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
- Chest CTs (with or without contrast).
- Single energy exams.
- Scans performed with a 64 or
greater number of detectors.
- Scans performed on
(cid:68)(cid:71)(cid:88)(cid:79)(cid:87)(cid:86)(cid:18)(cid:87)(cid:85)(cid:68)(cid:81)(cid:86)(cid:76)(cid:87)(cid:76)(cid:82)(cid:81)(cid:68)(cid:79)(cid:3)(cid:68)(cid:71)(cid:88)(cid:79)(cid:87)(cid:86)(cid:3)(cid:149)(cid:3)(cid:20)(cid:27)(cid:3)(cid:92)(cid:72)(cid:68)(cid:85)(cid:86)(cid:3)
of age.
- Slice thickness; 0.5 - 5.0 mm axial.
Exclusion Criteria
- All studies that are technically
inadequate, including studies with
motion artifacts, severe metal
artifacts, or inadequate field of view.

Inclusion criteria
- chest CT with or without contrast
- Single energy exams.
- Performed on CT scanners with 64
or greater number of detectors
- Scans performed on
(cid:68)(cid:71)(cid:88)(cid:79)(cid:87)(cid:86)(cid:18)(cid:87)(cid:85)(cid:68)(cid:81)(cid:86)(cid:76)(cid:87)(cid:76)(cid:82)(cid:81)(cid:68)(cid:79)(cid:3)(cid:68)(cid:71)(cid:88)(cid:79)(cid:87)(cid:86)(cid:3)(cid:149)(cid:3)(cid:20)(cid:27)(cid:3)(cid:92)(cid:72)(cid:68)rs
of age
- Slice thickness; 0.5 mm to 5.0 mm
axial slices
Exclusion Criteria
- All studies that are technically
inadequate, including studies with
motion artifacts, severe metal
artifacts, or 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).

- Integrated with image routing module
via image communication platform
(ICP) (image acquisition).
- Algorithm module (image
processing)
- Integrated with desktop application
for workflow integration (feed and
non-diagnostic Image Viewer).


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

Table 2. Time-to- notification comparison for BriefCase devices (Seconds)


Table 4. Frequency Distribution of Gender *


Table 5. Frequency Distribution of Manufacturer


Medical
charged-particle
radiationtherapy
system


Manufacturer TheraPanaceaSAS TheraPanaceaSAS Equivalent

ART-Plan is indicated for cancer
patients for whom radiation treatment
has been planned. It is intended to be
used by trained medical professionals
including, butnotlimitedto,radiologists,
radiation oncologists, dosimetrists, and
medicalphysicists.
ART-Plan is a software application
intended to display and visualize 3D
multi-modal medical image data. The
user may import, define, display,
transform and store DICOM 3.0
compliant datasets (including regionsof
interest structures). These images,
contours and objects can subsequently
be exported/distributed within the
system, across computer networks
and/or to radiation treatment planning
systems. Supported modalities include
CT, PET-CT, CBCT, 4D-CT and MR
images.
ART-Plan supports AI-basedcontouring
on CT and MR images and offers
semi-automatic and manual tools for
segmentation.
To help the user assess changes in
image data and to obtain combined
multi-modal image information,
ART-Plan allows the registration of


anatomical and functional images and
display of fused and non-fused images
to facilitate the comparison of patient
imagedatabytheuser.
With ART-Plan, users are also able to
generate, visualize, evaluate and
modifypseudo-CTfromMRIimages

It is intended to be used by trained
medical professionals including, but not
limited to, radiologists, radiation
oncologists, dosimetrists, and medical
physicists/Hospitals

AutomaticallydelineatesOARsand
healthylymphnodes
Deeplearningalgorithm.
Automaticsegmentationincludesthe
followinglocalizations:
*headandneck(onCTimages)
*thorax/breast(formale/femaleandon
CTimages)
*abdomen(onCTimagesandMR
images)
*pelvismale(onCTimagesandMR
images)
*pelvisfemale(onCTimages)
*brain(onCTimagesandMRimages)

Equivalent-
Thecandidate
deviceand
predicateare
capableof
automatically
contouringthe
organ-at-risk
(OAR)and
healthylymph
nodesusing
AI(deep
learning)
algorithm.
Thecandidate
device
includes48
additional
structures to
thealready
existing
localizations

Equivalent
Thebugfixes
introducedin
thecandidate
devicedonot
affectthe
safetyor
performance
ofthe
predicate
device


Brainstem Cerebellum Chiasma Encephalon Esophagus Hypophyse Larynx

LeftIMC
(internal
mammary
chain)lymph
node

RightIMC
(internal
mammary
chain)lymph
node


Left
vestibular
semicircular
canals
(VSCC)

Right
versibular
semicircular
canals
(VSCC)


Intervariabilitycomparisonto
experts
(CriterionCofperformance
criteria)
Minsamplesizeforevaluation
method:20

Samplesize:33
whichisabovethe
minimumdatasample
size

7. pulmonary
Intervariabilitycomparisonto
DICEdiff
Samplesize:33


experts
(CriterionCofperformance
criteria)
Minsamplesizeforevaluation
method:20


Thisstructurecorrespondstoa
booleanofotherstructures:carina
+leftbronchus+rightbronchus+
leftbronchia+rightbronchiawhich
whichhaveallpassedthe
performancetests

Qualitativeevaluationbyexperts
CriterionD
Minsamplesizeforevaluation
method:15

Samplesize:20
whichisabovethe
minimumdatasample
size


Qualitativeevaluationbyexperts
CriterionD
Minsamplesizeforevaluation
method:15

Samplesize:20
whichisabovethe
minimumdatasample
size

Samplesize:15
whichisabovethe
minimumdatasample
size


Intervariabilitycomparisonto
experts
(CriterionCofperformance
criteria)
Minsamplesizeforevaluation
method:20

Samplesize:30
whichisabovethe
minimumdatasample
size


Study Protocol and
Report Annotate
Performances
Summary(v1.10.1)

The purpose of this document is to describe the testing
protocols and testing results for validating the performance of
the Annotate module. TheperformancestudyoftheART-Plan
module, Annotate, evaluates the precision of the contours
done by the software either i) againsttheonedonebyhuman
experts through a direct comparison or ii) by a qualitative
validationdonebyhumanexperts.
The objective of the tests is to demonstrate that the
auto-segmentation algorithms (CT and MR) of the module
Annotate pass at least one acceptance criterion. This
document includes test procedures, documentation,
references, specifications, and acceptance criteria. This
document is updated to take into account modificationsmade
in ART-Plan v1.10.1 with the addition of heart substructures
andSBRTintheCTautomaticsegmentation.

Study Protocol and
Report quantitative
validation of Annotate

This test demonstrates that the Annotate provides clinically
acceptable (compared to inter-expert variability) for SBRT
structures. All organs that have passed the acceptance


criterion of reaching a percentage of a DSC(mean)≥ 0.8 or
DSC(mean)≥0.54) or DSC(mean)≥mean(DSC
inter-expert)+5% relative error (quantitative evaluation) have
beenreleasedinv.1.10.1.

Study Protocol and
Report Qualitative
Validation of Annotate
in ART-Plan V1.10.1
for Heart
substructuresCT

This test demonstrates that the module Annotate provides
acceptablecontoursfortheorgansevaluatedonCTimagesof
patients. All organs that have passedtheacceptancecriterion
of reaching a percentage of atleast85%ofAorB(qualitative
evaluation)havebeenreleasedinv.1.10.1.


to plug in multi-vendor
applications which meet
interface requirements.
uOmnispace is intended to
be used by trained
professionals, including but
not limited to physicians
and medical technicians.
The system is not intended
for the displaying of digital
mammography images for
diagnosis in the U.S.

syngo.via supports
interpretation and
evaluation of
examinations within
healthcare institutions,
For example, in
Radiology, Nuclear
Medicine and Cardiology
environments.
The system is not
intended for the
displaying of digital
mammography images
for diagnosis in the U.S.

Client-Server
Architecture and Multi-
User Access

Yes
Based on client-server
architecture and supports
multi-user access.

Yes
Communicate and store
medical images based on
standard communication
protocol DICOM.

Yes
Client:
Microsoft Windows 7 or
Microsoft

Yes
Client:
Microsoft Windows 7 SP1
or Microsoft

Functional Substantially Equivalent
The client and server operating systems of
the proposed device are Microsoft Windows
and Linux respectively, while those of the


Windows 10 or compatible
versions
Server:
Linux Core CentOS7.7 or
compatible versions

Windows 8.1 or Microsoft
Windows 10
Server:
Microsoft Windows Server
2008 R2 or
Microsoft Windows Server
2012 R2.
or Microsoft Server 2016

predicate device are Microsoft Windows and
Microsoft Windows Server respectively.
This difference doesn’t impact the safety
and effectiveness of the subject device.

Yes
Pre-processing: Auto
process images before
loading into post-processing
applications.
The client workflow
supports both single monitor
and dual monitors.

Yes
Workflows support the
user in preparing images
for examination.
Supports various monitor
setups workflow.

Functional Substantially Equivalent
Pre-processing is equivalent to part of the
“preparing images for examination”, the
predicate device also assigns the
examination to an application. This
difference doesn’t impact the safety and
effectiveness of the subject device.

Yes
Patient administration
displays the patient data and
offers the function of
searching, sorting, and
editing of patient data and
image preview.

Yes
With simplified search
functionality, clearer
structure of search results,
unlimited search results,
periodic updates of search
results, image preview and
flexible floating patient
browser window.

Functional Substantially Equivalent
The proposed device does not support
flexible floating patient browser window.
This difference doesn’t impact the safety
and effectiveness of the subject device.


2D image viewing, Textual
and graphical annotations，
distance, angle, ROI, image
addition and subtraction,
image filter are supported.

2D image viewing, Textual
and graphical
annotations，distance,
angle, ROI, image addition
and subtraction, image
filter are supported.

Yes
3D image viewing, Save 3D
images in batches.
Segmentation functions such
as CT bone removal tools,
CT/MR tissue growing tools,
VOI, cut.

Yes
3D image viewing.
Save 3D images in
batches.
Segmentation functions
such as CT bone removal
tools, CT tissue growing
tools, VOI, cut.

Yes
Volume rendering (VR) with
Hyper Realistic Rendering
(HRR), Multi-Planar
Reconstruction (MPR),
Maximum Intensity
Projection (MIP), Minimum
Intensity Projection (MinIP),
Curved Planar Reformation
(CPR), Surface-shaded
Display (SSD).
Automatic body bone
removal algorithm

Yes
Volume rendering (VR),
Multi-Planar
Reconstruction (MPR),
Maximum Intensity
Projection (MIP),
Minimum Intensity
Projection (MinIP),
Curved Planar
Reformation (CPR),
Surface-shaded Display
(SSD).
Automatic body bone
removal algorithm

Functional Substantially Equivalent
HRR is an extension to the standard VR
rendering algorithm to visualize the
photorealistic images.
This difference between the proposed device
and the reference device doesn’t impact the
safety and effectiveness of the subject device
as the necessary measures taken for the
safety and effectiveness of the proposed
device.


Yes
Threshold based
interactive algorithm

Functional Substantially Equivalent
Both are used for rib segmentation, the
reference device requires interaction,
while the proposed device does not.
This difference between the proposed device
and the reference device doesn’t impact the
safety and effectiveness of the subject device
as the necessary measures taken for the
safety and effectiveness of the proposed
device

Yes
3D virtual endoscopy view
and extract the centerline of
vessel, airway and colon.
Imaging algorithms:
CT colon Inner View
algorithm
CT vessel Inner View
algorithm
CT lung trachea Inner View
algorithm
MR vessel Inner View
algorithm

Yes
3D virtual endoscopy view
and extract the centerline
of vessel, airway and
colon.
Imaging algorithms:
CT colon Inner View
algorithm, CT vessel Inner
View algorithm
CT lung trachea Inner
View algorithm

Functional Substantially Equivalent
The MR vessel Inner View algorithm is
same with CT vessel Inner View algorithm,
the difference of input modality does not
affects the output.
This difference between the proposed device
and the reference device doesn’t impact the
safety and effectiveness of the subject device
as the necessary measures taken for the
safety and effectiveness of the proposed
device.


Support to print image and
selection of printer.

Yes
Support to create reports,
text editing and image
inserting，customize report
template, exporting and
printing of report.

Yes
Import of DICOM images
from configured network
nodes (modalities, medical
imaging process software,
PACS, etc) or local and
network drives or DVD/CD.
Export (archive) DICOM
images to network nodes, or
local and network drives or
DVD/CD.

Yes
Import of DICOM data
from network nodes or
external media, and of
DICOM-compliant or non
DICOM compliant data
from external media and
Windows file system.
Export to CD/DVD,
Windows file system, or
other DICOM nodes.

Functional Substantially Equivalent
The proposed product does not support to
import non DICOM compliant data from
external media and Windows file system.
This difference between the proposed device
and the reference device doesn’t impact the
safety and effectiveness of the subject device
as the necessary measures taken for the
safety and effectiveness of the proposed
device


The average dice of testing data is higher than 0.8

Table 8-1. Validation type and acceptance criteria


OF TECHNOLOGICAL CHARACTERISTICS
Subject Device
HipCheck

WITH THE PREDICATE DEVICE
PredicateDevice
HipCheck

Intended
Use/Indications for Use
Statement

[K230045]
HipCheck assists the surgeon to
determine quantitative measurements
for femoroacetabular impingement
(FAI) procedures. HipCheck provides
static localization information derived
from image processing of intra-
operatively acquired static fluoroscopic
images, by superposition of virtual
measurement tools onto those X-ray
images for skeletally mature patients.
HipMap FAI Analysis is a patient-
specific report used to support surgeon
or radiologist pre-operative clinical
decision making. HipMap
femoroacetabular impingement (FAI)

[K182359]
HipCheck assists the surgeon to
determine quantitative measurements
during femoroacetabular impingement
procedures.
HipCheck provides static localization
information derived from image
processing of intra-operatively acquired
X-ray images, by superposition of virtual
measurement tools onto those X-ray
images.

VI. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE PREDICATE DEVICE


potential FAI, including measurements
and visualizations that describe hip

Patients undergoing arthroscopic
surgery.

Surgical Approach
Same as predicate.
Arthroscopic surgery, specifically

femoroacetabular impingement.
Surgeons and clinical staff.

Operational
Environment
Primary Device Function

SHaimpCeh aesc pkr:e dicate.
Locate the femoral head and neck to
determine the alpha angle using intra-
oHpiperMataipve F XA-Ir Aayn iamlyasgiess: .
Provide three-dimensional analyses, 3D
surface reconstructions, and annotated

Surgical suite.
Locate the femoral head and neck to
determine the alpha angle using intra-
operative X-ray images.

images to support surgeons with pre-
operative clinical decision-making.

SHaimpCeh aesc pkr:edicate.
THaipblMeta p FAI Analysis:

General purpose computing platform
and/or HipCheck tablet.

N/A – Device does not have any patient
contact.

SHaimpCeh aesc pkr:e dicate.
- Workstation for viewing software
- Dedicated software for image
processing and display of virtual
HmipeMasaupr eFmAIe nAtn taoloylssi s:
Interactive HTML report for viewing on
general purpose computing platform

N /A – Device is not provided sterile or
a s a single use product.
- Workstation for viewing software
- Dedicated software for image
processing and display of virtual
measurement tools


- Extraction of the region of interest
- Extended Gabor filtering
- Binary edge detection and
classification
- Circular Hough transformation
- Feature point tracing

- RANSAC circle matching
- Target circle selection

No energy applied to the patient or
operating staff.

Software Level of
Concern
Software Operating
Environment

SHaimpCeh aesc pkr:e dicate.
Programming language used is C/C++.
The operating system is Windows 10.
The hardware platform consists of a

Moderate
Programming language used is C/C++.
The operating system is Windows 8.1.
The hardware platform consists of a
microprocessor-controlled system,

microprocessor-controlled system,
based on a PC.


DHT8B: Division of Radiological Imaging Devices and

Office of Product Evaluation and Quality


Parameter
Rapid ANRTN (Subject Device)
Viz Aneurysm (Predicate Device)

Rapid Aneurysm Triage and
Notification (ANRTN) is a
radiological computer-assisted
triage and notification software
device for analysis of CT images
of the head. The device is
intended to assist hospital
networks and trained radiologists
in workflow triage by flagging and
prioritizing studies with suspected
saccular aneurysms during routine
patient care.
Rapid ANRTN uses an artificial
intelligence algorithm to analyze
images and highlight studies with
suspected saccular aneurysms in a
standalone application for study
list prioritization or triage in
parallel to ongoing standard of
care. The device generates
compressed preview images that
are meant for informational
purposes only and not intended for

Viz ANEURYSM (Viz ANX)
is a radiological computer-
assisted triage and notification
software device for analysis of
CT images of the head. The
device is intended to assist
hospital networks and trained
radiologists in workflow triage
by flagging and prioritizing
studies with suspected
aneurysms during routine
patient care.
Viz ANEURYSM uses an
artificial intelligence algorithm
to analyze images and highlight
studies with suspected
aneurysms in a standalone
application for study list
prioritization or triage in
parallel to ongoing standard of
care. The device generates
compressed preview images
that are meant for informational


diagnostic use. The device does
not alter the original medical
image and is not intended to be
used as a diagnostic device.
Analyzed images are available for
review through the PACS, email
and mobile application. When
viewed the images are for
informational purposes only and
not for diagnostic use. The results
of Rapid ANRTN, in conjunction
with other clinical information and
professional judgment, are to be
used to assist with
triage/prioritization of saccular
aneurysm cases. Radiologists who
read the original medical images
are responsible for the diagnostic
decision. Rapid ANRT is limited
to analysis of imaging data and
should not be used in-lieu of full
patient evaluation or relied upon to
make or confirm diagnosis.
Rapid ANRT is limited to
detecting saccular aneurysms at
least 4mm in diameter in adults.
<inclusions and limitations
provided above>

purposes only and not intended
for diagnostic use. The device
does not alter the original
medical image and is not
intended to be used as a
diagnostic device. Analyzed
images are available for review
through the standalone
application. When viewed
through the standalone
application the images are for
informational purposes only
and not for diagnostic use. The
results of Viz ANEURYSM, in
conjunction with other clinical
information and professional
judgment, are to be used to
assist with triage/prioritization
of medical images.
Radiologists who read the
original medical images are
responsible for the diagnostic
decision. Viz ANEURYSM is
limited to analysis of imaging
data and should not be used in-
lieu of full patient evaluation or
relied upon to make or confirm
diagnosis.
Viz ANEURYSM is limited to
detecting aneurysms at least
4mm in diameter.

Identify patients
with pre-specified
clinical condition


Preview Images
Non-diagnostic
Non-diagnostic


Specification
Predicate Device
Proposed Device

It is used by radiation oncology
department to register
multimodality images and segment
(non-contrast) CT images, to
generate needed information for
treatment planning, treatment
evaluation and treatment
adaptation.

Auto Segmentation generates a Radiotherapy
Structure Set (RTSS) DICOM with segmented
organs at risk which can be used by dosimetrists,
medical physicists, and radiation oncologists as
initial contours to accelerate workflow for
radiation therapy planning. It is the responsibility
of the user to verify the processed output contours
and user-defined labels for each organ at risk and
correct the contours/labels as needed. Auto
Segmentation may be used with images acquired
on CT scanners, in adult patients.

Compatible Modality
Non-contrast CT images
CT (contrast and non-contrast) images

Basic result preview of automatic
segmentation results. Manual
segmentation is possible.
Configuration menu.

Automated execution of the software with no user
interaction, other than configuration settings.
Generated contours are automatically transmitted
to review workstation(s) supporting RTSS objects
for review and editing, as needed.

No Limitation on scanner model,
DICOM 3.0 compliance required

510(k) Premarket Notification Submission – Auto Segmentation


OAR
Auto Segmentation
Acceptance Criteria

Table 1: Summary of Auto Segmentation performance


Inferior PCM (Pharyngeal
Constrictor Muscle)


HERA W9 /HERA W10
HERA W9/HERA W10 (K220043)

Manufacturer
SAMSUNG MEDISON CO.,LTD
SAMSUNG MEDISON CO.,LTD

The HERA W9/ HERA W10 Diagnostic
Ultrasound System and transducers are
intended for diagnostic ultrasound
imaging
and fluid analysis of the human body.

- Q Scan
- ClearVision
- MultiVision
- Panoramic
- NeedleMate+
- AutoIMT+
- Elastoscan+
- E-Thyroid
- E-Breast
- E-Strain
- S-Detect for Breast
- S-Detect for Thyroid
- ADVR
- 3D Imaging
- (Volume Data Acquisition)
- 3D Imaging presentation
- 3D Cine/4D Cine
- 3D Rendering MPR
- 3D XI MSV/Oblique View
- Volume CT

- Q Scan
- ClearVision
- MultiVision
- Panoramic
- NeedleMate+
- AutoIMT+
- Elastoscan+
- E-Thyroid
- E-Breast
- E-Strain
- S-Detect for Breast
- S-Detect for Thyroid
- ADVR
- 3D Imaging
- (Volume Data Acquisition)
- 3D Imaging presentation
- 3D Cine/4D Cine
- 3DRendering MPR
- 3D XI MSV/Oblique View
- Volume CT


- 3D MagiCut
- Volume Calculation
- (VOCAL, XI VOCAL) XI STIC
- HDVI
- RealisticVue
- CEUS+
- HQ-Vision
- MV-Flow
- CrystalVue
- CrystalVue Flow*
- 5D CNS+
- 5D Follicle
- 5D Heart Color
- 5D Limb Vol
- 5D LB
- 5D NT
- 2D NT
- IOTA-ADNEX
- BiometryAssist
- E-Cervix
- LumiFlow
- ShadowHDR
- MPI+*
- Slice A
- HeartAssist *
- ViewAssist

HERA W9/ HERA W10 Diagnostic Ultrasound Systems


Software
CrystalVue Flow
Not Supported
Supported

Hardware
Internal DVD
Not Included
Included

The differences between HERA W9 and HERA W10 in the subject device are as below.


ANSI AAMI ES60601-1:2005/(R)2012 and A1:2012, C1:2009/(R)2012 and
A2:2010 /(R)2012 Medical Electrical Equipment - Part 1: General
Requirements for basic safety and essential performance.

IEC60601-1-2: 2020(4.1 Edition), Medical electrical equipment - Part 1-2:
General requirements for basic safety and essential performance - EMC


IEC60601-2-37:2007 + A1:2015, Particular requirements for the safety of
ultrasonic medical diagnostic and monitoring equipment

ISO 10993-1:2018, Biological evaluation of medical devices -- Part 1:
Evaluation and testing within a risk management process.

ISO 14971:2019, Medical devices - Application of risk management to medical
devices

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


Identifyandmarkregionsof
interest(ROIs)onchest
radiographsandlabelthebox
aroundtheROIaslung
hyperinflation

Identifyandmarkregionsof
interest(ROIs)onchest
radiographsandlabelthebox
aroundtheROIasoneofthe
following:Cardiac,
Mediastinum/Hila,Lungs,Pleura,
Bones,SoftTissues,Hardware,or
Other

Deviceintendedforuseasa
concurrentreadingaidfor
physiciansinterpretingchest
radiographs


Subject Device
Hologic, Inc.
Genius AI Detection 2.0
with CC-MLO
Correlation

Predicate
Hologic, Inc.
Genius AI
Detection 2.0

21 CFR 892.2090 /
Radiological Computer
Assisted Detection and
Diagnosis Software


Hologic Selenia
Dimensions
Hologic 3Dimensions
Supports both models
in the following
modes:
standard
resolution 1-mm
slices
high resolution 1-
mm slices (Clarity
HD)
high resolution 6-
mm SmartSlices
(3DQuorum)

Giotto FFDM
General Electric DBT
Fujifilm DBT

The predicate
and subject
device include
the support of
the following
modes on the
Hologic DBT
systems only:
standard res.
1-mm slices
high res. 1-
mm slices .
high resolution
6-mm
SmartSlices

Radiological
computer assisted
detection and
diagnostic software.

Image processing
device utilizing
machine learning to
aid in the detection,
localization, and
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.

Software that applies
algorithms for
recognition of
suspicious
calcifications and soft
tissue lesions to
detect and
characterize findings
in radiological breast
images and provide
information about
the presence,
location, and
characteristics of the
findings to the user.

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

Genius AI Detection 2.0 CC-MLO Correlation


Symptomatic
and
asymptomatic
women
undergoing
mammography

The device is
intended to be used
in the population of
women undergoing
screening
mammography and
digital breast
tomosynthesis.

MQSA-Qualified
Interpreting
Physicians and
Radiologists

Intended users of
Transpara® are
physicians qualified
to read screening
mammography
exams and digital
breast tomosynthesis
exams.

Output Device
Softcopy Workstation
Same
Same

Deployment
Stand-alone computer
Same
Same

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 ).

Computer aided
detection (CAD)
marks to highlight
locations where the
device
detected suspicious
calcifications or soft
tissue lesions
Decision support is
provided by region
scores on a scale
ranging from 0-100,
with
higher scores
indicating a higher
level of suspicion.


Characteristic
Proposed Device
Predicate Device
Comparison

Regulation
21 CFR 892.1750
21 CFR 892.1750
Same

Software
Device is software only
Device is software only
Same

Anatomical Site
Thoracic,Chest,Cardiac
Thoracic,Chest,Cardiac
Same

Support Input CT Scan
Low and standard dose.
Low and standard dose.
Different - Predicate is

Cardiac-gated and non-
Non-cardiac-gated. Up
not labeled for use in

cardiac-gated. Up to
to 3mm slice thickness.
cardiac-gated scans.

Use Environment
Hospitals and Clinics in-
Hospitals and Clinics in-
Same

cluding Inpatient and
cluding Inpatient and

cer screening programs.
cer screening programs.

Calcium Detection
Automatic
Automatic
Same

Default Threshold
130 (HU)
130 (HU)
Same

Calcium Reporting
CAC score and reference
3 category CAC risk
Different - Predicate

Outputs
DICOM Report and
DICOM Report and
Same

Image Viewing
Images viewed on exist-
Images viewed on exist-
Same

ing viewing workstation.
ing viewing workstation.


Subject Device
Primary Predicate
Secondary Predicate

Device Name
Detect
Second Opinion
Dental Assist

892.2070 Medical Image
Analyzer
892.2050 Picture Archive
and Communication
System

Trained professionals
including, but not limited
to, dentists and dental
hygienists

Adults 22 years of age
and older with permanent
dentition

Patients 12 years and
older with permanent
teeth

Platform
Cloud-based
Windows 7
Cloud-based

Intraoral (bitewing,
periapical)
Extraoral (panoramic)

RVG, DICOM, JPEG,
TIFF, and PNG and
converts to JPEG

Caries, Discrepancy at
the margin of an existing
restoration, Calculus,
Periapical radiolucency,
Crown (metal, including
zirconia & non-metal),
Filling (metal & non-
metal), Root canal,
Bridge and Implants.


Mesial and distal bone
levels associated with
each tooth


Bitewing: CEJ-Bone: Sensitivity
98.1% (96%, 99.5%)

Bitewing: CEJ-Bone: Specificity
93% (88.7%, 96.7%)

Bitewing: CEJ-Bone: MAE
0.513 mm (0.444 mm, 0.593 mm)

Bitewing: CEJ-Bone/CEJ-Root Ratio: MAE
3.8% (3.3%, 4.4%)

Periapical: CEJ-Bone: Sensitivity
98.2% (96.2%, 99.7%)

Periapical: CEJ-Bone: Specificity
88.5% (82.8%, 93.4%)

Periapical: CEJ-Bone: MAE
0.572 mm (0.497 mm, 0.653 mm)

Periapical: CEJ-Root: Sensitivity
96.9% (94.2%, 99%)

Periapical: CEJ-Root: Specificity
92.2% (87.9%, 96%)

Periapical: CEJ-Root: MAE
0.735 mm (0.612 mm, 0.868 mm)

Periapical: CEJ-Bone/CEJ-Root Ratio: MAE

Extraoral: CEJ-Bone-Root: Sensitivity
91.6% (89.6%, 93.6%)

Extraoral: CEJ-Bone-Root: Specificity
84.3% (70.8%, 95.8%)

Extraoral: CEJ-Bone/CEJ-Root Ratio: MAE
4.7% (4%, 5.5%)

Table 2: Primary Study Endpoints and Results


Primary
K173291
6440 – MyLab9
Esaote S.p.A.

Reference
K192157
6450 – MyLabX8
Esaote S.p.A.

Reference
K212021
6430 – MyLabX75
Esaote S.p.A.


Main application
Districts
Invasive access

Abdominal, Breast, Musculo-
skeletal, Neonatal, Pediatric, Small
Organs (Testicles), Thyroid,
Urological


2CWL (Continous Wave Doppler)
The present submission

5CWL (Continous Wave Doppler)
The present submission

• The following probes management is added on the upgraded 6440 system:


Success Rate > 80%
Success Rate > 90%
Success Rate > 75%


BoneMRI is an image
processing software that can
be used for image
enhancement in MRI
images. It can be used to
visualize the bone structures
in MRI images with
enhanced contrast with
respect to the surrounding
soft tissue. It is to be used in
the pelvic region, which
includes the bony anatomy
of the sacrum, hip bones
and femoral heads; and the
lumbar spine region, which
includes the bony anatomy
of the vertebrae from L3 to
S1. BoneMRI is not to be
used for diagnosis or
monitoring of (primary or
metastatic) tumors.
Warning: BoneMRI images
are not intended to replace
CT images.


Target Population
Adults
Adults
The same


Device Nature
Software package
Software package
The same

MRIguidance software
implements an image
enhancement algorithm
using convolutional neural
network. Original images are
enhanced by running them
through a cascade of filter
banks, where thresholding
and scaling operations are
applied. Separate neural
network-based filters are
obtained to assign a
Hounsfield Unit (HU) value
to a single volume element,
based on intensity and
contextual information. The
parameters of the model
were obtained through an
algorithm development
pipeline.

None – enhanced images
are viewed on existing
PACS workstations

The software operates on
DICOM files on the file
system, enhances the
images, and stores the
enhanced images on the file
system. The receipt of
original DICOM image files
and delivery of enhanced
images as DICOM files


depends on other software
systems. Enhanced images
co-exist with the original
images.


Anatomy
Pelvic region
Lumbar spine region

Sacroiliitis, developmental hip
dysplasia, avascular necrosis
of femoral head and femoral
acetabular impingement.

Sacroiliitis, degenerative spine
diseases, spondylolisthesis,
radiculopathy, spondylosis and
spinal fractures.

Data origin/Ethnicity
USA, Europe, Asia
USA, Europe, Asia

The demographics of the patient population are described in the table below.


Subject Swoop® Portable MR
Subject Swoop® Portable MR Imaging
Predicate Swoop® Portable MR

Imaging System™
System™
Imaging System™ (K223247)

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 professional
health care facilities such as 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

Strength
X: 24 mT/m, Y: 23 mT/m, Z: 39 mT/m
Same

The table below compares the subject device to the predicate.


Rise Time
X: 2.1 ms, Y: 2.0 ms, Z: 3.8 ms
Same

Slew Rate
X: 24 T/m/s, Y: 22 T/m/s, Z: 21 T/m/s
Same

Computer Display
Hyperfine-supplied tablet
Same

Inner Dimensions (mm)
205 mm x 240 mm
Same

Patient Weight Capacity
1.6kg-200 kg
Same

Noise correction and line noise
suppression for all sequences

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

Fast Iterative Shrinkage Thresholding
Algorithm (FISTA)

• 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)

Software verification testing in accordance with
the design requirements to ensure that the
software requirements were met.

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

Testing to verify the subject device 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 to ensure the device meets user needs
and performs as intended.

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


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.


Classification Description
21 CFR Section
Product Code

Automated Radiological Image Processing Software


Description
Decision
Subject Device (K230209)
Predicate Device (K213544)
Reference Device (K220975)

Trade/Device name
Sonix Health
TOMTEC-ARENA
V8 Diagnostic Ultrasound System

Product Code
QIH (Subsequent Product Code: LLZ)
QIH (Subsequent Product Code: LLZ)
IYN, IYO, ITX

Sonix Health is intended for quantifying
and reporting echocardiography for use by
or on the order of a licensed physician.
Sonix Health accepts DICOM-compliant
medical images acquired from ultrasound
imaging devices. Sonix Health is indicated
for use in adult populations.
Ultrasound images are acquired via the B
(2D), M, Pulsed-wave Doppler, and
Continuous-wave Doppler modes.

TOMTEC-ARENA software is a
clinical software package designed for
review, quantification and reporting of
structures and function based on multi-
dimensional digital medical data
acquired with different modalities.
TOMTEC ARENA is not intended to
be used for reading of mammography
images.

The V8 / V7 diagnostic ultrasound system and
probes are designed to obtain ultrasound images
and analyze body fluids.
The clinical applications include: Fetal/Obstetrics,
Abdominal, Gynecology, Intra-operative, Pediatric,
Small Organ, Neonatal Cephalic, Adult Cephalic,
Trans-rectal, Trans-vaginal, Muscular-Skeletal
(Conventional, Superficial), Urology, Cardiac
Adult, Cardiac Pediatric, Thoracic, Trans-
esophageal (Cardiac) and Peripheral vessel.
It is intended for use by, or by the order of, and
under the supervision of, an appropriately trained
healthcare professional who is qualified for direct
use of medical devices. It can be used in hospitals,
private practices, clinics and similar care
environment for clinical diagnosis of patients.
Modes of Operation: 2D mode, Color Doppler
mode, Power Doppler (PD) mode, M mode, Pulsed
Wave (PW) Doppler mode, Continuous Wave
(CW) Doppler mode, Tissue Doppler Imaging
(TDI) mode, Tissue Doppler Wave (TDW) mode,
ElastoScan Mode, Combined modes, Multi-Image
mode (Dual, Quad), 3D/4D mode.

Indications for use of TOMTEC-
ARENA TTA2 software are
quantification and reporting of
cardiovascular, fetal, abdominal
structures and function of patients with
suspected disease to support the
physician in the diagnosis

Where used
(hospital, home,
ambulance, etc.)

Inside of hospitals, clinics, and physician’s
offices.

Inside and outside of Hospitals,
Clinics, and Physician’s offices.

Sonix Health utilizes artificial intelligence
to automate previous manual quantification
tasks, resulting in increased efficiency for
users. Our system performs view

IMAGE-COM is a basic module for
reviewing and measuring digital
medical data. It supports routine
workflows for loading, analyzing and

The V8 / V7 are a general purpose, mobile,
software controlled, diagnostic ultrasound system.
Their function is to acquire ultrasound data and to
display the data as 2D mode,Color Doppler mode,

The identified predicate devices within this submission is shown in the following table:


classification and measurements according
to the US ASE guidelines, and the users
can review and modify the results if
necessary.

saving medical studies, e.g. for the
purpose of creating reports. IMAGE-
COM is where basic measurements can
be performed and the entry point for
advanced analysis modules. Study
related routine measurements can be
imported, displayed, edited and
exported to accompanying reporting
systems.

Power Doppler (PD) mode, M mode, Pulsed Wave
(PW) Doppler mode, Continuous Wave (CW)
Doppler mode, Tissue Doppler Imaging (TDI)
mode, Tissue Doppler Wave (TDW) mode,
ElastoScan Mode, Combined modes, Multi-Image
mode(Dual, Quad), 3D/4D mode. The V8 / V7 also
give the operator the ability to measure anatomical
structures and offer analysis packages that provide
information that is used to make a K223387
Traditional 510(k) diagnosis by competent health
care professionals. The V8 / V7 have a real time
acoustic output display with two basic indices, a
mechanical index and a thermal index, which are
both automatically displayed.

PLAX LV, A4C, A4C Zoomed LV, A2C,
A2C zoomed LV, M-mode LA/Ao, M-
mode LV, CW Doppler MS, CW Doppler
MR, PW Doppler MV, CW Doppler AV,
CW Doppler AR, CW Doppler TR, CW
Doppler PV, CW Doppler PR, PW Doppler
RVOT, PW Doppler LVOT, DTI MV
annulus

PLAX LV, A4C, A4C Zoomed LV, A2C, A2C
zoomed LV, M-mode LA/Ao, M-mode LV, CW
Doppler MS, CW Doppler MR, PW Doppler MV,
CW Doppler AV, CW Doppler AR, CW Doppler
TR, CW Doppler PV, CW Doppler PR, PW
Doppler RVOT, PW Doppler LVOT, DTI MV
annulus

IVSd (2D), IVSs (2D), LVIDd (2D),
LVIDs (2D), LVPWd (2D), LVPWs (2D),
LA (2D), Ao (2D), LVESV / LVEDV
(4CH), LAV (4CH), LVESV / LVEDV
(4CH), LVESV / LVEDV (2CH), LAV
(2CH), LVESV / LVEDV (2CH), LA (m-
mode), Aorta (m-mode), IVSd (m-mode),
LVIDd (m-mode), LVPWd (m-mode),
IVSs (m-mode), LVIIDs (m-mode),
LVPWs (m-mode), MV Peak E Vel, MV
Peak A Vel, MV Decel Time, e' (med), a'
(med), s' (med), LVOT Vmax, LVOT VTI,
RVOT Vmax, RVOT VTI, PV AccT, TR
Vmax, TR VTI, AR Vmax / PR Vmax, AR
PHT / PR PHT, MV Vmax, MV VTI, MV,
PHT, MR Vmax, MR VTI, AV Vmax / PV
Vmax, AV VTI / PV VTI, RVOT (2D)

B-Mode
- Ao Asc diam, Ao Ann diam, Ao STJ
diam, Ao SV diam, IVSd, LA diam
systole, LVIDd, LVIDs, LVOT diam,
LVPWd, RVDd base (RVD1), RVDd
mid (RVD2), RVLd, RVOT diam lax,
RVOT diam prox, TV Ann diam ant-
post, IVSd-LVIDd-LVPWd (Same
line)
DOPPLER Mode
- MV A Vel, MV E Vel, MV E/A
Slope, (MV A Vel, MV E Vel, MV
Time), MV Dec. Slope, (MV Dec
Time, MV E Vel), LVOT VTI, AV
VTI, PV VTI, TR Vmax, LV E’(l), LV
A’(l), LV E’(s), LV A’(s), RV A’(l),
RV E’(l), RV S’(l)

(PLAX LV) - Interventricular septum diameter at the
diastole and systole phase, LV internal diameter at
the diastole and systole phase, LV posterior wall
diameter at the diastole and systole phase, Aorta
diameter and LA diameter, (A4C) - LA Volume, LV
Volume, (A4C Zoomed LV) - LV Volume, (A2C) -
LA Volume, LV Volume, (A2C zoomed LV) - LV
Volume, (M-mode LA/Ao) - LA diameter and Aorta
diameter, (M-mode / LV) - Interventricular septum
diameter at the diastole and systole phase, LV
internal diameter at the diastole and systole phase,
LV posterior wall diameter at the diastole and systole
phase, (CW Doppler MS) - Vmax, VTI, PHT, (CW
Doppler MR) - Vmax, VTI, (PW Doppler MV) - E,
A, DT, (CW Doppler AV) - Vmax, VTI, (CW
Doppler AR) - Vmax, PHT, (CW Doppler TR) -
Vmax, VTI, (CW Doppler PV) - Vmax, VTI, (CW
Doppler PR) - Vmax, EDV, (PW Doppler RVOT) -
Vmax, VTI, (PW Doppler LVOT) - Vmax, VTI,
(DTI MV annulus) - S’, E’, A’


Description
Decision
Subject Device (K230209)
Predicate Device (K213544)
Reference Device (K220975)

After the automated analysis is complete,
users can review and modify the results as
needed. The product is intended for use by,
or on the order of a licensed physician.

Semi-automated adult
echocardiography 2D and Doppler
measurements are generated using and
artificial intelligence (AI) detection
algorithm without user interaction.
After measurement is generated, the
user can edit (manually adjust the
caliper positions), accept, or reject the
measurements. The automation of
measurements is constrained to the
specific imaging mode (2D, Doppler)
as recommended by ASE guidelines.

After the analysis is complete, users can review and
modify the results as needed. The product is for use
by, or on the order of a licensed physician.


Detector Type
Solid-state GOS ceramic
Solid-state GOS ceramic

Maximum slices generated
per rotation (multislice
capability)

Couch
Type/max. moving
length/max. table
loading/max. horizontal
speed

standard couch:
205kg/160mm/s
300kg couch (option):
300kg/160mm/s


Intelligent Positioning
Device
(optional)

option for Intelligent
Positioning feature


Filtered back-projection (FBP);
ClearView;
ClearInfinity (Optional);

Filtered back-projection
(FBP) ;
ClearView


Dual Energy Imaging
（ Prism Imaging）
(Optional)


Classification Name
Computed tomography x-ray system


HealthCCSng Manufactured by Zebra Health


Predicate Device:
HealthCCSng
Manufactured by Zebra
Medical
(K210085)

Intended User
Interpreting physicians
Interpreting physicians
Same

Intended location
Medical facility
Medical facility
Same

Measurement scale
Agatston units
Agatston units
Same

Regulation number
21 CFR §892.1750
21 CFR §892.1750
Same

Similar, iCAC can
support a wider variety of
scans


Coronary artery
calcification
quantification
method

CAC detection category
(based on Agatston
score), exact Agatston
score

Optional to copy result to
clipboard, insert in report,
DICOM Secondary
Capture

Yes, Coronary Calcium
Detection Category and
exact Agatston score
4 categories (for detection
category):
(cid:120) 0
(cid:120) 1-99
(cid:120) 100-399
(cid:120) (cid:149)(cid:23)(cid:19)(cid:19)

Yes, Coronary Calcium
Detection Category
3 categories:
(cid:120) 0-99
(cid:120) 10-399
(cid:120) >400

Similar, iCAC provides a
more precise
representation of coronary
calcium burden by
separating the category 0
from the other categories.


Classification Name:
S
ystem, Image Processing, Rad

Regulatory Class:
C
lass II per 21 CFR 892.2050

Predicate Devices:
N
emaris Surgimap 2.0, K14166


Feature
Subject Device
Predicate Device (K203182)

EzraFlashisanimageprocessingsoft-
ware used for image enhancement of
MR images. It can be used to reduce
image noise for non-contrast 3T head
MRI scans.

SubtleMR is an image processing soft-
ware that can be used for image en-
hancement in MRI images. It can
be used to reduce image noise for
head, spine, neck, abdomen, pelvis,
prostate, breast and musculoskeletal
MRI, or increase image sharpness for
head MRI.

The software processes DICOM com-
pliant image data

None – enhanced images are viewed
on existing PACS workstations

Table 1: Subject and Predicate Device Comparison.


Image Enhancement
Algorithm Descrip-
tion

EzraFlashsoftwareimplementsanim-
age enhancement algorithm using a
convolutionalneuralnetwork-basedfil-
tering. Original images are enhanced
by running through a cascade of filter
banks, where thresholding and scal-
ing operations are applied. These fil-
ters result in a single machine learn-
ing model that reduces noise. The pa-
rameters of the filters were obtained
through an image-guided optimization
process.

SubtleMR software implements an im-
ageenhancementalgorithmusingcon-
volutional neural network based filter-
ing. Original images are enhanced
by running through a cascade of filter
banks, where thresholding and scaling
operations are applied. Separate neu-
ral network based filters are obtained
for noise reduction and sharpness in-
crease. The parameters of the fil-
ters were obtained through an image-
guided optimization process.

The software operates on DICOM files
on the file system, enhances the im-
ages, and stores the enhanced images
on the file system. The receipt of orig-
inal DICOM image files and delivery of
enhanced images as DICOM files de-
pends on other software systems. En-
hanced images co-exist with the origi-
nal images.

Image Enhancement EzraFlashsoftwareimplementsanim- SubtleMR software implements an im-


Classification Name:
Magnetic Resonance Diagnostic Device

Regulation Number:
90-LNH (Per 21 CFR § 892.1000)

Vantage Galan 3T, MRT-3020, V9.0 with AiCE Reconstruction
Processing Unit for MR


Vantage Galan 3T, MRT-3020, V8.0 with AiCE Reconstruction Processing Unit for MR

Marketed By
Canon Medical Systems USA, Inc.


Item
Subject Device:
Predicate Device:
Notes

Vantage Galan 3T, MRT-3020, V9.0
Vantage Galan 3T, MRT-3020, V8.0

with AiCE Reconstruction
with AiCE Reconstruction

Processing Unit for MR
Processing Unit for MR (K222387)

Operational Modes
Normal and 1st Operating Mode
Normal and 1st Operating Mode
Same

Allows screen access to 1st level
operating mode

4W/kg for whole body (1st
operating mode specified in IEC
60601-2-33:
2010+A1:2013+A2:2015)

Potential emergency
condition and means
provided for shutdown

Shutdown by Emergency Ramp
Down Unit for collision hazard for
ferromagnetic objects


Sonio
24 rue du Faubourg Saint Jacques,
75014, Paris France

Florian Akpakpa
Head of Regulatory Affairs and Quality Assurance
Sonio
Phone: +33 6 19 38 71 45
Email: florian.akpakpa@sonio.ai

Donna-Bea Tillman
Senior Consulting
Biologics Consulting
Phone: +1 (410) 531-6542 - Direct
Email: dtillman@biologicsconsulting.com


21 CFR 892.1550 - accessory to Ultrasonic Pulsed Doppler Imaging System
21 CFR 892.1560 - accessory to Ultrasonic Pulsed Echo Imaging System
21 CFR 892.2050 - Medical image management and processing system


Table 1: List of views that can be automatically de
View

tected by the software
Gestational Age of the fetus

Transthalamic or Cavum septum Pellucidum or Midline falx /
Transventricular or Choroid plexus

Profile or Nuchal translucency
T1 and T2/T3

detected and verified by the software are detailed in the tables 1, 2 and 3 below.


Transthalamic view
Transventricular view
Transcerebellar view

• Midline falx
• Cavum septum pellucidum
• Cerebellum

• Aorta
• Apex heart
• Ascending aorta
• Descending aorta
• Interatrial septum
• Interventricular septum
• Left atrium
• Left ventricle
• Pulmonary trunk
• Right atrium
• Right ventricle
• Superior vena cava

Table 2:List of anatomical structures that can be automatically detected by the software


For the transthalamic view, Sonio
Detect automatically evaluates the
following criteria:

• Presence of the cavum septum pellucidum
• Absence of the cerebellum
• Brain occupies more than half of the width of the
ultrasound image

For the transcerebellar view, Sonio
Detect automatically evaluates the
following criteria:

• Presence of the cerebellum
• Presence of the cavum septum pellucidum
• Brain occupies more than half of the width of the
ultrasound image

For the transventricular view, Sonio
Detect automatically evaluates the
following criteria:

For the 4 chambers view, Sonio
Detect automatically evaluates the
following criteria:

• Presence of the right ventricle
• Presence of the left ventricle
• Presence of the left atrium
• Presence of the right atrium
• Presence of the interventricular septum
• Presence of the interauricular septum
• Presence of the apex of the heart
• Presence of the descending aorta

For the 3 vessels and 3 vessels and
trachea views, Sonio Detect
automatically evaluates the following
criteria:

• Presence of the pulmonary trunk
• Presence of the ascending aorta
• Presence of the superior vena cava

For the LVOT view, Sonio Detect
automatically evaluates the following
criteria:

• Presence of the left ventricle
• Presence of the aorta
• Presence of the right ventricle
• Presence of the left atrium
• Presence of the apex of the heart

Table 3: List of quality criteria that can be automatically verified by the software


Items
Predicate device 1: SonoLyst feature in
Proposed device: Sonio Detect

SonoLyst feature embedded in the device
Voluson SWIFT

- Ultrasonic Pulsed Doppler Imaging System.
21 CFR 892.1550, 90-IYN;
-Ultrasonic Pulsed Echo Imaging System, 21
CFR 892.1560, 90-IYO;
-Diagnostic Ultrasound Transducer, 21 CFR
892.1570, 90-ITX

- Accessory to Ultrasonic Pulsed Doppler
Imaging System, 21 CFR 892.1550
- Accessory to Ultrasonic Pulsed Echo
Imaging System, 21 CFR 892.1560
- Medical image management and processing
system, 21 CFR 892.2050

- Images labeled with correct view
- Quality criteria are identified as “found”
when detected and “not found” when not
detected

- Images labeled with correct view
- Quality criteria are identified as “Verified”
when detected and “Not verified” when not
detected

Table 4: Comparison of technological characteristics


General purpose radiology evaluation and
specialized for OB/GYN

Qualified and trained healthcare professional
personnel in a professional prenatal
ultrasound (US) imaging environment (this
includes sonographers, MFMs, OB/GYN,
and Fetal surgeons)

Artificial Intelligence
Lecture of biometrics
Colorimetry for 3D and Doppler

Secure cloud-based and stand-alone software
compatible with ultrasound system from GE
Medical, Samsung and Canon

510(k) Premarket Notification Submission


Device Name
Manufacturer
510(k) Number
Regulation
Product


Proposed device - SpotLight /
SpotLight Duo (with DLIR option)

Predicate Device – SpotLight Duo
(K213465)

Fast scintillator array coupled to
photodiode array.
33 (WFOV) or 23 (EFOV) configurable
high resolution (HR) modules
comprising 192 detector rows X pitch
0.5mm (Z direction, measured at
scanner center).
10(WFOV)-20 (EFOV) configurable low
resolution (LR). EFOV includes 10
modules on each wing while WFOV
includes 10 modules on one wing.

A table comparing the key features of the subject and predicate device is provided below.


comprising 48 detector rows X pitch
2.0mm
Analog to digital conversion per channel
on the detection module.
1D antiscatter collimator.

Contactless transmission (capacitive
coupling).
Rate up to 6.25 GBit/sec

Rotation drive
Direct drive DC motor
Direct drive DC motor
Same

2 x MCS 2093 X ray tubes by Varex
Imaging Corp.
Single ended grounded rotating anode
Anode angle 13 degrees
1.0 MHU anode heat capacity
Grid controlled focal spot modulation in
X direction
Small and large focal spots
Max kVp: 140 kV
Max power: 72 KW

Motorized vertical and horizontal motion
Optional lateral motion
Cantilever carbon fiber patient cradle.

Modified FDK cone beam algorithm
adapted for dual tubes geometry.
Adaptive filter to reduce directional
noise in low level raw data (MBAF).
Noise reduction algorithms:
ASIR-CV or DLIR AI based image
reconstruction algorithm (DLIR only for
250mm FOV and EFOV configuration).

Modified FDK cone beam algorithm
adapted for dual tubes geometry.
Adaptive filter to reduce directional
noise in low level raw data (MBAF)
Iterative reconstruction algorithm (ASIR-
CV) to reduce image noise.
For WFOV configuration, adapted to
reconstruct high resolution images
according to detector configuration,

Both devices
include ASiR-CV,
while the
proposed device
includes DLIR as
an alternative
noise reduction
algorithm. DLIR
can be applied to
250mm FOV and
EFOV
configuration.


For WFOV configuration, adapted to
reconstruct high resolution images
according to detector configuration,
lower resolution images outside FOV
covered by high resolution detectors.
For extended FOV configuration,
adapted to reconstruct high resolution
images up to FOV250mm, lower
resolution images outside FOV250mm.

The SpotLight
with DLIR
demonstrates the
product
performance
claims (LCD,
Noise, High
contrast, Spatial
resolution, NPS,
accuracy and
uniformity), as
did the SpotLight
with ASiR CV.

Metal parts (mostly steel and aluminum)
Lead and tungsten for X-ray shielding
PCB, electronic components and
electronic cables components
Table top made of carbon fiber
reinforced resin
Covers made pf molded polymers and
reinforced resins
Oil in X-ray tubes cooling systems
Detector scintillators made of CdWO4
and Gadolinium Oxysulfide (GOS) used
in other legally marketed CT scanners

Walt supply 380 to 480 V 3 phase
Max power demand 115 kVA
Max X ray power (total for two tubes)
72kW
Laser alignment lights: gantry bore
external lasers. <0.1mW per laser
beam.
Three lead ECG trigger module,
powered by medical grade power supply
through the system PDU.

Wall supply 380 to 480 V 3 phase
Max power demand 115 kVA
Max X ray power (total for two tubes)
72kW
Laser alignment lights: gantry bore
external lasers. <0.1mW per laser beam
Three lead ECG trigger module,
powered by medical grade power
supply through the system PDU

Head& hands and knees support
Optional operator desk (the site may
use their own desk) carrying the display
monitor, keyboard, mouse, scan
operation unit and optional accessories
Barcode reader
External Console UPS


Proposed device - SpotLight /
SpotLight Duo (with DLIR option)

Predicate Device – SpotLight Duo
(K213465)

The SpotLight is provided with software
in three domains:
• Console software
• Image reconstruction software,
including the DLIR algorithm
• Embedded software

The SpotLight Duo is provided with
software in three domains:
• Console software
• Image reconstruction software
• Embedded software

Substantially the
same: the
software principal
block diagram is
the same, where
the DLIR option
is a software
sub-system
integrated in the
existing image
reconstruction
chain of
SpotLight
Duo/CardioGrap
he as alternative
to current the
ASIR-CV noise
reduction
algorithm.

0.16 sec (partial), 0.24 sec (full scan) –
FOV up to 250mm
0.24 sec (full scan) – HR imaging at
FOV above 250mm for asymmetric
detector

Max axial
coverage in a
single axial
scan

25cm - 250mm at high resolution, with
DLIR option
EFOV – up to 450mm with lower
resolution outside FOV 250mm, with
DLIR option
WFOV - high resolution images at
configurable FOV between 250mm and
450mm

25cm - 250mm at high resolution
EFOV – up to 450mm with lower
resolution outside FOV 250mm
WFOV - high resolution images at
configurable FOV between 250mm and
450mm

Same as
predicate device,
except DLIR as
optional
reconstruction for
25cm and EFOV
configurations

17.5 lp/cm cutoff at center
10.0 lp/cm cutoff at radius above
125mm (outside FOV 250mm) covered
by HR detectors
7.0 lp/cm cutoff at radius above 125mm
(outside FOV 250mm) covered by LR
detectors


The diameter marking is not intended to be a final output, but serves the purpose of

visualization and measurement. The original, unmarked series remains available in the PACS as

well. The preview image presents an unofficial and not final measurement, and the user is instructed

to review the full image and any other clinical information before making a clinical decision. The

image includes a disclaimer: “Not for diagnostic use. The measurement is unofficial, not final, and

The BriefCase-Quantification produces a preview image annotated with the maximum axial diameter


Reference Device
AI-Rad Companion
(Cardiovascular)
(K183268)

Subject Device
Aidoc
BriefCase-Quantifica
tion of the
Abdominal Aortic
Measurement
(M-AbdAo)

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.

AI-Rad Companion
(Cardiovascular) is
image processing
software that provides
quantitative and
qualitative analysis
from previously
acquired Computed
Tomography DICOM
images to support
radiologists and
physicians from
emergency medicine,
specialty care, urgent
care, and general
practice in the
evaluation and
assessment of
cardiovascular
diseases.
It provides the
following functionality:
• Segmentation and
volume measurement
of the heart
• Quantification of the
total calcium volume
in the coronary
arteries
• Segmentation of the
aorta
• Measurement of
maximum diameters
of the aorta at typical
landmarks

BriefCase-Quantificati
on is a radiological
image management
and processing
system software
indicated for use in
the analysis of CT
exams with contrast,
that include the
abdominal aorta, in
adults or transitional
adolescents aged 18
and older.
The device is
intended to assist
appropriately trained
medical specialists by
providing the user
with the maximum
abdominal aortic axial
diameter of cases that
includes the
abdominal aorta
(M-AbdAo).
BriefCase-Quantificati
on is indicated to
evaluate normal and
aneurysmal
abdominal aortas and
is not intended to
evaluate
post-operative aortas.
The
BriefCase-Quantificati
on results are not


• Threshold-based
highlighting of
enlarged diameters
The software has
been validated for
non-cardiac chest CT
data with filtered
backprojection
reconstruction from
Siemens
Healthineers, GE
Healthcare, Philips,
and Toshiba/Canon.
Additionally, the
calcium detection
feature has been
validated on
non-cardiac chest CT
data with iterative
reconstruction from
Siemens
Healthineers.
Only DICOM images
of adult patients are
considered to be valid
input.

intended to be used
on a stand-alone
basis for clinical
decision-making or
otherwise preclude
clinical assessment of
cases. These
measurements are
unofficial, are not final,
and are subject to
change after review
by a radiologist. For
final clinically
approved
measurements,
please refer to the
official radiology
report. Clinicians are
responsible for
viewing full images
per the standard of
care.

Thoracic radiologists,
general radiologists,
pulmonologists,
cardiologists, or other
similar physicians

Radiologists and
physicians from
emergency medicine,
specialty care, urgent
care, and general
practice

Appropriately trained
medical specialists


CT exams with
contrast that include
the abdominal aorta

Artificial intelligence
algorithm with
database of images.

- Viz RV/LV is hosted
on Viz.ai’s Backend
Server and analyzes
applicable CTPA
scans.
- The results are
exported in DICOM
format are sent to a
PACS destination for
review by physicians
to assist in the
assessment.

- AI-Rad Companion
(Cardiovascular) is a
software only image
post-processing
application.

-BriefCase-Quantificat
ion, is hosted on a
cloud server, analyzes
applicable CT images
that are acquired on
CT scanner that are
forwarded to
BriefCase-Quantificati
on.
- The results of the
analysis are exported
in DICOM format, and
are sent to a PACS
destination for review
by medical specialists,
to assist in the
measurement of the
abdominal aorta.


Table 3. Frequency Distribution of Gender


Table 4. Frequency Distribution of Manufacturer


Classification name
System, image processing, radiological

Predicate Device: Olea Sphere V3.0 (K152602)


Product name
MR Diffusion Perfusion Mismatch V1.0

Trend name
MR Diffusion Perfusion Mismatch V1.0

Reference Device: MR Diffusion Perfusion Mismatch V1.0 (K223502)


892.2050 – Picture
Archiving and
Communication System

an automatic contouring test to ensure the contours were accurate.


Limbus Contour is not
intended for use with
digital
mammography.

Intended User
Healthcare providers
Healthcare providers
Same

Limbus AI K230575 Limbus Contour 510(k) Submission


Image Fusion
No fusion support.
No fusion support.
Same

Stand-alone package
which operates on
Microsoft
Windows operating
system operating
system

Stand-alone package
which operates on
Microsoft
Windows operating
system and MacOS
operating system

Limbus
Contour v1.7
does not
support
MacOS
operating
system


Feature
Comparison:
• Operating
System
• Hardware
Requirements
• Etc.

Operating System
• Windows 10 /
Windows Server
2016 and Above
Hardware Requirements
• 2 GHz or faster multi-
core processor
• 8 GB of RAM
• For GPU versions, a
CUDA capable
NVIDIA GPU is
required

Operating System
• Windows 10 /
Windows Server
2016
• Mac OS 10.14
Hardware Requirements
• 2 GHz or faster multi-
core processor
• 4 GB of RAM
• For GPU versions, a
CUDA capable
NVIDIA GPU is
required

Limbus
Contour v1.7
does not
support
MacOS
operating
system.
RAM
requirements
are increased
to 8GB in
Limbus
Contour v1.7

Two different types of
verification testing were
conducted to verify the
software requirements:
Manual and Automated.
All tests passed,
demonstrating that the
software performance is
in accordance with the
stated software
requirements.
Validation testing of the
following functions of the
Limbus Contour
application demonstrated
that the software meets
user needs and intended
uses and to support


software meets user
substantial equivalence:

substantial equivalence:
Validation Test

available for
A_Aorta_I
B
ladder
Contour


Radformation, Inc. 510(k) Submission 2 of 25


335 Madison Avenue, 4th Floor
New York, NY 10017

Alan Nelson
Chief Science Officer, Radformation


AutoContour, AutoContouring, AutoContour Agent,
AutoContour Cloud Server

Medical image management and processing system


Table 3: Technological Characteristics
AutoContour Model RADAC V3 vs. AutoContour Model RADAC V2 (K220598)

CTorMRinputforcontouringor
registration/fusion.
PET/CTinputforregistration/fusiononly.
DICOMRTSTRUCTforoutput


Regionsand CT orM Rinputforcontouringof CT orM Rinputforcontouringof

Volumesof ana tomi calregions:HeadandNeck, ana tomi calregions:HeadandNeck,

interest(ROI) Tho rax, AbdomenandPelvis. Tho rax, AbdomenandPelvis.


Windowsbased.NETfront-end
applicationthatalsoservesasagent
UploadersupportingMicrosoftWindows
10(64-bit)andMicrosoftWindowsServer
2016.
Cloud-basedServerbasedautomatic
contouringapplicationcompatiblewith
Linux.
Windowspython-basedautomatic
contouringapplicationsupporting
MicrosoftWindows10(64-bit)and
MicrosoftWindowsServer2016.

Windowsbased.NETfront-endapplication
thatalsoservesasagentUploader
supportingMicrosoftWindows10(64-bit)
andMicrosoftWindowsServer2016.
Cloud-basedServerbasedautomatic
contouringapplicationcompatiblewith
Linux.
Windowspython-basedautomatic
contouringapplicationsupportingMicrosoft
Windows10(64-bit)andMicrosoft
WindowsServer2016.


For CT Structure models large, medium and small structures resulted in a mean

DSC of 0.88+/-0.06, 0.88+/-0.08, and 0.75+/-0.12 respectively. A full summary of the CT

volume structures (eg. gallbladder, duodenum) and 0.5 for Small structures (eg Cornea,


Table 4: CT Training Data Results for AutoContour Model RADAC V3

Cornea_L Small 0.5 N/A* N/A* N/A* N/A* N/A

Cornea_R Small 0.5 N/A* N/A* N/A* N/A* N/A


Retina_L Small 0.5 N/A* N/A* N/A* N/A* N/A*

Retina_R Small 0.5 N/A* N/A* N/A* N/A* N/A*

Rib_L Large 0.8 N/A* N/A* N/A* N/A* N/A*


Rib_R Large 0.8 N/A* N/A* N/A* N/A* N/A*


Table 5: CT External Clinical Dataset References


Pass # Average Average Bound95% Reviewer

CTStructure Size Criteria Testings DSC DSCStd. Confidence Average


glioblastoma and astrocytoma patients. Images were acquired on either a GE Signa

HDxT (3T) or Siemens Skyra (3T) scanner and

average of 274 training image sets and 92 testing image sets and were acquired from


Table 7: MR Initial Testing Dataset References


Table 8: MR Training Data Results for AutoContour Model RADAC V3


Table 9: MR External Clinical Dataset References

Nyholm, Tufve, Stina Svensson, Sebastian Andersson, Joakim Jonsson,

Maja Sohlin, Christian Gustafsson, Elisabeth Kjellén, et al. 2018. “MR

and CT Data with Multi Observer Delineations of Organs in the Pelvic

Area - Part of the Gold Atlas Project.” Medical Physics 12 (10): 3218–21.

accuracy of the models on image sets acquired that were unique to the training datasets.


For the Brain models, datasets acquired via data-use agreement from a clinical

partner were acquired containing 20 MR T1 Ax post (BRAVO) image scans acquired with

a GE MR750w scanner. Images had an average slice thickness of 1.6mm, In-plane

resolution between 0.94 mm, and acquisition parameters of TR=5.98ms, TE=96.8s. Data

for testing of the MR Pelvis structure models were acquired from a publicly available

Gold Atlas Data set which contained 19 images of patients with prostate or rectal cancer.

Images were acquired on a GE DISCOVERY MR750w (Ax T2 FRFSE) with in-plane

resolution of 0.9mm, Slice thickness of 2.5mm, TR=5.988s and TE=96.8ms.

DSC values were calculated between ground truth contour data and AutoContour

structures and rated on the same DSC passing criteria as was used for the training DSC

validation. All structures passed the minimum DSC criteria for small and medium

qualitative clinical appropriateness of AutoContour structures generated on these scans

was graded by clinical experts. Autocontour structures were graded on a scale from 1 to

5 where 5 refers to contour requiring no additional edits, and 1 refers to a score in which

full manual re-contour of the structure would be required. An average score >= 3 was

used to determine whether a structure model would ultimately be beneficial clinically. An

average rating of 4.4 was found across all MR structure models demonstrating that only

minor edits would be required in order to make the structure models acceptable for


Table 10: MR External Reviewer Results for AutoContour Model RADAC V3


Deep Learning Image Reconstruction
Deep Learning Image

DLIR uses a dedicated Deep Neural
Network (DNN) which is trained on the
CT scanner and therefore models the
propagation of noise through the
system to identify and remove the
noise

System statistics -
Noise modeling of
the data collection
imaging chain
(photon noise and
electronic noise)

Characterization of the photon
statistics as it propagates through the
preprocessing and calibration imaging
chain

System statistics –
Noise characteristics
of the reconstructed
images

DLIR uses a trained DNN which models
the scanned object using information
obtained from extensive phantom and
clinical data to identify the noise
characteristics and remove it

technological similarities and differences between the predicate device and the proposed device.


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

Using the same reference protocols
provided on the Revolution CT/Apex
platform systems for ASiR-V

Using the same reference protocols
provided on the Revolution EVO,
Revolution Maxima, Revolution
Ascend and Discovery CT750 HD
family systems for ASiR-V

510(k) Premarket Notification Submission - DLIR


Primary Predicate Device: SwiftMR – K220416 by AIRS Medical, Inc., Class II,

CFR 892.2050, classification with product code LLZ.

SwiftMR, is software used as a Medical Device (SaMD) consisting of a software

algorithm that enhances images taken by MRI scanners. The device only

processes DICOM images for the end User and is intended to be used by radiology

technologists in an imaging center, clinic, or hospital.


Item
Predicate Device
Subject Device
Differences

Software device that
operates on off-the-
shelf computer
hardware

Computer
PC Compatible
Same as predicate
No Difference

The software processes
DICOM-compliant
image data


Modalities
MRI
Same as predicate
No Difference

SwiftMR implements an
image enhancement
algorithm using
convolutional
neural network-based
filtering. Original
images are enhanced
by running through a
cascade of
filter banks, where
thresholding and
scaling operations are
applied. Neural
network-based filters
that perform noise
reduction are obtained.
The parameters of the
filters were obtained
through an image
guided optimization
process.
Sharpening filter is
additionally applied to
the deep learning
processed image.

SwiftMR implements an
image enhancement
algorithm using
convolutional
neural network-based
filtering. Original
images are enhanced
by running through a
cascade of
filter banks, where
thresholding and
scaling operations are
applied. Neural
network-based filters
that perform noise
reduction and/or
sharpening are
obtained. The
parameters of the filters
were obtained through
an image guided
optimization process.
Sharpening filter is
additionally applied to
the deep learning
processed image.

The deep learning model also
performs sharpening function.

3 General sequence
models
1 TOF sequence model

The deep learning model is applied
for all types of images (including but
not limited to T1, T2, T2*, PD,
FLAIR, DWI, MRA).

Brain, Spine, knee,
ankle, shoulder, and hip

The software operates
on DICOM files,
enhances the images,
and stores the
enhanced images on
PACS. Enhanced
images co-exist with the
original images.

The software operates
on DICOM files,
enhances the images,
and stores the
enhanced images on
PACS or on MR device.
Enhanced images co-
exist with the original
images.

The enhanced images can also be
sent to MR device not only to
PACS.


Radiological Computer Assisted Prioritization Software for
Lesions

Radiological Computer Aided Triage and Notification Software


Name of Device:
Lunit INSIGHT CXR Triage


Device Name
Lunit INSIGHT CXR Triage
qXR-PTX-PE

Radiological computer aided
triage and notification software

Radiological Computer-Assisted
Prioritization Software For Lesions

Manufacturer
Lunit Inc.
Qure.ai Technologies

Lunit INSIGHT CXR Triage is a
radiological computer-assisted
triage and notification software
that analyzes adult chest X-ray
images for the presence of pre-
specified suspected critical
findings (pleural effusions and/or
pneumothorax). Lunit INSIGHT
CXR Triage uses an artificial
intelligence algorithm to analyze
images for features suggestive of
critical findings and produce case-
level output available in the
PACS/workstation for worklist
prioritization or triage.
As a passive notification for
prioritization-only software tool
within standard of care workflow,
Lunight Insight CXR triage does
not send a proactive alert directly
to appropriately trained medical
specialists. Lunit INSIGHT CXR
Triage is not intended to direct
attention to specific portions of
an image. Its results are not
intended to be used on a
standalone basis for clinical
decision-making.

qXR-PTX-PE is a radiological
computer-assisted triage and
notification software that analyzes
adult chest X-ray images for the
presence of pre-specified suspected
critical findings (pleural effusion
and/or pneumothorax). qXR-PTX-PE
uses an artificial intelligence
algorithm to analyze images for
features suggestive of critical findings
and provides case-level output
available in the PACS/workstation for
worklist prioritization or triage.
As a passive notification for
prioritization-only software tool
within standard of care workflow,
qXR-PTX-PE does not send a proactive
alert directly to the appropriately
trained medical specialists. qXR-PTX-
PE is not intended to direct attention
to specific portions of an image or to
anomalies other than pleural effusion
and/or pneumothorax. Its results are
not intended to be used on a stand-
alone basis for clinical decision-
making

Appropriately trained medical
specialists who are qualified to
interpret chest radiographs.

Radiologists, clinicians, and other
appropriately trained medical
specialists qualified to read chest
radiographs

Table 1 Comparison between qXR-PTX-PE and the Predicate Device


Pleural effusion, Pneumothorax
on Chest/Lung Frontal Chest X-ray

Pneumothorax, Pleural effusion on
Chest/Lung Frontal Chest X-rays

Algorithm for pre-
specified critical findings
detection

AI algorithm designed to detect
pleural effusion and
pneumothorax in chest X-ray
images. Lunit INSIGHT CXR Triage
uses a vendor agnostic algorithm
compatible with DICOM chest X-
ray images

qXR-PTX-PE uses an AI algorithm to
detect pneumothorax and pleural
effusion on chest X-ray images. qXR-
PTX-PE uses a vendor agnostic
algorithm compatible with DICOM
chest X-ray images

Device output in case of
positive detection

When deployed on other
radiological imaging equipment,
Lunit INSIGHT CXR Triage
automatically runs after image
acquisition and prioritizes and
displays the analysis result
through the worklist interface of
PACS/workstation.
No markup on original image.
Secondary capture of the finding.
Upon image acquisition from
other radiological imaging
equipment (e.g. X-ray systems),
an on-device, technologist
notification indicating which cases
were flagged by Lunit INSIGHT
CXR Triage in PACS, is generated.
The on device notification is
contextual and does not provide
any diagnostic information. It is
not intended to inform any clinical
decision, prioritization, or action
to the technologist

When deployed on other radiological
imaging equipment, qXR-PTX-PE will
automatically run after image
acquisition to perform triage. It
displays the analysis result through
the worklist interface of
PACS/workstation.
No markup of the conditions will be
done on the original image.
Secondary capture of the device will
indicate the presence of findings
suspicious of pneumothorax or
pleural effusion.
Upon image acquisition from other
radiological imaging equipment (e.g.
X-ray systems) a passive notification
is generated.

Notification (i.e.,
recipient, timing and
means of notification)

Passive notification. Images with
suspicion of pleural effusion
and/or pneumothorax are flagged
in PACS/workstation.

Passive notification. Images with
suspicion of pneumothorax and/or
pleural effusion are flagged in
PACS/workstation/DICOM viewer.

Where generated results
(i.e., DICOM files) are
stored


Performance metrics of the predicate device and qXR-PTX-PE

Performance level –
Timing of notification

The average time taken for the
notification to travel from the
Lunit INSIGHT CXR Triage to the
point at which the result is
displayed in the destination
PACS/RIS/EPR worklist is 14.66
seconds.

The average time taken for the
notification to travel from qXR-PTX-
PE to the point at which the result is
displayed in the destination Picture
Archiving and Communication System
(PACS) or workstation/digital
radiographic processing system (ex.
digital radiography, digital X-ray
system etc.) is 10 seconds.

Performance level –
accuracy of classification


AUC (95% CI)
Sensitivity (95% CI), TP/P
Specificity (95% CI), TN/N

Table 2 Overall Results of Accuracy Testing of qXR-PTX-PE for Pneumothorax


Table 3 Overall Results of Accuracy Testing of qXR-PTX-PE for Pleural Effusion


3rd Floor,IdeastoImpact,PallodFarms 3
BehindVijaySales,Baner
Pune,India,Maharashtra411405

RoryA. Carrillo
RegulatoryConsultant
Cosm


The DeepTek CXR Analyzer V1.0 is
a computer-assisted detection
(CADe) software device that
analyzes chest radiograph studies
using machine learning techniques
to identify, categorize, and highlight
suspicious ROIs in one of the
following categories: Lungs, Pleura,
Cardiac, and Hardware. The device
is intended for use as a concurrent
reading aid for radiologists.
DeepTek CXR Analyzer V1.0 is
indicated for adults and transitional
adolescents (18 to <22 years old but
treated like adults) only.

Chest-CAD isacomputer-assisted
detection(CADe) softwaredevicethat
analyzeschestradiographystudies
usingmachinelearningtechniquesto
identify,categorize,andhighlight
suspiciousregionsof interest(ROI).
Any suspiciousROI identifiedby
Chest-CAD isassignedtooneof the
followingcategories:Cardiac,
Mediastinum/Hila,Lungs,
Pleura,Bones,SoftTissues,
Hardware,or Other.Thedeviceis
intendedfor use as aconcurrent
readingaidfor physicians.
Chest-CAD isindicatedfor adults
only.

Identifyandmarkregionsof
interest(ROIs) on chestradiographs

Identifyandmarkregionsof interest
(ROIs) onchestradiographs


Identified ROIsareassigned toone
of thefollowing categories: Lungs,
Pleura,Cardiac,or Hardware

Identified ROIsareassigned tooneof
thefollowing categories:Cardiac,
Mediastinum/Hila, Lungs, Pleura,
Bones,Soft Tissues,Hardware, or
Other

Device intendedfor useas areading
aidfor radiologists
interpreting chestradiographs

Device intendedfor useas areading
aidfor physiciansinterpreting chest
radiographs

Secureon-premise processingand
deliveryof chestradiographs

Securecloud-based processing and
deliveryof chestradiographs

ImageViewing
Image displayedonPACSsystem
Image displayedonPACSsystem


TheDeepTekCXR Analyzer demonstratedthefollowing results for detection ofROIs.


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

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


EFAI NEUROSUITE CT ICH ASSESSMENT SYSTEM

Radiological Computer-Assisted Triage And Notification
Software

Radiological Computer Aided Triage and Notification Software


EFAI ICHCT is a software
workflow tool designed to aid in
prioritizing the clinical
assessment of adult non-contrast
head CT cases with features
suggestive of acute intracranial
hemorrhage (ICH). EFAI
ICHCT analyzes cases using
deep learning algorithms to
identify suspected ICH findings.
It makes case-level output
available to a PACS/workstation
for worklist prioritization or
triage.
EFAI ICHCT is not intended to
direct attention to specific
portions of an image or to
anomalies other than acute ICH.
Its results are not intended to be
used on a stand-alone basis for
clinical decision-making nor is it
intended to rule out hemorrhage
or otherwise preclude clinical
assessment of CT studies.

CuraRad-ICH is a software
workflow tool designed to aid
in prioritizing the clinical
assessment of adult
non-contrast head CT cases
with features suggestive of
acute intracranial hemorrhage.
CuraRad-ICH analyzes cases
using deep learning algorithms
to identify suspected ICH
findings. It makes case-level
output available to a
PACS/workstation for worklist
prioritization or triage.
CuraRad-ICH is not intended to
direct attention to specific
portions of an image or to
anomalies other than acute ICH.
Its results are not intended to be
used on a stand-alone basis for
clinical decision-making nor is
it intended to rule out
hemorrhage or otherwise
preclude clinical assessment of
CT studies.

Table - Comparison with the Predicate Device.


Adult patients indicated for
non-contrast head CT

Acquires medical image data
from DICOM compliant
imaging devices and modalities.

Clinical condition
Acute Intracranial Hemorrhage
Acute Intracranial Hemorrhage

Independent of
standard of care
workflow

Output
Suspected ICH (Yes or No)
Suspected ICH (Yes or No)

Results Receiver
PACS / Workstation
PACS / Workstation


This predicate has not b
een subject to a design


Level P,24Campbell Street
Sydney, NSW2000
Australia


Annalise Enterprise CTBTriage (cid:177)OH

Radiologicalcomputer aided triage andnotificationsoftware
(21CFR892.2080)


Finding
Slice Thickness Range
Operating Point
Sensitivity % (Se)
Specificity % (Sp)


Training Dataset
Tuning Dataset
Validation Dataset

(n=390 samples)
(n=376 samples)
(n=163 samples)

race/ethnicity all reflect the broader U.S. population.


Validation Testing
(Mean Abs. Error ± Std.
Dev.)

Tumor Volume (n=157)
cubic centimeters (cc)
6.48 ± 12.67

Tumor-to-breast volume ratio (n=157)
%
0.56 ± 0.93

Tumor longest dimension (n=163)
centimeters (cm)
1.48 ± 1.46

Tumor-to-nipple distance (n=161)
centimeters (cm)
1.00 ± 1.03

Tumor-to-skin distance (n=163)
centimeters (cm)
0.63 ± 0.60

Tumor-to-chest distance (n=163)
centimeters (cm)
0.94 ± 1.34

Tumor center of mass (n=157)
centimeters (cm)
0.735 ± 1.26

Performance data for the automated measurements is summarized below:


Tumor segmentation (n=157)
Volume Dice
0.676 ± 0.289

Results of Dice and surface Dice are summarized below:


Manufacturer
Merge CAD Inc.
SimBioSys Inc.

Medical image management and
processing system

Device Common Name
Image Processing System
Image Processing System

- Extract dynamic contrast
enhanced MRI sequence from
MRI images for the 3D display
and visualization of the anatomy
of patient’s breast

A table comparing the key features of the subject and predicate devices is provided below:


CADstream is intended to be
used in the visualization,
analysis, and reporting of
magnetic resonance imaging
(MRI) studies. CADstream
supports evaluation of dynamic
MR data acquired during
contrast administration.
CADstream performs other user
selected processing functions
(such as image registration,
subtractions, measurements, 3D
renderings, and reformats).
CADstream also includes user-
configurable features for
reporting on findings in breast or
general MRI studies.
Additionally, CADstream assists
users in planning MRM guided
interventional procedures.
When interpreted by a skilled
physician, this device provides
information that may be used for
screening, diagnosis, and
interventional planning. Patient
management decisions should
not be made based solely on
theresults of CADstream.
CADstream may also be used as
an image viewer of multi-
modality, digital images,
including ultrasound and
mammography. CADstream is
not intended for primary
interpretation of digital
mammography images.

TumorSight Viz is intended to
be used in the visualization and
analysis of breast magnetic
resonance imaging (MRI)
studies for patients with biopsy
proven early-stage or locally
advanced breast cancer.
TumorSight Viz supports
evaluation of dynamic MR data
acquired from breast
studiesduring contrast
administration. TumorSight Viz
performs processing functions
(such as image registration,
subtractions, measurements, 3D
renderings, and reformats).
TumorSight Viz also includes
user-configurable features for
visualizing and analyzing
findings in breast MRI studies.
Patient management decisions
should not be made based solely
on the results of TumorSight
Viz.

Graphic and text results of
breast anatomy are accessed via
a device with internet
connectivity

"-non-invasive software package
-DICOM compatible"


Clinician review and assessment
of analysis prior to use in
planning MRI guided
interventional procedures.

Clinician review and assessment
of analysis prior to use in pre-
operative planning.

No - Segmentation is not
editable, but surgical margins
are editable

Interactive rotation of 3D
volume rendering


Automated radiological image processing software

Medical image management and processing system


EFAI Intelligent Cardiothoracic Ratio (iCTR) Assessment System


Device type
Quantifying software
Quantifying software

Manufacturer
Ever Fortune.AI Co., Ltd.
Qure.ai Technologies

EFAI Intelligent Cardiothoracic
Ratio Assessment System (or
iCTR) is a software for use by
hospitals and clinics to
automatically assess the
cardiothoracic ratio (CTR) of a
chest X-ray image from the X-
ray imager subject. The iCTR is
designed to measure the
maximal transverse diameter
of heart and maximal inner
transverse diameter of
thoracic cavity and calculate
the CTR of a chest X-ray image
in posterior-anterior (PA)
chest view using an artificial
intelligence algorithm.
Intended users of the
software are aimed to the
physicians or other licensed
practitioners in the healthcare
institutions, such as clinics,
hospitals, healthcare facilities,
residential care facilities and
long-term care services. The
system is suitable for adults
between 20 - 80 years of age.
Its results are not intended to
be used on a stand-alone
basis for clinical decision
making or otherwise preclude
clinical assessment of
cardiothoracic ratio (CTR)
cases.

qXR-CTR is a deep-learning based
software for use by hospitals and
clinics for automated assessment of
the CTR on chest X-ray (CXRs) scans.
qXR-CTR is designed to measure the
ratio of the maximal transverse
diameter of the heart (CD) and the
maximal inner transverse diameter
(TD) of the thoracic cavity and
calculate the CTR value on posterior-
anterior view chest view using an
artificial intelligence algorithm.
The intended users of this device are
physicians or licensed practitioners in
healthcare institutions, such as clinics,
hospitals, residential care facilities,
long-term care services, and
healthcare facilities.
The system is suitable for adults ≥ 22
years of age.
The device is used to aid the intended
users and results are not intended to
be used on a stand-alone basis for
clinical decision making or otherwise
preclude clinical assessment of CTR
cases.

Algorithm for
measurement of target
structures

Post-anterior (PA) view chest
X-ray image

Pdf report is generated, DICOM
Secondary capture is returned to the
PACS

Report will be output in the DICOM
and JSON file format which is
structured with following information


following information and
function tool: 1) CTR 2)
adjustable annotation line
(maximal inner border
diameter of thoracic cavity
and maximal diameter of
heart) 3) the trajectory of CTR
(including chest X-ray)

and function tool: 1) CTR 2) annotation
line (maximal inner border diameter of
thoracic cavity and maximal diameter
of heart)

Physicians or other licensed
practitioners in the healthcare
institutions

Target Population
20-80 years
≥ 22 years

Healthcare institutions
(Clinics, hospitals, healthcare
facilities, residential care
facilities and long-term care
services)

Software device that
operates on off the shelf
hardware

Software devices uses
software algorithms for
image

Saved in JSON and DICOM file
format text for DICOM and
DICOM file format

The text report is generated with the CXR
scan on a Structured Report (SR) format for
PACS-based mode. For the Web-based
mode, the user can download a PDF
version of the report with the same
information

Ubuntu 18.04 (Web browser:
chrome 88.0.4324.182 or
above)

Performance metrics of the predicate device and qXR-CTR

RMSE:
Cardiac Diameter = 8.81 mm
Thoracic Diameter = 14.4 mm
Mean Absolute Error:
NR

RMSE:
Cardiac Diameter = 7.55 mm
Thoracic Diameter = 5.43 mm
Mean Absolute Error:
5.66 (5.0) mm
4.04 (3.63) mm


Measurement
Root Mean Squared Error (95%
Mean Absolute Error (SD) in mm

Table 2 Overall Results of Accuracy Testing of qXR-CTR


Current Predicate
Device
syngo.CT Lung CAD
(VD20) (K203258)

Type of Change and
Impact to Safety & Ef-
fectiveness

Solid and subsolid pul-
monary nodules in
screening and diagnos-
tic chest CT acquisi-
tions

Solid and subsolid
pulmonary nodules in
screening and diag-
nostic chest CT acqui-
sitions

syngo.CT Lung CAD
software device is a
Computer-Aided De-
tection (CAD) tool de-
signed to assist radiol-
ogists in the detection
of pulmonary nodules
during review of multi-
detector computed to-
mography (MDCT) ex-
aminations of the
thorax (chest). The
software is an adjunc-
tive tool to alert the ra-
diologist to regions of
interest (ROI) that may
otherwise be
overlooked.

syngo.CT Lung CAD
software device is a
Computer-Aided De-
tection (CAD) tool
designed to assist ra-
diologists in the de-
tection of pulmonary
nodules during review
of multi-detector com-
puted tomography
(MDCT) examina-
tions of the thorax
(chest). The software
is an adjunctive tool
to alert the radiologist
to regions of interest
(ROI) that may other-
wise be overlooked.


Slice Thickness
Up to and including
2.5 mm with
1.25 mm preferred

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

Slice Overlap
0–50%
Note: Reconstruction
overlap is allowed,
but gaps are not per-
mitted

Kernel
Consistent with tho-
racic 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 .

Kernel
Consistent with tho-
racic CT protocols
and in line with pa-
tient 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.


Preprocessing
Standardization of
the input images and
lung segmentation

(a) isotropic volume
resampling and image
standardization.
(b) lung segmentation is ac-
complished using a CNN.
Initially a coarse estimation
of the lung is performed us-
ing a V-net process. Using
two predefined bounding
boxes left and right lungs are

(a). Isotropic volume resampling and
image standardization
(b) lung segmentation is accomplished by
computing masks of right upper (RUL),
right middle (RML), right lower (RLL),
left upper (LUL), and left lower (LLL)
lobes for a given CT dataset of chest. The
segmentation task is designed as a voxel
level classification task for predicting it
as one of the lung lobe labels or the

Table 1 Summary of Differences between the Subject Device and the Predicate Device


initialized. Another V-net is
used to segment left and
right lungs. This is followed
by up-sampling to the origi-
nal image resolution.

background label. The output is a proba-
bility map indicating how likely each
voxel belongs to each lung lobe. The al-
gorithm is trained on lung CT data in-
cluding comorbidities for robustness.

Candidate Genera-
tion
The partitioned vol-
ume is processed us-
ing a CNN and fil-
tered to yield a list of
candidates for each
subvolume.

(a) isotropic volume is parti-
tioned into subvolumes
(b) Each subvolume is fed to
a CNN to compute features
(“response volume”). Filter-
ing and non-maximum sup-
pression yield a list of candi-
dates for each subvolume
(c) candidates above a cer-
tain threshold score are
passed to the next step.

(a) isotropic volume is partitioned into
subvolumes
(b) Each subvolume is fed to a CNN to
compute features (“response volume”).
Filtering and non-maximum suppression
yield a list of candidates for each sub-
volume
(c) candidates above a certain threshold
score are passed to the next step.

Candidate Classifi-
cation utilizes a
CNN-based classifier
to process each can-
didate and estimate
the
likelihood of its type
as either “nodule” or
“non-nodule”.

CNN is used for feature
computation for each candi-
date.
(a) The input image patch is
firstly processed by batch
normalization.
(b) Three group blocks of
operations are computed. In
each block, a convolution,
followed by a batch
normalization and then a
ReLU activation are used.
Residual blocks and
concatenation operations are
used for robustness
(c) Semantic features from
image features are computed
using two fully connected
layers.
(d) A soft-max function, ap-
plied to each candidate, as-
signs 2 values corresponding
to the probability of being a
nodule or being a false posi-
tive.

CNN is used for feature computation for
each candidate.
(a) The input image patch is firstly pro-
cessed by batch normalization.
(b) Three group blocks of operations are
computed. In each block, a convolution,
followed by a batch normalization and
then a ReLU activation are used.
Residual blocks and concatenation
operations are used for robustness.
(c) Semantic features from image fea-
tures are computed using two fully con-
nected layers.
(d) A soft-max function, applied to each
candidate, assigns 2 values correspond-
ing to the probability of being a nodule or
being a false positive.
(e) A weighted-sum of the scores from
this phase and the results of the prior step
is computed. Candidates above a certain
threshold score are labeled as nodule can-
didates.


(e) A weighted-sum of the
scores from this phase and
the results of the prior step
is computed. Candidates
above a certain threshold
score are labeled as nodule
candidates.

This step includes the appli-
cation of two cascaded fil-
ters. These two filters aim at
removing false positives
originating from the colon
and calcified protrusions (for
example, areas where the
sternum meets the
manubrium, spine
malformations, osteophytes,
and so on).
The first filter is a CNN-
based classifier that has a
similar structure to that of
the classifier used in the
candidate classification step.
The second filter uses three
orthogonal slices at the can-
didate location as input to
three CNN-based classifiers
(one per slice). The results
from the three classifiers are
then combined by a max-
voting mechanism. Any can-
didate deemed a false posi-
tive by either filter is thus re-
moved.

This step includes the application of
three cascaded filters. The first one aims
at removing false positives originating
from the colon and the second one aims
at removing false positives from calci-
fied protrusions (for example, areas
where the sternum meets the manubrium,
spine malformations, and osteophytes,
and so on).The third one aims at assign-
ing nodule type labels to all findings
(solid, nonsolid, fully calcified).
The first filter is a CNN-based classifier
that has a similar structure to that of the
classifier in step 3.
The second filter uses three orthogonal
slices at the candidate location as input to
three CNN-based classifiers (one per
slice). The results from the three classifi-
ers is then combined by a max-voting
mechanism. Any candidate deemed a
false positive by either filter is thus re-
moved.
The third filter has two parts. The first
part is a CNN-based classifier that has a
similar structure to that of the classifier
used in the candidate classification step.,
which classifies findings into solid and


non-solid groups. The second filter is
constructed by using a set of hand-crafted
rules based on features extracted from
Raycast methods. The second filter fur-
ther labels some of the solid nodules as
“fully calcified”. User could choose to
display findings in “solid only” mode so
that non-solid nodules and fully calcified
nodules are excluded.

The location information of
all the nodule candidates are
collected into a final candi-
date list passed to the Host-
ing Application

The location information of all the nod-
ule candidates are collected into a final
candidate list passed to Hosting Applica-
tion.


Intraparenchymal Hemorrhage (IPH)
96.61% (88.29-99.59)

Intraventricular Hemorrhage (IVH)
100.00% (59.04-100.00)

Subarachnoid Hemorrhage (SAH)
35.71% (12.76-64.86)


Subdural Hemorrhage (SDH)
66.67% (40.99-86.66)


Gender
Sensitivity (95% CI)
Specificity (95% CI)

Table 1. Summary of the performance metrics for the subsets of scans stratified by ICH Subtype.


Age
Sensitivity (95% CI)
Specificity (95% CI)

Table 2. Summary of the performance metrics for the subsets of scans stratified by gender.


Slice Thickness
Sensitivity (95% CI)
Specificity (95% CI)

Table 3. Summary of the performance metrics for the subsets of scans stratified by age group.


Clinical Site
Sensitivity (95% CI)
Specificity (95% CI)

Table 4. Summary of the performance metrics for the subsets of scans stratified by slice thickness


Minimal Volume Threshold (ml)
Sensitivity above Threshold (95% CI)

Table 5. Summary of the performance metrics for the subsets of scans stratified by referring hospital


Aquilion Serve (TSX-
307A/1) V1.2 with
AiCE-i

JAK:
System, X-ray,
Tomography,
Computed


Device Name,
Aquilion Serve
Aquilion Serve

Model Number
(TSX-307A/1) V1.3
(TSX-307A/1) V1.2 with AiCE-i

Conventional scan (Axial Scan)
Volume, Dynamic volume scan
Dynamic scan
Helical scan

Conventional scan (Axial Scan)
Volume, Dynamic volume scan
Helical scan

-Dynamic scan is a
continuous scanning
mode for a target
region.

[Volume, Dynamic volume,
Dynamic scan]
(80-row scan*1: 0.5 mm)
40-row scan: 0.5 mm
8-row scan: 0.5 mm*2
4-row scan: 0.5, 1 and 2 mm*2

[Volume, Dynamic volume
scan]
(80-row scan*1: 0.5 mm)
40-row scan: 0.5 mm

*1: Option
Dynamic volume CT
upgrade kit (CGS-
55A) is mandatory.
*2: For Dynamic scan
only

technological characteristics between the subject and the predicate device is included below.


Reconstruction field
up to 800 mm for
data acquired with
an FOV of 500 mm,
becomes available.


Level P, 24 Campbell Street
Sydney, NSW 2000
Australia


Device Name
Annalise Enterprise CTB Triage Trauma

Radiological computer aided triage and notification software
(21CFR892.2080)


Finding
Slice Thickness Range
Operating Point
Sensitivity % (Se)
Specificity % (Sp)

results of the study are summarized in the table below.


Document name: 510(k) Summary CEPHX - Cephalometric Analysis Software


Parameter
CEPHX
WebCeph K220903
Comparison

CEPHX - Cephalometric Analysis
Software is a software indicated
for use by dentists who provide
orthodontic treatment for image
analysis, simulation, profilogram,
VTO (Visual Treatment Objective)

WebCeph is a software
indicated for use by dentists
who provide orthodontic
treatment for image analysis,
simulation, profilogram,
VTO/STO (Visual Treatment

Different -
Has no safety or
security impact on
the product. The
STO and age


and patient consultation. Results
produced by the software’s
diagnostic, treatment planning
and simulation tools are
dependent on the interpretation
of trained and licensed
practitioners or dentists. The
device is only for use on patients
14 years old and above.

Objective) and patient
consultation. Results produced
by the software’s diagnostic,
treatment planning and
simulation tools are dependent
on the interpretation of trained
and licensed practitioners or
dentists. The device is only for
the use of patients above 7
years old.

User Interface
Mouse, Keyboard
Mouse, Keyboard
Same

CPU processor type
x64-based processor or higher
x64-based processor or higher
Same

Modality Support
X-ray or CT
X-ray or CT
Same

Component
Client (Internet Browser)
Client (Internet Browser)
Same

Different -
Has no safety,
security or
performance
impact on the
product. The
difference is
explained and
detailed below.

Full, side by side, thumbnail,
Zoom in / out, template.

Brightness, contrast, flip, rotate,
annotation, cephalometric
tracing.