for a complete clinician's
review or their clinical judgment that
takes into account other relevant
information from the image or patient
history.


Genius AI Detection is a computer-aided
detection and diagnosis (CADe/CADx)
software device intended to be used
with compatible digital breast
tomosynthesis (DBT) systems to identify
and mark regions of interest including
soft tissue densities (masses,
architectural distortions and
asymmetries) and calcifications in DBT
exams from compatible DBT systems
and provide confidence scores that
offer assessment for Certainty of
Findings and a Case Score.
The device intends to aid in the
interpretation of digital breast
tomosynthesis exams in a concurrent
fashion, where the interpreting
physician confirms or dismisses the
findings during the reading of the
exam.

Hologic Selenia Dimensions
Hologic 3Dimensions
Supports both models in the following

mRaoddioelso:g i cal computer assisted
•detescttaionnd aarnddr edsiaogluntoiosntic1 s-mofmtwsalricee. s
• high resolution1-mmslices(Clarity

ImagHeD p)rocessing device utilizing
machine learning to aid in the
highresolution6-mmSmartSlices
detection, localization, and
(3DQuorum)
characterization of soft tissue densities
(masses, architectural distortions and
asymmetries) and calcifications in the
1-mm 3D DBT slices. Findings are co-
registered to 6-mm SmartSlices.

To inform the primary diagnostic and
patient management decisions that
are made by the clinical user.

Symptomatic and asymptomatic
women undergoing
mammography

MQSA-Qualified Interpreting Physicians
and Radiologists


Places mark within suspicious lesion by
default (Emphasize™; RightOn™) and
reports confidence of finding next to each
identified lesion in the image. CAD display
may be toggled on/off. Option to
automatically zoom into or contour the
suspicious region of interest (PeerView™).


Office of Product Evaluation and Quality


Rapid ICH is a radiological
computer aided triage and
notification software indicated for
use in the analysis of non-enhanced
head CT images. The device is
intended to assist hospital networks
and trained radiologists in
workflow triage by flagging and
communication of suspected
positive findings of pathologies in
head CT images, namely
Intracranial Hemorrhage (ICH).
Rapid ICH uses an artificial
intelligence algorithm to analyze
images and highlight cases with
detected ICH on a standalone

Rapid ICH is a radiological
computer aided triage and
notification software indicated for
use in the analysis of non-enhanced
head CT images. The device is
intended to assist hospital networks
and trained radiologists in
workflow triage by flagging and
communication of suspected
positive findings of pathologies in
head CT images, for IPH, IVH,
SAH, and SDH Intracranial
Hemorrhages (ICH).
Rapid ICH uses an artificial
intelligence algorithm to analyze

and predicate devices is provided below.


desktop application in parallel to
the ongoing standard of care image
interpretation. The user is
presented with notifications for
cases with suspected ICH findings.
Notifications include compressed
preview images that are meant for
informational purposes only and
not intended for diagnostic use
beyond notification. The device
does not alter the original medical
image and is not intended to be
used as a diagnostic device. The
results of Rapid ICH are intended
to be used in conjunction with other
patient information and based on
professional judgment, to assist
with triage/prioritization of medical
images. Notified clinicians are
responsible for viewing full images
per the standard of care.

images and highlight cases with
detected ICH on a server or
standalone desktop application in
parallel to the ongoing standard of
care image interpretation. The user
is presented with notifications for
cases with suspected ICH findings.
Notifications include compressed
preview images, which are meant
for informational purposes only and
not intended for diagnostic use
beyond notification. The device
does not alter the original medical
image and is not intended to be
used as a diagnostic device.
The results of Rapid ICH are
intended to be used in conjunction
with other patient information and
based on professional judgment, to
assist with triage /prioritization of
medical images. Notified
radiologists are responsible for
viewing full images per the
standard of care.

No, the device does not highlight or
direct a user’s attention to a specific
location in the image file.

Presentation of a preview of the
study for initial assessment not
meant for diagnostic purposes.


The device operates in parallel with
the standard of care, which remains.


Time to open in standard of
care (Minutes)

Time to notification of Rapid
ICH (Minute)

notification of the Rapid ICH software (0.65 minutes: 95% CI 0.63 – 0.67).


rmance
across su
btype differentiation
shows:

Performance across subtype differentiation shows:


Performance within Make/Model of Scanners


Performance within Reconstruction Methods of Make/Model of Scanners


Elucid Bioimaging Inc.
vascuCAP A.1.2
K183012


Plaque Burden,
tested range 0.4-1.0
(ratio)

Calcified Area
(mm2):
Tested range: 0-14


510(k) Sponsor
Ever Fortune.AI Co., Ltd.

Rm. D, 8F. No. 573, Sec. 2 Taiwan Blvd.
West Dist.
Taichung City 403020
TAIWAN


EFAI ChestSuite XR Pneumothorax Assessment System v1.0

Radiological Computer-Assisted Prioritization Software For
Lesions

Radiological Computer Aided Triage and Notification Software


Company
Ever Fortune.AI Co., Ltd.
Behold.AI Technologies Limited

EFAI PNXXR is a software
workflow tool designed to aid
the clinical assessment of adult
(22 years of age or older)
Posteroanterior (PA) view Chest
X-Ray cases with features
suggestive of pneumothorax in
the medical care environment.
EFAI PNXXR analyzes cases
using an artificial intelligence
algorithm to identify suspected
findings. It makes case-level
output available to a
PACS/workstation for worklist
prioritization or triage. EFAI
PNXXR is not intended to direct
attention to specific portions or
anomalies of an image. Its
results are not intended to be
used on a stand-alone basis for
clinical decision-making nor is
it intended to rule out
pneumothorax or otherwise
preclude clinical assessment of
X-Ray cases.

The red dot™ software platform
is a software workflow tool
designed to aid the clinical
assessment of adult Chest X-Ray
cases with features suggestive of
Pneumothorax in the medical
care environment. red dot™
analyzes cases using an artificial
intelligence algorithm to identify
suspected findings. It makes
case-level output available to a
PACS/workstation for worklist
prioritization or triage. red dot™
is not intended to direct attention
to specific portions of an image
or to anomalies other than
Pneumothorax. Its results are not
intended to be used on a stand-
alone basis for clinical decision-
making nor is it intended to rule
out Pneumothorax or otherwise
preclude clinical assessment of
X-Ray cases.

Hospital networks and trained
clinicians

Table - Comparison with the Predicate Device.


Supported Modalities
X-Ray (PA view)
X-Ray (PA or AP view)

Clinical condition
Pneumothorax
Pneumothorax


This device provides a systematic, automated
analysis of NCCT scans of the head to provide a
standardized, automated ASPECT score for
Stroke workup.
The clinical reader study, which included 4
neurologists and 6 neuroradiologists,


patient benefit. The study demonstrates
improvement in the assessment for readers
interpreting NCCT scans for overall ASPECTS
assessment.
This device additionally also provides
transparent AI processing enabling users to
understand how the automated output has
been determined and enables users to perform
a more technically informed review of the
automated output. This is a significant benefit
which may help mitigate risks related to false
negatives or false positives in the automated
output, particularly for more challenging cases.

There are minimal potential risks associated
with the use of the device.

Incorrect scoring which may result in false
positive results and result to incorrect patient
management with possible adverse effects such
as, unnecessary additional medical imaging
and/or unnecessary additional diagnostic
workup.

Incorrect scoring which may result in false
negative results may lead to complications,
including incorrect diagnosis and delay in
disease management.

The device could be misused to analyze images
from an unintended patient population or on
images acquired with incompatible imaging
hardware or incompatible image acquisition
parameters, leading to inappropriate diagnostic
information being displayed to the user.

Device failure could lead to the absence of
results, delay of results or incorrect results,
which could likewise lead to inaccurate patient
assessment.


However, based on the performance data and
the application of mitigating measures
(including but not limited to general controls
and special controls established for this device
type), use of the device is unlikely to decrease
diagnostic performance of the user and
possible misuse of the device does not present
additional risks compared with misuse of
other types of radiological image processing
devices.
The device includes a gating condition in
software to help ensure that the software is not
used on stroke mimics or the incorrect patient
population.
The physician may either confirm or overrule
the device
Heatmap provides additional transparency on
regions evaluated by the ASPECTS, which may
be used by the clinician to qualify or correct
device outputs
The study was enriched to cover the range of
ASPECT scores; and, the readers in practice may
not experience a significant improvement in
determining ASPECTS.
This device may improve the diagnostic
accuracy of less experienced or less confident
physicians in rating NCCT for the ASPECT score
in the context of acute ischemic stroke caused
by ICA or MCA occlusion (confirmed using
vascular imaging such as CTA or MRA).

This device shows positive improvement in
diagnostic accuracy in comparison to the
reference standard.
Although some performance data may suggest
that sensitivity for some ASPECTS regions may
be diminished or inconsistent in comparison to
other region however, sample size limitations


Characteristic
Subject Device
Predicate Device
Reference Device
Reference Device

Device Name
Device Name
AVIEW Lung
syngo. CT Lung
InferRead
AVIEW

Medical Image
Management and
Processing System

Product Code
OEB, LLZ
OEB
OEB, LLZ
LLZ, JAK

Review Panel
Radiology
Radiology
Radiology
Radiology

AVIEW Lung Nodule CAD is a Computer-Aided Detection (CAD) software designed to assist
radiologists in the detection of pulmonary nodules (with diameter 3-20 mm) during the review of
CT examinations of the chest for asymptomatic populations. AVIEW Lung Nodule CAD provides
adjunctive information to alert the radiologists to regions of interest with suspected lung nodules
that may otherwise be overlooked. AVIEW Lung Nodule CAD may be used as a second reader
after the radiologist has completed their initial read. The algorithm has been validated using non-
contrast CT images, the majority of which were acquired on Siemens SOMATOM CT series
scanners; therefore, limiting device use to use with Siemens SOMATOM CT series is
recommended.

The syngo. CT Lung CAD device is a Computer-Aided Detection (CAD) tool designed to assist
radiologists in the detection of solid and subsolid (part-solid and ground glass) pulmonary nodules
during review of multi-detector computed tomography (MDCT) from multivendor examinations
of the chest. The software is an adjunctive tool to alert the radiologist to regions of interest (ROI)
that may otherwise be overlooked.
The syngo. CT Lung CAD device may be used as a concurrent first reader followed by a full
review of the case by the radiologist or as second reader after the radiologist has completed his/her
initial read.
The software device is an algorithm which does not have its own user interface component for
displaying of CAD marks.
The Hosting Application incorporating syngo. CT Lung CAD is responsible for implementing a
user interface.

lnferRead Lung CT.AI is comprised of computer-assisted reading tools designed to aid the
radiologist in the detection of pulmonary nodules during the review of CT examinations of the
chest on an asymptomatic population. Infer Read Lung CT.AI requires that both lungs be in the
field of view. lnferRead Lung CT.AI provides adjunctive information and is not intended to be
used without the original CT series.

AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This
software could be used to support the physician quantitatively in the diagnosis, follow up
evaluation and documentation of CT lung tissue images by providing image segmentation of sub-
structures in lung, lobe, airways and cardiac, registration of inspiration and expiration which could
analyze quantitative information such as air trapping volume, air trapped index, and
inspiration/expiration ratio. And, volumetric and structure analysis, density evaluation and
reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on premise
and as cloud environment as well to allow users to connect by various environment such as mobile
devices and chrome browser. Characterizing nodules in the lung in a single study, or over the time
course of several thoracic studies. Characterizations include nodule type, location of the nodule


and measurements such as size (major axis, minor axis), estimated effective diameter from the
volume of the nodule, volume of the nodule, Mean HU(the average value of the CT pixel inside
the nodule in HU), Minimum HU, Max HU, mass(mass calculated from the CT pixel value), and
volumetric measures(Solid major; length of the longest diameter measured in 3D for solid portion
of the nodule, Solid 2nd Major: The length of the longest diameter of the solid part, measured in
sections perpendicular to the Major axis of the solid portion of the nodule), VDT (Volume doubling
time), and Lung-RADS (classification proposed to aid with findings). The system automatically
performs the measurement, allowing lung nodules and measurements to be displayed and, integrate
with FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides CAC
analysis by segmentation of four main artery (right coronary artery, left main coronary, left anterior
descending and left circumflex artery then extracts calcium on coronary artery to provide Agatston
score, volume score and mass score by whole and each segmented artery type. Based on the score,
provides CAC risk based on age and gender.

The AVIEW Lung Nodule CAD is a software product that detects nodules in the lung. The lung
nodule detection model was trained by Deep Convolution Neural Network (CNN) based algorithm
from the chest CT image. Automatic detection of lung nodules of 3 to 20mm in chest CT images.
By complying with DICOM standards, this product can be linked with the Picture Archiving and
Communication System (PACS) and provides a separate user interface to provide functions such
as analyzing, identifying, storing, and transmitting quantified values related to lung nodules. The
CAD’s results could be displayed after the user’s first read, and the user could select or de-select
the mark provided by the CAD. The device’s performance was validated with SIEMENS’
SOMATOM series manufacturing. The device is intended to be used with a cleared AVIEW
platform.

Simens Healthcare GmbH intends to market the syngo. CT Lung CAD which is a medical device
that is designed to perform CAD processing in thoracic CT examinations for the detection of solid
pulmonary nodules (between 3.0 mm and 30.0mm) and subsolid (part-solid and ground glass)
nodules (between 5.0mm and 30.0mm) in average diameter. The device processes image acquired
with multi-detector CT scanners with 16 or more detector rows.
The syngo. CT Lung CAD device supports the full range of nodule locations (central, pe-ripheral)
and contours (round, irregular).
The syngo. CT Lung CAD sends a list of nodule candidate locations to a visualization application,
such as syngo MM Oncology, or a visualization rendering component, which
generates output images series with the CAD marks superimposed on the input thoracic CT images
to enable the radiologist’s review. syngo MM Oncology (FDA clearance k191309) is deployed on
the syngo.via platform (FDA clearance k191040), which provides a common framework for
various other applications implementing specific clinical workflows (but are not part of this
clearance) to display the CAD marks. The syngo. CT Lung CAD device may be used either as a
concurrent first reader, followed by a review of the case, or as a second reader only after the initial
read is completed
The subject device and predicate device have the same basic technical characteristics. This does
not introduce new types of safety or effectiveness concerns as demonstrated by the statistical
analyses and results of the reader study and additional evaluations results documented in the
Statistical Analysis.

InferRead Lung CT.AI uses the Browser/Server architecture and is provided as Software as a

Service (SaaS) via a URL. The system integrates algorithm logic and database in the same server

to ensure the simplicity of the system and the convenience of system maintenance. The server is

able to accept chest CT images from a PACS system, Radiological Information System (RIS

system) or directly from a CT scanner, analyze the images and provide output annotations

regarding lung nodules. Users are then able to use an existing PACS system to view the annotations


on their workstations. Dedicated servers can be located at hospitals and are directly.

connected to the hospital networks. The software consists of 4 modules which are Image reception

(Docking Toolbox), Image predictive processing (DLServer), Image storage (RePACS) and Image

The AVIEW is a software product which can be installed on a PC. It shows images taken with the
interface from various storage devices using DICOM 3.0 which is the digital image and
communication standard in medicine. It also offers functions such as reading, manipulation,
analyzing, post-processing, saving, and sending images by using the software tools. And is
intended for use as diagnostic patient imaging which is intended for the review and analysis of CT
scanning. Provides following features as semi-automatic nodule management, maximal plane
measure, 3D measures and columetric measures, automatic nodule detection by integration with
3rd party CAD. Also provides Brocks model which calculated the malignancy score based on
numerical or Boolean inputs. Follow up support with automated nodule matching and
automatically categorize Lung-RADS score which is a quality assurance tool designed to
standardize lung cancer screening CT reporting and management recommendations that is based
on type, size, size change and other findings that is reported. It also automatically analyzes
coronary artery calcification which support user to detect cardiovascular disease in early stage and
reduce the burden of medical.

pulmonary nodules in
non-contrast chest CT
acquisitions

Solid and subsolid
(part-solid and
ground-glass)
pulmonary nodules in
screening and
diagnostic chest CT
acquisitions.

solid pulmonary
nodules in diagnostic
chest CT acquistions

Diameter:
 Pulmanoary
nodules ≥ 3 mm
and <20 mm
Locations:
 Full range: central,
peripheral
Contours:
 round, irregular

Diameter:
 solid ≥ 3mm and
≤30mm
 Subsolid (part-
solid and ground
glass) ≥ 5mm and ≤
30mm
Locations:
 Full range: central,
peripheral
Contours:
round, irregular

Solid nodules ≥
3mm and 10mm and
full rande, central,
peripheral round.
irregular


DICOM GSPS
(Grayscale Softcopy
Presentation State)
XML (Coordinate of
detected nodules)
Able to view results
on AVIEW, AVIEW
LCS viewer page

Generates output
images series with the
CAD makrs
superimposed.
Able to view results
syngo MM Oncology
viewer page.

Scanners
Multi-vendor and
multi-detector CT
(MDCT) scanners
(Siemens, GE, Philips,
and Toshiba)

Slice Thickness
Up to and including
2.5mm, it is
recommended that
<=1.25mm be used
for the detection of
smaller nodules (e.g.,
4.0mm)

Slice Thickness
Up to and including
2.5mm, it is
recommended that
<=1.25mm be used for
the detection of
smaller nodules (e.g.,
3.0mm)

Slice Overlap
0~50%
Note: Reconstruction
overlap is allowed,
but gaps are not
permitted


Kernel
Consistent with
thoracic CT protocols
and in line with patient
safety guidelines.
Kernels were grouped
as to their profile.
Typical kernels
validated by the reader
study were:
Smooth: B, B30f,
Standard, FC10
Medium: C, B45f,
Br49d, B50f, I50f,
Br60f, Lung, FC50,
FC51
Sharp: D, B70f, I70f,
B80s, I80s, Bone

Kernel
Consistent with
thoracic CT protocols
and in line with patient
safety guidelines.
Kernels were grouped
as to their profile.
Typical kernels
validated by the reader
study were:
Smooth: B, B30f,
Standard, FC10.
Medium: C B45f,
B50f, Lung, FC50,
FC51, Bv49d_2,
I50f_2, B60f.
Sharp: D, B70f, Bone,
FC52

Dose
Consistent with
thoracic CT protocols
and in line with patient
safety guidelines.
Typical values are:
CTDIvol < 8.0 mGy
(milligray) in
diagnostic protocols
and
CTDIvol of = 3.0
mGy in screening
protocols.
These values are
defined for standard
sized patient—5 ft 7
in., 154 lb (170
cm, 70 kg)—based on
a 32-cm reference
phantom with
appropriate reductions
in
CTDIvol for smaller
patients and
appropriate
increases in CTDIvol
for larger patients.


Unaided
Aided
Difference in point estimate

1. Overall unaided/aider reader performacne comparison


AAMI ANSI ES60601-1:2005/(R)2012 and A1:2012,
C1:2009/(R)2012 and A2:2010/(R)2012 (Consolidated Text)
Medical electrical equipment - Part 1: General requirements for
basic safety and essential performance (IEC 60601-1:2005, MOD)

IEC60601-1-2: 2014(4th Edition) , Medical electrical equipment -
Part 1-2: General requirements for basic safety and essential
performance - EMC

IEC 60601-2-37 Edition 2.0 2007, Medical electrical equipment –
Part 2-37: Particular requirements for the basic safety and essential
performance of ultrasonic medical diagnostic and monitoring
equipment

ISO 10993-1:2009/(R)2013, Biological evaluation of medical
devices – Part 1: Evaluation and testing within a risk management
process

ISO 14971:2007, Medical devices - Application of risk management
to medical devices

NEMA UD 2-2004 (R2009)
Acoustic Output Measurement Standard for Diagnostic Ultrasound
Equipment Revision 3


Validation Type
Definition
Acceptance Criteria

Number of correctly detected frames
×100
Total number of frames with nerve

1000
Average latency time of each frame (msec)


Validation Type
Average
Standard Deviation
95% CI

Summary Performance data, Standard Deviations & Confidence Intervals:


Planning Software
Quantitative Insights, Inc. QuantX

The Avenda Health AI Prostate Cancer
Planning Software is an artificial
intelligence (AI)-based decision support
software, indicated as an adjunct to the
review of magnetic resonance (MR)
prostate images and biopsy findings in the
prostate oncological workflow. The
Avenda Health AI Prostate Cancer
Planning Software is designed to support
the prostate oncological workflow by
helping the user with the segmentation of
MR image features, including the prostate;
in the evaluation, quantification, and
documentation of lesions; and in pre-
planning for diagnostic and interventional
procedures such as biopsy and/or soft
tissue ablation. The device is intended to
be used by physicians trained in the
oncological workflow in a clinical setting
for planning and guidance for clinical,
interventional, diagnostic, and/or treatment
procedures of the prostate.
The Avenda Health AI Prostate Cancer
Planning Software’s lesion
characterization functions are intended for
use on patients with a pathology-confirmed
Gleason Grade Group (GGG)≥ 2 lesion
and for whom corresponding biopsy
coordinate information have been
uploaded. These functions are indicated
for the evaluation of the extent of known
disease. Extent of known disease refers to
the boundary of a pathology confirmed
lesion of GGG≥ 2 for a particular patient.
Specifically, using prostate MR images,

QuantX is a computer-aided diagnosis
(CADx) software device used to assist
radiologists in the assessment and
characterization of breast abnormalities
using MR image data. The software
automatically registers images, and
segments and analyzes user-selected
regions of interest (ROI). QuantX extracts
image data from the ROI to provide
volumetric analysis and computer
analytics based on morphological and
enhancement characteristics. These
imaging (or radiomic) features are then
synthesized by an artificial intelligence
algorithm into a single value, the QI score,
which is analyzed relative to a database of
reference abnormalities with known
ground truth.
QuantX is indicated for evaluation of
patients presenting for high-risk screening,
diagnostic imaging workup, or evaluation
of extent of known disease. Extent of
known disease refers to both the
assessment of the boundary of a particular
abnormality as well as the assessment of
the total disease burden in a particular
patient. In cases where multiple
abnormalities are present, QuantX can be
used to assess each abnormality
independently.
This device provides information that may
be useful in the characterization of breast
abnormalities during image interpretation.
For the QI score and component radiomic
features, the QuantX device provides


biopsy, pathology, and clinical data, the
device creates and displays a cancer map
that assigns a probability to each voxel
within the prostate, indicating its
probability for containing clinically
significant prostate cancer (csPCa, defined
as GGG ≥2). A user selects a threshold for
the cancer map to create a boundary of the
lesion. The lesion boundary is assigned an
Encapsulation Confidence Score indicating
the confidence that all csPCa is
encapsulated within the boundary. The
Encapsulation Confidence Score is from a
lookup table generated by a database of
cases with known ground-truth. When
interpreted by a trained physician, this
information may be useful in supporting
lesion characterization and subsequent
patient management.
The Avenda Health AI Prostate Planning
Software may also be used as a medical
image application, for the viewing,
manipulation, 3D-visualization, and
comparison of MR prostate images. The
images can be viewed in a number of
output formats including volume
rendering. It enables visualization of
information that would otherwise have to
be visually compared disjointedly.

comparative analysis to lesions with
known outcomes using an image atlas and
histogram display format.
QuantX may also be used as an image
viewer of multi-modality digital images,
including ultrasound and mammography.
The software also includes tools that allow
users to measure and document images,
and output in a structured report.
Limitations: QuantX is not intended for
primary interpretation of digital
mammography images.

§892.2060, Radiological computer-assisted
diagnostic software for lesions suspicious
of cancer.

§892.2060, Radiological computer-
assisted diagnostic software for lesions
suspicious of cancer.


POK, Computer-Assisted Diagnostic
Software For Lesions Suspicious For
Cancer

Trained physicians in the intended
workflow (Radiologist/Urologist)

Trained physicians in the intended
workflow (Radiologist)

Same as the predicate device –
intended for use by the
appropriate trained physicians.

Different, but same intended
use and the differences do not
raise different questions of
safety or effectiveness.

Different, but same intended
use within the workflow and
the differences do not raise
different questions of safety or
effectiveness.

Image Review and Workflow Support
Interventional/Treatment Planning and
Guidance for Diagnostic and Interventional
Procedures

Similar – both devices are
Web-based software devices.

DICOM Compatible
Yes
Yes
Same as the predicate device.

Type of Scans
MR, DICOM-compatible
MR, DICOM-compatible
Same as the predicate device.

● Pan, zoom, rotate, slice scroll (view
multiple slices),

● Pan, zoom, rotate, change in contrast,
slice scroll (view multiple slices)


Planning Software
Quantitative Insights, Inc. QuantX

● Adjust window level,
minimize/maximize

Interventional tool, trajectory, and damage
volume overlay (overlay profiles
customizable by the user)

Algorithm-driven segmentation of the
prostate gland with the possibility of
manual adjustment

Prostate gland:
● Volume
● ROIs within the prostate
For each Lesion:
● Location
● Volume

For each lesion:
• Volume
• Surface Area

Assessment and characterization of
prostate lesions/abnormalities confirmed to
contain csPCA through biopsy and MR
image data.

Assessment and characterization of breast
abnormalities using MR image data.

Similar to the predicate device
with respect to analysis of
image-based information for
assessment of lesions.

Risk Stratification, Classification, Extent
of Disease (boundary and total burden)

Subset of the functionalities of
the predicate device.


Machine learning-based algorithm based
on patient information and image
morphological, intensity, and geometric
characteristics.

Machine learning-based algorithm based
on image morphological and enhancement
characteristics.

Cancer Estimation Map
Default Lesion Contour
Encapsulation Confidence Score

QI Score
Similar Cases Database (via Image Atlas
and Histogram Display)

The Proposed Device utilizes
different outputs from the
predicate device. As these
outputs do not impact the
intended use and are similarly
supplementary to information
reviewed by the physician as a
part of standard of care, they
do not raise different questions
of safety or effectiveness.

● DICOM
● Planning assets
● Intervention plan for supported
systems

Similar – both devices allow
export of findings for use in
future oncological steps.


Submitter Information [21 CFR 807.929(a)(1)]

2121 Sage Road, Suite 300
Houston, Texas 77056

Pending 510(k) clearance and marketing of device

Name of the device [21 CFR 807.92(a)(2)]

Automated Radiological Image Processing Software

Legally marketed device to which
equivalence is claimed
[21 CFR 807.92(a)(3)]

Device description
[21 CFR 807.92(a)(4)]

Spine CAMP™ is a fully-automated image processing software
device. It is designed to be used with X-ray images and is
intended to aid medical professionals in the measurement and
assessment of spinal parameters. Spine CAMP™ is capable of
calculating distances, angles, linear displacements, angular
displacements, and mathematical combinations of these metrics
to characterize the morphology, alignment, and motion of the
spine. These analysis results are presented in the form of
reports, annotated images, and visualizations of intervertebral
motion to support their interpretation.

Indications for use
[21 CFR 807.92(a)(5)]

Spine CAMP™ is a fully-automated software that analyzes X-
ray images of the spine to produce reports that contain static
and/or motion metrics. Spine CAMP™ can be used to obtain
metrics from sagittal plane radiographs of the lumbar and/or
cervical spine and it can be used to visualize intervertebral
motion via an image registration method referred to as
“stabilization.” The radiographic metrics can be used to
characterize and assess spinal health in accordance with
established guidance. For example, common clinical uses
include assessing spinal stability, alignment, degeneration,
fusion, motion preservation, and implant performance. The
metrics produced by Spine CAMP™ are intended to be used to
support qualified and licensed professional healthcare
practitioners in clinical decision-making for skeletally mature
patients of age 18 and above.

Summary of the technological characteristics of the device compared to the predicate device
[21 CFR 807.92(a)(6)]


QIH*
* The product code for the predicate
device is LLZ. The QIH product
code was created subsequent to
clearance of the predicate device
and appears to be a better match for
Spine CAMP™ than LLZ since
Spine CAMP™ employs non-
adaptive machine learning
algorithms that were trained from
data generated by the predicate
device to automate the radiological
image processing and analysis.

Image Input
DICOM
AVI, DICOM, JPEG, TIFF, BMP

Anatomical Area
Spine
Musculoskeletal (including Spine)

Vertebral body detection;
Vertebral body landmark
specification; Vertebral body
registration

Landmark specification; rigid body
registration

Display of Image Alignment /
Stabilization

Intended User
Trained professionals
Trained professionals

Spine CAMP™ is designed to utilize the same analysis methodology as the predicate device, KIMAX
QMA®, except that software operations performed manually in the predicate device software are
automated in Spine CAMP™ through the use of non-adaptive AI models – specifically for lumbar and
cervical spine X-rays. The types of inputs and outputs are identical between the two devices. The data
labels used to train Spine CAMP™’s AI models were derived directly from the KIMAX QMA®
technology. Similarly, substantial equivalence between Spine CAMP™ and the predicate device was
established by directly running the same images through both systems and evaluating the correlation
and statistical equivalence of their outputs.

Summary of bench tests (non-clinical) conducted for determination of substantial equivalence
[21 CFR 807.92(b)(1)]

Software verification and validation testing was completed to demonstrate functionality of the device
across multiple datasets that had not been used to train any of the AI models and that had ground truth
established directly from the predicate device. Ground-truth results for these validation datasets were
obtained from experienced operators using the predicate device. The software functioned as intended
and all results observed were as expected.
Additional bench testing was performed by evaluating Spine CAMP™’s performance on a large dataset
that was previously analyzed by five experienced operators using the predicate device. This dataset
included 215 lateral cervical spine radiographs and 232 lateral lumbar spine radiographs. Statistical
correlations and equivalence tests were performed by directly comparing vertebral landmark
coordinates, image calibration, and radiographic metrics between Spine CAMP™ and the predicate
device. This analysis demonstrated correlation and statistical equivalence for all variables evaluated.


Device Name:
Xeleris V Processing and Review System

GE Medical Systems Israel, Functional Imaging


Manufacturer:
Hermes Medical Solutions AB


Xeleris 4.0 Processing and Review System

510(k) Premarket Notification Submission


Attributes
Predicate Device
Proposed Device

Xeleris V Processing and Review
Xeleris V Processing and Review

- NM Imaging Data: SPECT-CT and
Whole-body Planar

- NM Imaging Data: SPECT-CT, PET-
CT, and Whole-body Planar
- Non-Imaging Data

- Preparation for Dosimetry Toolkit
- Q.Volumetrix MI for Q.Thera AI

- Organ and Lesion isotope
residence time
- Radiation dose calculations per
MIRD committee of SNM and
ICRP Publication 89

- Derived planar images from
SPECT-CT studies, as cleared on
the reference Xeleris 4.0.
- Derived planar images from
SPECT only studies.