GE Medical Systems Ultrasound and Primary care Diagnostics, LLC
9900 Innovation Dr
Wauwatosa, WI 53226

GE Ultrasound Korea, Ltd.
9, Sunhwan-ro 214 beon-gil, Jungwon-gu,
Seongnam-si, Gyeonggi-do, 13204 Republic of Korea

Bryan Behn
Regulatory Affairs Director
GE HealthCare
T:(262)247-5502

Qingmeng Chen
Regulatory Affairs Program Manager
GE HealthCare
T: +86-18180590723

Device Trade Name:
LOGIQ E10s, LOGIQ Fortis

Common / Usual Name:
Diagnostic Ultrasound System

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

K211524 LOGIQ E10s, LOGIQ Fortis Diagnostic Ultrasound System

K211488 LOGIQ E10 Diagnostic Ultrasound System
K202035 Vscan Air
K181685 Vivid E80/ Vivid E90/ Vivid E95 R3
K200743 Vivid E80/ Vivid E90/ Vivid E95 R4
K170445 LOGIQ S8
K202233 Venue Go

In accordance with 21 CFR 807.92 the following summary of information is provided:


Summary test statistics or other test results
including acceptance criteria or other
information supporting the appropriateness
of the characterized performance

• For Longitudinal model the accuracy of measurement is expected to
be higher than 80% and for the Transverse model it is expected to be
higher than 70%
• The number of individual patients’ images were collected from: 30
Patients.
• The number of samples is 60 with 30 Longitudinal views and 30
Transverse view

Information about clinical subgroups and
confounders present in the dataset

• Gender: Male & Female
• Age: range 26-81 yrs old
• Ethnicity/Country; USA (58%) and Japan (42%)

Information about equipment and protocols
used to collect images

The datasetused for final verification was collected prospectively on 30
demographically
diverse patients at 2 sites: Japan and the USA exclusively with the system
under consideration
GE Logiq Fortis after the Auto Renal Measure Assistant function was
developed using C1-6-D
probe in Abdomen application;

Information about how the reference
standard was derived from the dataset (i.e.
the “truthing” process)

• The Validation set of 30 patient ( 60 images ) were loaded onto the
LOGIQ validation system.
• 2 Readers (certified sonographer/Clinician) to ground truth the
measurements: Length on the Longitudinal views and the Height and
Width Measurements on the Transverse views.
• Board Certified Nephrologist arbitrated the ground truth between the
above two readers to establish the reference standard for the dataset
• Summary of Results:

• Auto Renal Measure Assistant : Al Summary of Testing


• The overall model success rate of the Aorta, Kidney, Liver, GB, and
Pancreas view suggestion is expected to be 80% or higher.
• The number of individual patients’ images were collected from: 50+
patients
• The number of samples, if different from above, and the relationship
between the two: 1100+ images

• Gender: Male & Female
• Age: Wide range, but specific age not collected
• Ethnicity/Country; USA (77%) and Australia (23%)

Mix of data from across three different probe models and two different
Console variants. The data collection protocol was standardized across all
data collection sites.

• Auto Abdominal Color Assistant : Al Summary of Testing


• The overall model success rate of the Abdomen, Air, Breast, Carotid,
Leg, MSK, Scrotal, Thyroid and Carotid/Thyroid (Mixed) view
suggestion is expected to be 80% or higher.
• The number of individual patients’ images were collected from: 110+
patients
• The number of samples, if different from above, and the relationship
between the two: 2600+ images

• Gender: Male & Female
• Age: Wide range, but specific age not collected
• Ethnicity/Country; USA (41.2%) , Austria (3.8%), Australia (1.1%),
Japan (41.3%), Italy (0.7%) and Greece (12%).

Mix of data from across five different probe models and three different
Console variants. The data collection protocol was standardized across all
data collection sites.

• Before the process of data annotation, all information displayed on
the device is removed and performed on information extracted purely
from Ultrasound B-mode images.
• Readers (certified sonographer/Clinician) to ground truth the
“anatomy” visible in static B-Mode image. (Before running AI)
• Ran AI and created confusion matrix of ground truth vs AI
predictions.
• Calculated the accuracies of the algorithm against each class.

Description of how independence of test
data from training data was ensured

The exams used for test/training validation purpose are separated from the
ones used during training process and there is no overlap between the two.

• Auto Preset Assistant : Al Summary of Testing


Digital Imaging and Communications in Medicine
(DICOM) Set; PS 3.1 - 3.20 2022d

Medical Device Software –Software Life Cycle
Processes; 62304:2006 (1st Edition)/A1:2016


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

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

Medical devices - Symbols to be used with medical
device labels, labelling, and information to be
supplied - Part 1: General requirements
ISO 15223-1 Fourth edition 2021-07


Regulatory Classification
Unclassified (pre-a


Classification Name
Device, Sensing,OpticalContour

Regulatory Classification
Unclassified (pre-amendment)

Neurological andPhysicalMedicine Devices(OHT5)
Neuromodulation andPhysicalMedicine Devices(DHT5B)


PredicateName
Quantec Spinal Measurement System


Submitted Name:
MeVis Medical Solutions AG

Establishment Name:
MeVis Medical Solutions AG

Classification Name:
Computed tomography x-ray system


510(k) Number
Primary Predicate Device
Product Code


510(k) Number
Secondary Predicate Device
Product Code


510(k) Number
Reference Device
Product Code


MeVis Liver Suite is an image analysis software and intended to be used for visualization of
hepatic imaging studies derived from CT and MR scanning devices (image source: single- and
multiframe DICOM).
MeVis Liver Suite supports physicians in their workflow for evaluating the liver, related vascular
anatomy, and the volume of liver and liver vascular territories for treatment planning,
preoperative evaluation of surgery strategies, and post-procedure or therapy follow-up
assessment of the hepatic system and related vascular structures.
MeVis Liver Suite supports image analysis for multiphase contrast enhanced CT, dynamic
contrast enhanced MRI, and MRCP. MeVis Liver Suite only allows import of homogenous
image series (i.e., image and pixel size must be constant within each series) and with
orthogonal image matrix (without a gantry tilt). Images with a resolution of > 5mm slice spacing
are not suitable for image analysis with MeVis Liver Suite.
MeVis Liver Suite can be used for manual segmentation, user-defined manual labeling, and 3D
visualization of:
• abdominal organs (i.e., liver, stomach, duodenum, spleen, kidney, gallbladder, and
pancreas).
• liver related vascular structures (i.e., bile ducts, hepatic artery, hepatic vein, portal
vein, and inferior vena cava)
• lesions inside and adjacent to the liver
The tools for manual segmentation and 3D visualization are applicable for CT and MR imaging
studies.
In addition to the manual segmentation tools, MeVis Liver Suite provides AI based semi-
automatic pre-segmentation tools for liver, hepatic artery, hepatic vein, and portal vein
restricted to CT scans of potential living liver donors with healthy livers and intended for:
• Liver: contrast enhanced late-venous and venous phase
• Hepatic vein: contrast enhanced late-venous and venous phase
• Portal vein: contrast enhanced late-venous and venous phase
• Hepatic artery: contrast enhanced arterial phase
Using MeVis Liver Suite, users can evaluate the segmented objects by exploring, calculating,
and manually correcting:
• the volume of the segmented abdominal organs (see above)
• the volume of the segmented lesions inside and adjacent to the liver
• the volume of the manually defined parts of the liver
by defining separation planes (“separation proposals”)
o
from vascular territories that are derived from the user-defined labeling of the
o
liver related vascular structures
• 3D visualizations of user-defined (vascular) tumor margins (coloring of area - based
on user-defined margin sizes/distances - between the edges of user-defined lesions in
relation to the edges of user-defined vascular structures of the liver)
• based on user provided values, calculation of liver volume to body weight ratios (i.e.,
estimated weight for remnant or graft, body surface area, graft to recipient body
weight ratio, graft to SLV ratio, remnant to body weight ratio).
Using a manual spatial registration of the images, the segmented objects (created on different
CT phases and MRI sequences) can be visualized together.
The information created with MeVis Liver Suite is intended to be used only in addition to the
original images, clinical data, and the real anatomical and clinical situation. Physicians make all
final patient management, assessment, and treatment decisions.

Table 1 Comparison table for indications for use.


MeVis Liver Suite is not intended for the anatomical systems integumentary, skeletal,
muscular, lymphatic, respiratory, nervous, reproductive, and cardiovascular (excluding
hepatic).
MeVis Liver Suite does not support the following application areas: real time viewing,
diagnostic review, image manipulation, optimization, virtual colonoscopy, and automatic lesion
detection.
MeVis Liver Suite does not utilize high-resolution displays or display drivers and should not be
used as a replacement for a PACS workstation.
Intended Patient Population:
The intended patients for MeVis Liver Suite are oncologic patients or hepatic donor/transplant
patients. CT or MR imaging with contrast agents need to be possible for analysis of vascular
structures inside the liver. The age group for the intended patient population is 18 or older.
In addition, the semi-automatic pre-segmentation tools are restricted to CT scans of potential
living liver donors with healthy livers.
Intended Part of the Body:
Images supported for the intended medical indication include the abdominal body part with the
liver and related vascular structures (bile ducts, hepatic artery, hepatic vein, portal vein, inferior
vena cava).
Intended User Profile:
The intended users are radiologists and surgeons.
Intended Use Environment:
The use environment is expected to be in an office environment under typical office conditions
in hospitals or medical practices.
Operation Principle:
The software acts as modality for display of CT or MR imaging data, and for visualization of the
image analysis results on a computer system. MeVis Liver Suite does not come into direct
contact or indirect contact with patients or other medical devices.

Primary Predicate
device:
Siemens syngo.
CT Liver Analysis
(K133643)

syngo.CT Liver Analysis is an image analysis software for CT volume data sets. It analyses the
liver and its intrahepatic vessel structures to identify the vascular territories of sub-vessel
systems in the liver. These regions can be evaluated by exploring the volume of the liver and
its vascular territories.
Using syngo.CT Liver Analysis, you can evaluate the liver volume and examine the vessels of
the liver. The following evaluation tools are provided:
• Computation and manual correction of liver volumes
• Computation and manual correction of tumor volumes and extent
• Computation and manual correction of liver vessel tree structure
• Computation of territories based on vessel branches
• Tumor position in relation to vessels (i.e. 3D visualization of liver, tumor and vessels)
• Manual definition of separation plane proposals
• Computation of volume of liver parts
• Combination of information from different CT and MR phase volumes
syngo.CT Liver Analysis facilitates reporting by using of appropriate reporting tools, for
example, volume statistics and key image creation. You can use syngo.CT Liver Analysis to
create a DICOM Structured Report.

Secondary
Predicate device:
Radiomics App
v1.0 (K173420)

Microsoft Radiomics Advanced Image Contouring v1.0 (Radiomics App) is a software-only
medical device intended for use by trained radiation oncologists, dosimetrists and physicists to
derive optimal organ and tumor contours for input to radiation treatment planning. Supported
image modalities are Computed Tomography and Magnetic Resonance. Radiomics App
assists in the following scenarios:
Load, save and display of medical images and contours for treatment evaluation and treatment
planning.


OS
Windows 10 (64-bit)
syngo.via platform
Microsoft Windows

DICOM images are manually
selected and imported into
MeVis Liver Suite.

The user has to decide which
anatomical structures need to
be evaluated and on which CT
series the necessary
structures have to be
segmented.

The user manually loads
images into the software.

• Liver,
• Stomach,
• Duodenum,
• Spleen,
• Kidney,
• Gallbladder, and
• Pancreas
Manual segmentation, user-
defined manual labelling, and
3D visualization.
For segmenting the liver, the
user can trigger a semi-
automatic pre-segmentation
that creates contours of the
liver. The created contours
can be manually corrected with
interactive contouring tools.

• Liver
Automatic segmentation of
liver, interactive manual
correction of the resulting
contour.

• No restrictions
Assisted and automatic
contouring modes. Creation,
transformation, and
modification of contours.

Segmentation
of liver related
vascular
structures

Manual segmentation, user-
defined manual labelling, and
3D visualization of liver related
vascular structures (bile ducts,
hepatic artery, hepatic vein,
portal vein, inferior vena cava).
Semi-automatic pre-
segmentation for liver
related vascular structures is
available: the user can click in
a user-identified vessel and
the software detects the
connected vessels. The user
can manually correct, add, or
delete detected vessels
interactively

Segmentation of tubular
structures using a semi-
automated segmentation of
arterial, portal venous and
venous vascular bile ducts
tree.
The segmentation is
performed by setting seed
point into vessels entering the
liver and the system
automatically starts to
segment the whole
intrahepatic vascular object.
The user can manually add or
delete vessels.

Lesions inside and adjacent to
the liver
Manual segmentation, user-
defined manual labelling, and
3D visualization of lesions
inside and adjacent to the liver.

Semi-automated segmentation
of liver lesions.
The user triggers automatic
segmentation by drawing a
stroke across a lesion at its
largest extent. The user can
correct the given contour of a
lesion.

Tumors
Creation, transformation, and
modification of contours.
Localization and definition of
solid tumors.


exploring,
calculating,
and manually
correcting the
volume of
manually
segmented
objects of
interest

3D
visualizations
of user-defined
(vascular)
tumor margins
(coloring of
area - based on
user-defined
margin
sizes/distances
- between the
edges of user-
defined lesions
in relation to
the edges of
user-defined
vascular
structures of
the liver)

The user can manually enter
different margin values, which
are used to create color-coded
3D visualizations around user
defined lesions.

Tumor position in relation to
vessels (i.e. 3D visualization of
liver, tumor and vessels). The
margin size can be chosen
interactively for calculating
safety margin around given
tumors.

Based on user
provided
values,
calculation of
liver volume to
body weight
ratios

The user can enter the weight
and height of a patient. Using
the user-defined manually
created segmentations, MeVis
Liver Suite can calculate liver
volume to body weight ratios
(i.e., estimated weight for
remnant or graft, body surface
area, graft to recipient body
weight ratio, graft to SLV ratio,
remnant to body weight ratio).

Using a manual spatial
registration of the images, the
segmentations created on
different images (i.e. different
CT phases and MRI
sequences) can be visualized
together. The user can
manually align the images in
pairs on top of each other
using manual rigid registration

Manual rigid registration of CT
phase volumes -
In case more than one CT (or
MR) series has been selected,
the series have to be manually
aligned to each other to match
up the liver anatomy, using the
manual alignment tool.


AI-Rad Companion Organs RT (K193562)
Median: 0.85
Median: 2.0 mm

MeVis Liver Suite
Median: 0.98
Median: 1.5 mm


The qER-Quant device is intended for
automatic labeling, visualization and
quantification of segmentable brain
structures from a set of Non-Contrast
head CT (NCCT) images. The software is
intended to automate the current manual
process of identifying, labeling and
quantifying the volume of segmentable
brain structures identified on NCCT
images.

BriefCase-Quantification of Midline Shift
(MLS) is a radiological image
management and processing system
software intended for automatic
measurement of brain midline shift in
non-contrast head CT (NCCT) images,
in adults or transitional adolescents
aged 18 years and older.


qER-Quant provides volumes from NCCT
images acquired at a single time point and
provides a table with comparative
analysis for two or more images that were
acquired on the same scanner with the
same image acquisition protocol for the
same individual at multiple time points.
The qER-Quant software is indicated for
use in the analysis of the following
structures: Intracranial Hyperdensities,
Lateral Ventricles and Midline Shift.

The device is intended to assist
appropriately trained medical specialists
by providing the user with an automated
current manual process of measuring
midline shift.
The device provides midline shift
measurement from NCCT images
acquired at a single time point, and can
additionally provide an output with
comparative analysis of two or more
images that were acquired in the same
individual at multiple time points.
The device does not alter the original
medical image and is not intended to be
used as a diagnostic device. The
BriefCase-Quantification results are not
intended to be used on a stand-alone
basis for clinical decision-making or
otherwise preclude clinical assessment
of cases. Clinicians are responsible for
viewing full images per the standard of
care.

Target structures analyzed
on NCCT scans

Midline shift, Intracranial hyperdensities,
and lateral ventricles

Data acquisition protocol
Non-contrast head CT (NCCT) images
Non-contrast head CT (NCCT) images

Time point
Single or multiple time points
Single or multiple time points

Multiple electronic reports with volumetric
information of brain structures and midline
shift and Annotated DICOM Images

A Summary report with measurement
information of midline shift and
annotated images


Artificial intelligence algorithm with
database of images.

- The qER-Quant software
interacts with the user’s picture
archiving and communication
system (PACS) to receive scans
and returns the results to the
same destination.
- The core processing component
is coupled with a pre-processing
module to prepare input digital
imaging and communications in
medicine (DICOMs) for
processing by the CNNs and a
post-processing module to
convert the output into visual and
tabular output for users.

- BriefCase-Quantification is
hosted on a cloud server and
analyzes applicable CT images
that are acquired on CT scanner
that are forwarded to BriefCase-
Quantification
- The results of the analysis are
exported and presented to
medical specialists for review ,
to assist in the measurement of
MLS.


Table 3: Frequency Distribution of Gender


Table 4: Frequency Distribution of Manufacturer


Device
System, Image Processing, Radiological

Classification regulation
21 CFR 892.2050


Device trade name
Volpara Imaging Software

Legal Manufacturer
Volpara Health Technologies Limited


Predicate Device Volpara
Imaging Software
K182310

Transpara Density is a
software application
intended for use with
data from compatible

VolparaDensity is a
software application
intended for use with the
raw data from digital

Similar from the user
perspective. Main
outputs for the user are
volumetric density and

device. The substantial equivalence comparison table below provides details.


digital mammography
and digital breast
tomosynthesis systems.
Transpara Density utilizes
deep learning artificial
intelligence algorithms to
automatically determine
volumetric breast density
(VBD), breast volume,
and an ACR BI-RADS 5th
Edition breast density
category to aid health
care professionals in the
assessment of breast
tissue composition. It is
not a diagnostic aid.

breast x-ray systems,
including tomosynthesis.
VolparaDensity calculates
and quantifies
a density map and from
that determines
volumetric breast density
as a ratio of
fibroglandular tissue and
total breast volume
estimates. Volpara
provides these numerical
values along with a BI-
RADS breast density 4th
or 5th Edition category to
aid health care
professionals in the
assessment of breast
tissue composition.
VolparaDensity is not an
interpretive or diagnostic
aid and should be used
only as adjunctive
information when the
final assessment of breast
density category is made
by an MQSA-qualified
interpreting
physician.

the BI-RADS 5th Edition
density category. These
outputs are the same.

Intended Users
Health Care Professionals
Health Care Professionals
Same

Digital mammograms
from mammography or
tomosynthesis systems
(any paddle type)

Digital mammograms
from mammography or
tomosynthesis systems,
including those obtained
using with curved
paddles.

Same. The subject device
is insensitive for paddle
shapes because it uses
images processed by the
manufacturer which are
not affected by paddle
shape.

FOR PRESENTATION
(processed) images for
mammography and
Synthetic 2D images for
DBT

FOR PROCESSING (raw)
images for
mammography and the
central raw projection
image for DBT

The core algorithm for
density computation of
the subject device has a
different design which
does not require raw
data.

Volume of Breast
Volumetric Breast Density

Similar, the clinically
relevant outputs in
practice are the same.


BIRADS 5th Edition Breast
Density Category

BIRADS 4th or 5th Edition
Breast Density Category
Volume of Fibroglandular
tissue
Average thickness of
dense tissue
Maximum thickness of
dense tissue (and
location)
Maximum volume of
dense tissue above any
1cm2 square region

Density map in DICOM
SCI format, for
visualization as user
specifies.

Density maps are not
required in clinical
practice

Classification
21 CFR 892.2050
21 CFR 892.2050
Same


Standard ID
Standard Title
FDA Recognition #

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

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

Medical devices - Application of risk
management to medical devices

IEC 62304 Edition
1.1 2015-06
CONSOLIDATED
VERSION

Medical Device Software - Software Life Cycle
Processes

Health software - Part 1: General requirements
for product safety

voluntary FDA recognized standards and guidelines:


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

ISO 20417 First
edition 2021-04
Corrected version
2021-12

Medical devices – Information to be supplied by
the manufacturer


 Fetal
 Abdominal
 Pediatric
 Small Organ
 OB/GYN
 Cardiac
 Transesophage
al
 Intracardiac
 Transrectal
 Transvaginal
 Peripheral vessel
 Musculo-skeletal
(conventional)
 Musculo-skeletal
(superficial)
 Neonatal cephalic
 Adult cephalic


Sequoia
Sequoia
Origin
Sequoia
K# 211726

tur
e / Characteristic
Select
Select
Predicate

 M √ √ √ √ √

OB)
(Auto OB)
(Auto OB)
(Auto OB)
(eSie OB)
(eSie OB)

Calcs)
(Auto Calcs)
(Auto Calcs) (e
Sie Calcs)

AC
USON Sequoia, S
equoia Select a
nd Origin Diagn
ostic Ultrasound
System


 DICOM √ √ √ √ √

AC
USON Sequoia, S
equoia Select a
nd Origin Diagn
ostic Ultrasou
nd System


ACUSON
Sequoia &
Sequoia
Select
K# 223735
Predicate
device

ACUSON
SC2000
K# 211726
Predicate
device

 Cardiac Volume
Imaging Feature
 AI Assist
 AI Measure
 2D HeartAI
 4D HeartAI
 Stress Echo
 Wide FOV

√
--
√
(eSie Measure)
√
(Velocity
Variance
Mapping, eSie
LH)
√
(eSie LH, eSie
LVA, eSie RH +
GLS)
√
--

Monitor: 23.8” Dual
Layer High Dynamic
Range FPD

Touch Screen: 13.3’’
adjustable Touch
Screen


Rapid ASPECTS is a computer-
aided diagnosis (CADx) software
device used to assist the clinician in
the assessment and characterization
of brain tissue abnormalities using
CT image data. The Software
automatically registers images and
segments and analyzes ASPECTS
Regions of lnterest (ROIs). Rapid
ASPECTS extracts image data for
the ROI(s) to provide analysis and
computer analytics based on
morphological characteristics. The
imaging features are then
synthesized by an artificial
intelligence algorithm into a single
ASPECT (Alberta Stroke Program
Early CT) Score.
Rapid ASPECTS is indicated for
evaluation of patients presenting for
diagnostic imaging workup with
known MCA or ICA occlusion, for
evaluation of extent of disease.

Rapid ASPECTS is a computer-aided
diagnosis (CADx) software device used
to assist the clinician in the assessment
and characterization of brain tissue
abnormalities using CT image data.
The Software automatically registers
images and segments and analyzes
ASPECTS Regions of lnterest (ROIs).
Rapid ASPECTS extracts image data
for the ROI(s) to provide analysis and
computer analytics based on
morphological characteristics. The
imaging features are then synthesized
by an artificial intelligence algorithm
into a single ASPECT (Alberta Stroke
Program Early CT) Score.
Rapid ASPECTS is indicated for
evaluation of adult patients presenting
for diagnostic imaging workup, for
evaluation of extent of disease. Extent
of disease refers to the number of
ASPECTS regions affected which is
reflected in the total score. This device

table, as well as the Risk Benefit Analysis in the next paragraph is comparative to the predicate:


ASPECTS Atlas ROIs, highlighted
by algorithms

Primary User(s)
Neuroradiologist/Clinician
Neuroradiologist/Clinician


This device provides a systematic, automated analysis of
NCCT scans of the head to provide a standardized, automated
ASPECT score for Stroke workup. The clinical reader study,
which included 1 expert neuroradiologist and 5 non-expert
typical readers demonstrated a statistically significant
improvement in the accuracy of the 6 readers’ scores when
scoring was performed in conjunction with the Rapid
ASPECTS output. In a subgroup analysis, the benefit of the
software was most substantial among the non-neuroradiologist
expert reader which typically evaluates CT scans in
community hospitals and primary stroke centers. These non-
expert readers also evaluate CT scans in comprehensive
centers, particularly in the acute setting, when expert
neuroradiologists are not immediately available. The software
allows the non-expert physician to perform at the expert-like
level. Use of the system did not appear to have any significant
impact (either positive or negative) on the score of the expert
neuroradiologist included in the test reader group. Overall,
this system should provide a more consistent and timely
benefit of standardized reads regardless of physician and
center specialty.

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

Incorrect scoring which may result in false positive
results and results to incorrect patient management with
possible adverse effects such as, unnecessary additional


medical imaging and/or unnecessary additional
diagnostic workup.

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

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

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

However, based on the performance data and the
application of mitigating measures (general controls and
special controls established for this device type), use of
the device is unlikely to decrease diagnostic
performance of the user and possible misuse of the
device does not present additional risks compared with
misuse of other types of radiological image processing
devices.

The study was enriched to cover the range of ASPECT scores
and scanner manufacturers and the readers in practice may not
experience a significant improvement in determining
ASPECTS.

Conclusions: Do the
probable benefits outweigh
the probable risks.

Yes. The probable benefits outweigh the probable risks, given
the combination of required general controls and the special
controls established for this device. The Special Controls
will sufficiently assist in managing risks associated with
incorrect brain tissue characterization determining
ASPECT scoring, application of the device results to the
wrong patient population, analysis of incompatible images,
and/or device failure by ensuring proper performance and
use of the device.
By providing a systematic, automated analysis of NCCT scans
of the head to provide a standardized, automated ASPECT
score for Stroke workup. The Rapid ASPECTS analytics
calculates morphological characteristics of brain tissue using
the historical training data and providing results which the
attending physician may evaluate and modify based on other
presenting conditions of the patient. In addition to the Rapid


ASPECTS clinical module, other clinical information is easily
accessible within the Rapid System framework such as CTA
and CTP to inform the clinical decision-making process.
The clinical reader study demonstrates a statistically
significant improvement of ASPECTS reads among a diverse
sample of 6 typical readers representing multiple specialties,
years of practice, and practice settings.
Overall, this system should provide a more consistent and
timely benefit of standardized reads regardless of physician
and center specialty. Therefore, given the available
information concerning the benefits, risks, and supporting
data; the probable benefits outweigh the probable risks,
given the combination of required general controls and
special controls established for this device.


Feature
Proposed device:
Predicate device:

QP-Brain® (K232231)
NeuroQuant® (K170981)

Picture Archiving and
Communication System

Medical Image management
and processing system

Regulation number
21 CFR 892.2050
21 CFR 892.2050

Classification Panel
Radiology
Radiology

Manufacturer:
Quibim S.L.
CorTechs Labs, Inc.

QP-Brain® is a medical
imaging processing
application intended for
automatic labeling and
volumetric quantification of

NeuroQuant® is intended
for automatic labeling,
visualization and volumetric
quantification of
segmentable brain


segmentable brain
structures and white matter
hyperintensities (WMH) from
a set of adults and
adolescents 18 and older
MR images.

structures and lesions from
a set of MR images.
It is intended to automate
the manual process of
identifying, labeling and
quantifying the volume of
segmentable brain
structures identified on MR
images.

Software as a Medical
Device, DICOM compatible
and operate on off-the-self
hardware (multiple vendors).

The application should be
used by clinicians with
proper training, as a support
tool in assessment of
structural MRIs.
Patient management
decisions should not be
based solely on the results
of the device.

NeuroQuant® is used by
medical professionals, such
as radiologists, neurologists
and neuroradiologists, as
well as by clinical
researchers, as a support
tool in assessment of
structural MRIs.

Medical device intended
patient population

Adult patients and
adolescent patients aged 18
through 21 with brain MRI
study. Available up to 94
years.

Adult and pediatric patients
with brain MRI study.
Available for ages 3 to 100
years.

Provides volumetric
measurements of brain
structures.
- Includes segmented
color overlays and
morphometric
reports.
- Automatically
compares results to
reference percentile
data and to prior
scans when
available.
Supports DICOM format as
output of results that can be
displayed on DICOM
workstations and Picture
Archive and
Communications Systems.

MRI scanner: 3D T1 MRI
scans (for brain structures)

MRI scanner: 3D T1 MRI
scans (for brain volumetry)


Feature
Proposed device:
Predicate device:

QP-Brain® (K232231)
NeuroQuant® (K170981)

and T2 FLAIR MR (for White
Matter Hyperintensities)
acquired with specified
protocols.
QP-Brain® supports DICOM
format as input.

and T2 FLAIR MR (for White
Matter Hyperintensities)
acquired with specified
protocols.
NeuroQuant® supports
DICOM format as input.

Automated internal pipeline
that performs:
- artifact correction
- segmentation
- atlas-based
parcellation
- WMH quantification
- volume calculation
- report generation

Automated internal pipeline
that performs:
- artifact correction
- segmentation
- atlas-based
parcellation
- lesion quantification
- volume calculation
- report generation

Automated quality control
function: scan protocol
verification.
Results must be reviewed
by a clinician with proper
training.

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


Region
DICE Score
Relative Volume Difference

QP-Brain® Brain volumetry analysis module.


Size
DICE Score
Absolute volume error
F1 score


Regulation Number
Regulation Name
Product Code

Medical Image Management and Processing System

Regulation Number, Name and Product Code:


Automated Radiological Image Processing Software


LVivo Software Application (LVivo Bladder)


Manufacturer Name:
DiA Imaging Analysis Ltd


Clarius Ultrasound Transducers
C3 HD3 and PA HD3

Clarius Ultrasound App (Clarius App) for iOS;
Clarius Ultrasound App (Clarius App) for Android

K213436). Clarius Bladder AI is not a stand-alone software device.


RATIONALE
(if subject device differs
from predicate device)

Clarius Bladder AI
Bladder AI (AIBV01)
LVivo Software

Same as predicate device
and reference device

Automated Radiological
Image Processing Software

Medical Image
Management and
Processing System

Non-invasive processing of
ultrasound images using
automatic image
segmentation and
measurement of
anatomical structures
utilizing artificial
intelligence/machine-
learning algorithms.

Non-invasive processing of
ultrasound images using
automatic image
segmentation and
measurement of
anatomical structures
utilizing artificial
intelligence/machine
learning algorithms.

Clarius Bladder AI is
intended for semi-
automatic non-invasive
measurements of bladder
volume on ultrasound data
acquired by the Clarius
Ultrasound Scanner (i.e.,
curvilinear and phased
array scanners). The user

Bladder AI uses machine-
learning techniques to aid
in the quantification of
bladder volume from
ultrasound images. The
device is intended to be
used on images of patients
aged two years or
older.

LVivo platform is intended
for non-invasive processing
of ultrasound images to
detect, measure, and
calculate relevant medical
parameters of structures
and function of patients
with suspected disease.

The Clarius Bladder AI
indications for use are very
similar to the predicate
device’s indications for use
as both devices are
indicated for measurement
of bladder volume from
ultrasound image data.

Table 1 - Comparison of the Subject Device to the Legally Marketed Devices


shall be a healthcare
professional trained and
qualified in
ultrasound. The user shall
retain the ultimate
responsibility of
ascertaining the
measurements based on
standard practices and
clinical judgment.

Radiological application/
Supported modality

The subject device’s
compatible scanner
frequency falls within the
range of the predicate
device’s compatible
scanner frequency

Ultrasound image
processing software
application implementing
artificial intelligence
including non-adaptive
machine learning
algorithms trained with
clinical and/or artificial
data intended for non-
invasive segmentation and
measurements of
ultrasound data.


Yes – Segmentation of
anatomical structures
(bladder)

Yes – Measurement of
anatomical structures
(bladder)

Image segmentation for
border detection, and
bladder view classification
using a Deep Neural
Network.

Image segmentation for
border detection.

Image segmentation for
border detection using
machine learning and
active contour.

Similar to predicate device
and reference device

Same as predicate device
and reference device

Manual adjustment/
Manual editing by user
capability
(Yes or No)

Healthcare setting (e.g.,
hospital, clinic)


RATIONALE
(if subject device differs
from predicate device)

Clarius Bladder AI
Bladder AI (AIBV01)
LVivo Software

Adults and pediatrics (2
years and older)

Similar to the predicate
device and same as the
reference device.


IEC 62304:2006 + A1:2015 - Medical device software — Software life cycle processes

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

NEMA PS 3.1 - 3.20 (2022d) Digital Imaging and Communications in Medicine (DICOM) Set

IEC 62366-1:2015 + A1:2020 Medical devices — Part 1: Application of usability engineering to
medical devices

ISO 15223-1:2021 Medical devices — Symbols to be used with medical device labels, labelling
and information to be supplied


Subject Gender
Training Dataset Number of
Test Dataset Number of

Gender distribution of the subjects included in the training and test datasets are shown below.


Subject Device:
AI-Rad Companion
Brain MR VA50A

AI-Rad Companion
Brain MR is a post-
processing image
analysis software that
assists clinicians in
viewing, analyzing, and
evaluating MR brain
images.
AI-Rad Companion
Brain MR provides the
following
functionalities:
• Automatic
segmentation and
quantitative analysis
of individual brain
structures and
white matter
hyperintensities
• Quantitative
comparison of each
brain structure with
normative data from
a healthy
population
• Presentation of
results for reporting
that includes all
numerical values as
well as visualization
of these results

icobrain is intended
for automatic
labeling,
visualization and
volumetric
quantification of
segmentable brain
structures
from a set of MR or
NCCT images. This
software is intended
to automate the
current manual
process of
identifying,
labeling and
quantifying the
volume of
segmentable brain
structures identified
on MR or NCCT
images. icobrain
consists of two
distinct image
processing pipelines:
icobrain cross and
icobrain long.
icobrain cross is
intended to provide
volumes from MR or
NCCT images
acquired at a single
time point. icobrain
long is intended to
provide changes in
volumes between two
MR images that were
acquired on the same
scanner,

raise different questions of the safety and effectiveness.


with the same image
acquisition protocol
and with same
contrast at two
different timepoints.
The results of
icobrain cross cannot
be compared with the
results of icobrain
long.

Pre-processing
functionality for
automatic segmentation
and volumetry of
MPRAGE data.

Image processing for
automatic
segmentation and
volumetry of
MPRAGE data.

Brain White
Matter
Hyperintensities
Segmentation

AI-Rad Companion
Brain MR White Matter
Hyperintensities
(WMH) includes
segmentation and
quantification of White
Matter Hyperintensities
on the basis of T1-
weighted
MPRAGE and T2-
weighted FLAIR
datasets as input.

Pre-processing
functionality for
automatic segmentation
and volumetry of
MPRAGE and FLAIR
data.

Image processing for
automatic
segmentation and
volumetry of FLAIR
data.

Brain White
Matter
Hyperintensities
Quantification

The WMH report
contains visualization of
WMH (3D overlay of
WMH map) and
numeric results of count
and volume of WMH as
per four brain regions
periventricular,
juxtacortial,
infratentorial and deep
white matter.

Unnormalized
volume and volume
changes of FLAIR
white matter
hyperintensities as
per 4 brain regions


The longitudinal
assessment of Brain MR
images from two
timepoints provides the
rate of change of
volumes of brain
structures and the count
and volume of new or
enlarged WMH.

The follow-up feature is
not available in the
equivalent device (AI-
Rad Companion Brain
MR VA40)

Assessment of
New/Enlarging lesion
count

Calculation of white
matter hyperintensities
map fused with the
processed FLAIR data
User customizable color
labels for the overlay
map.

Calculation of white
matter
hyperintensities map
overlaid with the
FLAIR data

Calculation of label
maps (display of brain
segmentation) and
partially combined label
maps (fused with the
processed MPRAGE
data).

Normalized and
unnormalized volume
and volume changes
of different brain
structures.

Brain
Morphometry:
Deviation Map
and Label map

• Deviation results
include deviation map,
which presents different
brain regions, color-
coded to indicate the
degree of deviation from
the average age- and
gender-matched
normative volume.
• Label results include
the label map, which
shows different brain
regions using different
colors.

Creation of an image
series for morphometry
and WMH reports.
Automatic transfer of
generated maps and

Automatic transfer of
generated image
series and report to a
PACS system.


Cloud-based and on-
edge deployment in the
institution premise. For
Edge deployment, the
data processing is
performed within the
institution whereas
logging, institution
management, and audit
logging are performed in
the cloud.

Cloud only solution
with no components
deployed on
customer premise.

Configuration UI
User can activate or
deactivate the
processing of brain MR
cases in AI-Rad
Companion Engine.
If you activate AI-Rad
Companion Brain MR,
then the brain MR cases
uploaded to AI-Rad
Companion Engine are
processed and is
displayed in the Patient
List. You can manually
adjust the settings
available in the General
Settings screen.
If you deactivate AI-Rad
Companion Brain MR,
then no brain MR cases
are processed in AI-Rad
Companion Engine. The
settings available in the
General Settings screen
are not displayed.
To activate or deactivate
AI-Rad Companion
Brain MR, see
Configuring AI-Rad


Companion in the AI-
Rad Companion Engine
Instructions for Use.
Longitudinal (Follow-
up) configurations and
Support for output in
different orientations are
added.
Conformation UI
On the Results Preview,
user can confirm or
decline the results and
send them to the PACS.
All changes are
temporarily saved until
the case is sent to the
PACS.

Companion in the AI-
Rad Companion Engine
Instructions for Use.
Conformation UI
On the Results Preview,
user can confirm or
decline the results and
send them to the PACS.
All changes are
temporarily saved until
the case is sent to the
PACS.

AI-Rad Companion
Brain MR was tested on
Microsoft Windows 10.
AI-Rad Companion
Brain MR is not
validated on any other
operating system, for
example, MAC.
AI-Rad Companion
Brain MR is not
validated for use with
touch screen or mobile
devices.
AI-Rad Companion
Notifier requires a 64-bit
Windows Operating
System (Windows 10
recommended). It is
recommended to use
Google Chrome as a
preferred web browser
for use with AI-Rad
Companion Brain MR.

Cloud based deployment
Edge deployment
Platform


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

IEC 62366-1
Edition 1.1
2020-06
CONSOLIDATED
VERSION

Medical Devices – application
of risk management to
medical devices

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


Digital Imaging and
Communications in Medicine
(DICOM) Set

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

Health software – Part 1:
General requirements for
product safety


A PCC >= 0.77 is considered as a passed case
for volumetric segmentation accuracy

A mean Dice score >=0.47 is considered as a
passed case for segmentation quality

WMH Change Region-wise Segmentation
Accuracy

A median F1-score >=0.69 is considered a
passed case


Summary Performance data, Standard Deviations & CIs:


UPenn: (US): 15
ADNI (US): 15
Lausanne (EU): 22
Prague (EU): 23


Predicate Device
FDA Clearance Number
Product
Manufacturer

are substantially equivalent to the following predicate device:


Reference Devices
FDA Clearance Number
Product
Manufacturer

MAGNETOM Vida with
software syngo MR XA50A

MAGNETOM Prisma with
software syngo MR XA30A


Please note: due to reasons of data privacy, we did
not record how many individuals the datasets
belong to. Gender, age, and ethnicity distribution
were also not recorded during data collection. Due
to the network architecture, attributes like gender,
age and ethnicity are not relevant to the training
data.

Please note: Due to
the network
architecture, attributes
like gender, age and
ethnicity are not
relevant to the training
data.

The input and output variables of the network have been derived from the
same dataset so that no confounders exist for the training methodology.

The acquired datasets
represent the ground truth
for the training and
validation. Input data was
retrospectively created
from the ground truth by
data manipulation and
augmentation. This
process includes further
under-sampling of the
data by discarding k-
space lines, lowering of
the SNR level by addition
of noise and mirroring of
k-space data.

The acquired datasets
represent the ground
truth for the training and
validation. Input data
was retrospectively
created from the ground
truth by data
manipulation. k-space
data has been cropped
such that only the center
part of the data was
used as input. With this
method corresponding
low-resolution data as
input and high-resolution
data as output / ground
truth were created for
training and validation.

Applying three different
methods for bias field
correction to the data,
homodyne filtering, N4
and UNICORN.

The impact of the network
has been characterized
by several quality metrics
such as peak signal-to-
noise ratio (PSNR) and
structural similarity index
(SSIM). Additionally,
images were inspected
visually to ensure that
potential artefacts are
detected that are not well
captured by the metrics
listed above.
After successful passing
of the quality metrics
tests, work-in-progress
packages of the network
were delivered and
evaluated in clinical
settings with cooperation
partners. In a total of
seven peer-reviewed
publications 427 patients

The impact of the
network has been
characterized by several
quality metrics such as
peak signal-to-noise
ratio (PSNR), structural
similarity index (SSIM),
and perceptual loss. In
addition, the feature has
been verified and
validated by inhouse
tests. These tests
include visual rating and
an evaluation of image
sharpness by intensity
profile comparisons of
reconstruction with and
without Deep Resolve
Sharp. Both tests show
increased edge
sharpness.

Two step test
procedure.
1. During training, the
test data set was
used to validate
how the network
performed on
unseen data.
2. During system tests,
the standard
deviation was
determined, and the
RMS error was
calculated (against
the ground truth).
The tests show that
Deep RxE increases
image homogeneity in
a reproduceable way
on the receive profile.
Images acquired with
Deep RxE (DL bias


dynamic pTx for TFL
(for MAGNETOM
Terra.X only)

Cloos MA, Boulant N, Luong M, Ferrand G, Giacomini E, Le Bihan D,
Amadon A. kT -points: short three-dimensional tailored RF pulses for flip-
angle homogenization over an extended volume. Magn Reson Med. 2012
Jan;67(1):72-80. doi: 10.1002/mrm.22978. Epub 2011 May 16. PMID:
21590724.

Majewski K. Simultaneous optimization of radio frequency and gradient
waveforms with exact Hessians and slew rate constraints applied to kT-
points excitation. Journal of Magnetic Resonance. 2021 May 1;
326:106941.

Herrler, J, Liebig, P, Gumbrecht, R, et al. Fast online-customized
(FOCUS) parallel transmission pulses: A combination of universal pulses
and individual optimization. Magn Reson Med. 2021; 85: 3140-3153.
https://doi.org/10.1002/mrm.28643

Tanner, Mark, Giulio Gambarota, Tobias Kober, Gunnar Krueger, David
Erritzoe, José P Marques, and Rexford Newbould. 2012. ‘Fluid and White
Matter Suppression with the MP2RAGE Sequence.’ Journal of Magnetic
Resonance Imaging: JMRI 35 (5): 1063-70.
https://doi.org/10.1002/jmri.23532.

Weight limit
reduction for 31P/1H
TxRx Flex Loop Coil

Parasoglou, P, et al., 3D-Mapping of Phosphocreatine Concentration in
the Human Calf Muscle at 7T: Comparison to 3T, Magn Reson Med.
Author manuscript; available in PMC 2014 December 01, 2013
December, 70(6)

Hooijmans M. T., et al., Spatially localized phosphorous metabolism of
skeletal muscle in Duchenne muscular dystrophy patients: 24±month
follow-up,https://doi.org/10.1371/journal.pone.0182086,August 1, 2017


Medical electrical equipment -
Part 1: General requirements for
basic safety and essential
performance (IEC 60601-
1:2005, MOD)

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

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

Medical devices - Application of
risk management to medical
devices

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

Medical device software -
Software life cycle processes

62304 Edition 1.1
2015-06
CONSOLIDATED
VERSION

Biological evaluation of medical
devices - part 1: evaluation and
testing within a risk
management process


Digital Imaging and
Communications in Medicine
(DICOM) Set

Determination of Signal-to-
Noise Ratio (SNR) in Diagnostic
Magnetic Resonance Images

Determination of Two-
dimensional Geometric
Distortion in Diagnostic
Magnetic Resonance Images

Determination of Image
Uniformity in Diagnostic
Magnetic Resonance Images

Acoustic Noise Measurement
Procedure for Diagnosing
Magnetic Resonance Imaging
Devices

Determination of Slice
Thickness in Diagnostic
Magnetic Resonance Imaging

Determination of Signal-to-
Noise Ratio and Image
Uniformity for Single-Channel,
Non-Volume Coils in Diagnostic
Magnetic Resonance Imaging
(MRI)

Characterization of the Specific
Absorption Rate for Magnetic
Resonance Imaging Systems

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

Determination of Local Specific
Absorption Rate (SAR) in
Diagnostic Magnetic Resonance
Imaging Systems

Quantification and Mapping of
Geometric Distortion for Special
Applications


Proprietary name:
InVision Precision LVEF (LVEF)

Medical image management and processing system

QIH Automated Radiological Image Processing Software


Predicate Device:
Caption Interpretation
Automated Ejection
Fraction Software
K210747


Automation level
Semi automated
Fully automated

Views for EF Calculation
Single view or Biplane
Single view or Biplane

Uses the interface of
approved PACS system

Quantitative feedback to
enable clinician to
assess EF calculation

Network Architecture
Spatiotemporal Pooling
Simple Pooling


The qER-Quant device is intended for
automatic labeling, visualization and
quantification of segmentable brain
structures from a set of Non-Contrast head
CT (NCCT) images. The software is intended
to automate the current manual process of
identifying, labeling and quantifying the
volume of segmentable brain structures
identified on NCCT images.
qER-Quant provides volumes from NCCT
images acquired at a single time point and
provides a table with comparative analysis for
two or more images that were acquired on the
same scanner with the same image
acquisition protocol for the same individual at
multiple time points.
The qER-Quant software is indicated for use
in the analysis of the following structures:
Abnormal Intracranial Hyperdensities, Lateral
Ventricles and Midline Shift.

The Viz HDS device is intended for automatic
labeling, visualization, and quantification of
segmentable brain structures from a set of
Non-Contrast CT (NCCT) head scans. The
software is intended to automate the current
manual process of identifying, labeling, and
quantifying the volume of segmentable brain
structures identified on NCCT images. Viz
HDS provides volumes from NCCT scans
acquired at a single time point. The Viz HDS
software is indicated for use in the analysis of
the following structures: Intracranial
Hyperdensities, Lateral Ventricles and
Midline Shift. The device output should be
reviewed along with patient’s original images
by a physician.


Non-contrast CT from a single or multiple
time points

Non-contrast CT from a single time point

Intracranial hyperdensities, lateral ventricles
and midline shift

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

Multiple electronic reports with volumetric
information of brain structures and midline
shift AND Annotated DICOM Images


Summary test statics
or other test results
including acceptance
criteria or other
information
supporting the
appropriateness of the
characterized
performance

∙ The overall model success
rate of the Abdomen, Air,
Breast, Carotid, Leg, MSK,
Scrotal, Thyroid and
Carotid/Thyroid(Mixed)
view suggestion is expected
to be 80% or higher.
∙ The number of individual
patient’s images were
collected from: 50+ patients
∙ The number of samples, if
different from above, and
the relationship between the
two: 330+ images

Information about
clinical subgroups and
confounders present
in the dataset

∙ Gender: Male & Female
∙ Age: Reproductive age,
specific age not collected.
∙ Ethnicity/Country;
USA(57%) and
Australia(43%)

Information about
equipment and
protocols used to
collect images

Mix of data from across 4
different probe models with
LOGIQ Totus console. The
data collection protocol was
standardized across all data


Information about
how the reference
standard was derived
from the dataset(i.e.
the “truthing”
process)

For the testing process, the
results are generated by the
AI software and the same
are verified as Pass or Fail
by a certified
sonographer/clinician. The
results are then aggregated
to yield an accuracy metric
for the AI algorithm.

Description of how
independence of test
data from training
data was ensured

The exams used for
test/training validation
purpose are separated from
the ones used during
training process and there is
no overlap between the two.

510(k) Premarket Notification Submission


Summary test
statistics or other test
results including
acceptance criteria or
other information
supporting the
appropriateness of the
characterized
performance

∙ The overall model success
rate of the Aorta, Kidney,
Liver, GB and Pancreas
view suggestion is expected
to be 80% or higher.
∙ The number of individual
patients’ images were
collected from: 40 patients.
∙ The number of samples, if
different from above, and
the relationship between the
two: 280+ images

∙ Gender: Male & Female
∙ Age: Reproductive age,
specific age not collected.
∙ Ethnicity/country:
USA(35%) and
Australia(65%)

Information about
equipment and
protocols used to

Mix of data from across 4
different probe models with
LOGIQ Totus console. The


VideaHealth,Inc.
179SouthStreet,Floor5
Boston,MA, 02111
+1617-340-9940
florian@videa.ai

AdamForesman
Directorof Quality&RegulatoryAffairs
VideaHealth,Inc.
+1617-340-9940
adam@videa.ai


Caries
3years andolder
Bitewing andPeriapical

Attrition
3years andolder
Bitewing andPeriapical

Broken/Chipped
3years andolder
Bitewing andPeriapical

Restorative Imperfection
3years andolder
Bitewing andPeriapical

12years of ageand
olderwith permanent
dentition

Dens Invaginatus
3years &older
Bitewing andPeriapical

22years of ageand
olderwith permanent
dentition

Calculus
3years andolder
Bitewing andPeriapical

WidenedPeriodontal Ligament
3years andolder
Bitewing andPeriapical

1.Impacted Tooth
2.Mental Foramen
3.MaxillaryTuberosity
On Bitewing,Periapical
& Panoramic

Table 1: VDAIndications ScopebyPatient Ageand ImageModality Type


All otherindicationsare
onBitewing, Periapical
& Panoramic except:
1.‘Mandibular Condyle’
VDA normalanatomy
indicationisonly on
Panoramicimages.


Device Name
Videa DentalAssist
Videa CariesAssist

Bitewing Images,Periapical
Images,and Panoramic
Images.
Radiograph viewtypescopeis
Videa DentalAssistindication
specific.

Caries: Activeand Secondary
Cariesatall penetrationdepths
AdditionalSuspect Dental
Findings listed intheVidea
Dental Assist’sIndicationsFor
Use statement.

Caries: Activeand Secondary
Cariesatall penetration
depths.

Historical Treatmentand
Normal Anatomy
Indications


For thecariesindicationonly:
Proximal, Buccal/Lingual,
Occlusal, Root,Cervical.
Noneof theadditionalVDA
‘Suspect DentalFinding’
indications arespecifictoa
tooth surface.

Caries: Proximal,
Buccal/Lingual, Occlusal,
Root, Cervical.

Message indicating if and
how many findings were
detected for each enabled
Videa Dental Assist’s
indication for use.
All Videa Dental Assist’s
indications use a set of
togglable bounding boxes
around suspected areas of
interest.

Message indicating if and
how many carious lesions
were detected.
Set of togglable bounding
boxes around suspected
lesions.

Patients ≥3yearsof age.
PatientagerangeisVidea
Dental Assistindication
specific.

Intended User
Dental professionals
US licensed dentists

Development Technology
Supervised Deep Learning
Supervised Deep Learning


Table 4: Imagebreakdownby radiographic view


Table 6: Imagebreakdownby radiographic view


AveragePercentageImprovementWith VDA
(VDA Aidedvs. UnaidedMean AFROCFOM)

Table 7: ClinicalPerformance MetricsofVDA byradiographic viewtype andline type.


AveragePercentage ImprovementWithVDA
(VDA Aidedvs. UnaidedMean AFROCFOM)


N/A. Seetableaboveontheoverall PRL indicationlevel
results. PRL isonlyapplicableonperiapical radiographsso
there isnosubgroupanalysistoperform.


AveragePercentage Improvement
With VDA
(VDA Aidedvs. UnaidedMean
AFROC FOM)

Calculus isonlyintendedfor
patients12years of ageand
older.

Furcation isonlyintendedfor
patients22years of ageand
older.

Periapicalradiolucencyis only
intendedfor patients22years
of ageand older.

AFROC ScoresOn PediatricPatients(Ages 3to 21Yearsof AgeUnlessOtherwise Noted)


Pulp Stonesindicationisonly
intendedfor patients12years
of ageand older.


Intended Use/Indications for Use 21 CFR 807.92(a)(5)

SmartChest is a radiological computer assisted triage and notification software that analyzes frontal chest X-ray images (Postero-Anterior
(PA) or Antero-Posterior (AP)) of transitional adolescents (18 - 21 yo but treated like adults) and adults (≥22 yo) for the presence of
suspected pleural effusion and/or pneumothorax. SmartChest uses an artificial intelligence algorithm to analyze the images for features
suggestive of critical findings and provides case-level output available to a PACS (or other DICOM storage platforms) for worklist
prioritization.
As a passive notification for prioritization-only software tool within the standard of care workflow, SmartChest does not send a proactive
alert directly to a trained medical specialist.
SmartChest is not intended to direct attention to a specific portion of an image. Its results are not intended to be used on a stand-alone
basis for clinical decision-making.

Indications for Use Comparison 21 CFR 807.92(a)(5)

The predicate device Indications for Use is: 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 effusion and/or

pneumothorax). Lunit INSIGHT CXR Triage 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, Lunit INSIGHT CXR Triage does not send a proactive alert directly
to the appropriately trained medical specialists. Lunit INSIGHT CXR Triage 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.
The Indications for Use are similar and the Intended Use is the same.

Technological Comparison 21 CFR 807.92(a)(6)

The subject device (SmartChest) and the predicate device (Lunit INSIGHT CXR) are both radiological computer assisted prioritization
notification software. The technologies use artificial intelligence algorithms to analyze radiological images.
The subject and predicate devices share similar technological characteristics as follows:
- target population, intended user and use environment, anatomical site and modality, means of notification, standalone performance
level, and triage effectiveness.
All outputs are the same between the subject device and the primary device.
The only difference is related to the predicate device being able to connect to radiological imaging equipment and in parallel also
connecting to PACS. The subject device only connects to the PACS (or other DICOM storage platforms) and flags cases there.
These differences do not change or modify the risks associated with the device type nor does it raise new questions of safety or
effectiveness.
Both the subject device and predicate device are only intended to passively notify the end user that a study is suspicious of the indicated
findings and enable them to prioritize those studies, but the end user is still required to review all images and make the find clinical
diagnosis.
TNheorenfo-reC, blyin exiacmainla taionn dof /thoer d eCviclien's iincteanld eTde usset asn dS teuchmnolmogiacarl yat tr&ibu Cteso, snubcsltaunstiailo eqnusiva len2ce1 i sC suFppRo rt8ed0. 7.92(b)


Common Use/Usual Name
Computer Tomography X-ray system

Classification
Class II, 21 CFR 892.1750


Predicate Classification
Class II, 21 CFR 892.1750


VIDA|vision
VIDA Diagnostics, Inc
(Reference Device)

Regulation Number
21 CFR 892.1750
Same
Same

Software Accessory to a
Computed tomography x-ray
system

The Thirona LungQ software
provides reproducible CT
values for pulmonary tissue
which is essential for
providing quantitative
support for diagnosis and
follow up examination. The
LungQ software can be used
to support physician in the
diagnosis and
documentation of
pulmonary tissues images
(e.g., abnormalities) from CT
thoracic datasets. Three-D

Table 5-1: Substantial Equivalence Comparison between Subject, Predicate and Reference Device


segmentation and isolation
of sub-compartments,
volumetric analysis, density
evaluations, fissure
evaluation, and reporting
tools are provided.

Application
Command-line interface
Same
Equivalent

Segmentation
Provides 3D segmentation
Same
Same

Provides Segmentation of
the:
• Left Lung
• Right Lung
• Left Upper Lobe
• Left Lower Lobe
• Right Upper Lobe
• Right Middle Lobe
• Right Lower Lobe
• Pulmonary
(sub)segments

Ability to measure volume
for:
• Both Lungs
• Left Lung
• Right Lung
• Left Upper Lobe
• Left Lower Lobe
• Right Upper Lobe
• Right Middle Lob
• Right Lower Lobe
• Pulmonary
(sub)segments

Ability to measure volume at
multiple density ranges for:
• Both Lungs


• Left Lung
• Right Lung
• Left Upper Lobe
• Left Lower Lobe
• Right Upper Lobe
• Right Middle Lob
• Right Lower Lobe
• Pulmonary
(sub)segments

Ability to measure the 15th
percentile density analysis


Principles for medical device security—Risk management

Medical device software - Part 1: Guidance on the
application of ISO 14971 to medical device software

Manufacturer Disclosure Statement for Medical Device
Security

Table 5-2: Standards and Guidance documents


Medical device software - Software life-cycle processes

Medical devices - Application of usability engineering to
medical devices

Health software - Part 1: General requirements for
product safety

Medical devices - Application of risk management to
medical devices

Medical devices — Symbols to be used with information
to be supplied by the manufacturer — Part 1: General
requirements

Medical devices — Information to be supplied by the
manufacturer

Information technology — The JSON data interchange
syntax

Information technology — Security techniques —
Information security management systems —
Requirements

Digital Imaging and Communications in Medicine
(DICOM) Set


General Principles of Software Validation

Applying Human Factors and Usability Engineering to Medical
Devices

The 510(k) Program: Evaluating Substantial Equivalence in
Premarket Notifications [510(k)]

Appropriate Use of Voluntary Consensus Standards in Premarket
Submissions for Medical Devices

Design Considerations and Pre-market Submission
Recommendations for Interoperable Medical Devices

Unique Device Identification System: Form and Content of the
Unique Device Identifier (UDI)

Recommended Content and Format of Non-Clinical Bench
Performance Testing Information in Premarket Submissions

Off-The-Shelf Software Use in Medical Devices

Content of Premarket Submissions for Device Software Functions

Cybersecurity in Medical Devices: Quality System Considerations and
Content of Premarket Submissions

Cybersecurity in Medical Devices: Refuse to Accept Policy for Cyber
Devices and Related Systems Under Section 524B of the FD&C Act


Scanner manufacture
GE MEDICAL SYSTEMS; SIEMENS; Philips

LightSpeed16; LightSpeed VCT; Sensation 64;
Definition; Sensation 16; Definition AS+;
SOMATOM Definition Flash; Brilliance 64;
LightSpeed Pro 16; Discovery CT750 HD;
SOMATOM Definition; LightSpeed Ultra
SOMATOM Definition AS; LightSpeed16

Table 5-4: Imaging parameters equivalence study.


Monsuru (Kenny) Bello
Regulatory Affairs
monsuru.bello@siemens-healthineers.com
+1(240) 601-3848

Clayton Ginn
Regulatory Affairs
clayton.ginn@siemens-healthineers.com
+1 (865) 898-2692

Siemens Medical Solutions, Inc. USA
Molecular Imaging
810 Innovation Drive
Knoxville, TN 37932
Establishment Registration Number: 1034973

Siemens Healthcare GmbH
SIEMENSSTRASSE 1 -OR-
Rittigfeld 1
FORCHHEIM Bavaria, DE 91301
Establishment Registration Number: 3004977335

Siemens Medical Solutions USA, Inc.
40 Liberty Boulevard
Malvern, PA 19355
Establishment Registration Number: 2240869


syngo.CT Brain Hemorrhage
(SOMARIS/8 VB80)

syngo.CT Brain Hemorrhage
(SOMARIS/8 VB60)

Notification-only, parallel
workflow tool

Radiologists and clinical administrators


Identify patients with a
prespecified clinical
condition

Intracranial hemorrhage,
Subarachnoid hemorrhage

Alert to finding
Yes; flagged for review
Yes; flagged for review
Same

Independent of standard
of care workflow

Modality
Non-contrast CT
Non-contrast CT
Same

Training Data
29 713 cases
28 814 cases
Improved

Suspected hemorrhage / Suspected
hemorrhage including the
subarachnoid space / Processing
finished

Suspected hemorrhage / Processing
finished


Standard
Version
Content
FDA Recognition

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

Digital Imaging and Communications in Medicine
(DICOM) Set

Application of Risk Management to Medical Devices

Edition 1.1 2020-06
CONSOLIDATED
VERSION

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

Medical devices - Symbols to be used with information
to be supplied by the manufacturer - Part 1: General
requirements

First edition 2021-
04 Corrected
version 2021-12

Medical devices - Information to be supplied by the
manufacturer

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


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

Rapid SDH is a radiological
computer aided triage and
notification software indicated for
use in the triage and notification of
hemispheric SDH in non-enhanced
head images. The device is
intended to assist trained
radiologists in workflow triage by
providing notification of suspected
findings of hemispheric Subdural
Hemorrhage (SDH) in head CT
images.
Rapid SDH uses an artificial
intelligence algorithm to analyze
images and highlight cases with
suspected hemispheric SDH on a
server or standalone desktop
application in parallel to the
ongoing standard of care image
interpretation. The user is
presented with notifications for
cases with suspected hemispheric
SDH findings. Notifications
include compressed preview
images, that are meant for
informational purposes only and
not intended for diagnostic use
beyond notification. The device
does not alter the original medical
image and is not intended to be
used as a diagnostic device.
The results of Rapid SDH are
intended to be used in conjunction
with other patient information and
based on professional judgment, to
assist with triage/prioritization of

features are compared in the following table:


medical images. Notified
clinicians are responsible for
viewing full images per the
standard of care.
Note: See limitations in IFU
statement above

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

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


Source TP FP FN TN Total Measure Estimate 95% 95%

provided in those sites where enough samples are included to provide statistical relevance:


Source TP FP FN TN Total Measure 95% 95%


should not be used in-lieu of full patient evaluation or solely

in the measurementsof mesialanddistalbonelevelsassociated


Imagesimportedfromthe
radiographicdevice,or
fromthepractice
managementsystem,or
manuallyuploadedintothe
system

Imagesimportedfromthe
radiographicdevice,or from
thepracticemanagement
system

DICOM, JPEG, JPG, PNG,
TIFF,TIF, JFIF or BMP
file

Enduser Dentist,dentalhygienist Dentist,dentalhygienist Yes


PrimaryPredicate
Overjet DentalAssist
K210187

patientsliving intheUnited
States,who are22yearsold
or older,that donothave
any remainingprimary
teeth.

patientsliving intheUnited
States,who are22yearsold
or older,and thatdonot have
any remainingprimary teeth.

Model output
Line segments
Line segments
Yes


length measurementslabeled bythree(3) ground trutherswithinthose radiographs.


PrimaryPredicate
Ethos Treatment
Management,2.1;
Ethos Treatment
Planning,1.1

Reference
device
Eclipse Treatment
PlanningSystem

System, Planning,
Radiation Therapy
Treatment

Radiological image
processing software
forradiationtherapy

Theproposeddevicesharesthesame
common name ”System, Planning,
RadiationTherapyTreatment”withthe
primary predicate, especially Ethos
Treatment Planning, 1.1 (that has
“MUJ” asaproductcodeinits510(k)
summary) and with some of the
referencedevices.

Varian Medical
Systems (now
Varian Medical
Systems,Inc)

The proposed device, primary
predicate and reference devices
haveidenticaldeviceclassification.

The primary product code isMUJfor
“System,Planning,RadiationTherapy
Treatment” asitisasoftwareusedin
theplanningofradiotherapytreatment
like the primary predicate, especially
Ethos Treatment Planning, 1.1 (that
has “MUJ” as a product code in its
510(k)summary) andsomereference
devices use it as their primary or
secondarycode


Assecondaryproductcode:
- QKB (Radiological image
processing software for radiation
therapy)hasbeenincludedasthe
softwareusesAIalgorithmsandis
intendedforradiationtherapy.Itis
also the primary code of the
reference device ART-Plan
v1.10.1ofwhichART-Plan2.1.0is
anupdate;
- LLZ (System, Image Processing,
Radiological) has been included
as the software is used inimage
processing and it is also the
subsequent codeofthereference
deviceART-Planv1.10.1ofwhich
ART-Plan2.1.0isanupdate.

ART-Plan’s
indicated target
populationiscancer
patients for whom
radiotherapy
treatment has been
prescribed. In this
population, any
patient for whom
relevant modality
imaging data is
available.

The patient target
groups are the
patients for whom
radiation therapyis
indicated.

ART-Plan’s indicated
target population is
cancer patients for
whom radiotherapy
treatment has been
prescribed. In this
population,anypatient
for whom relevant
modality imaging data
isavailable.

Any patients with
malignantorbenign
diseases

The proposeddeviceandtheprimary
predicate have identical target
populations.

The proposed device, primary
predicate and reference devices
haveidenticaltargetenvironments.

IntendedUse
ART-Plan is a
softwareintendedto
be used by trained
clinicians who are
familiar with
radiation therapy,
such as medical
physicists, medical
dosimetrists and

IntendedUse
Ethos Treatment
Management is
used to manage
and monitor
radiation therapy
treatment plans
and sessions; it is
intendedtobeused

IntendedUse
ART-Plan is a
software for
multi-modal
visualization,
contouring and
processing of 3D
images of cancer
patients for whom

IntendedUse
TheVarianEclipse
device is a
treatmentplanning
system used for
diagnostic image
analysis,
contouring and
segmentation,
geometrical

Intendeduse
Not availableinthe
summary:
Indicationforuse
The Eclipse
Treatment Planning
System (Eclipse
TPS) is used to
plan radiotherapy

The intended use and indications
for use of the proposed device,
ART-Plan v2.1.0 and the primary
predicate (especially Ethos
Treatment Planning, 1.1) are
similar as they arebothsoftwares
intendedtobeusedintheplanning
ofradiotherapytreatment1:


PrimaryPredicate
Ethos Treatment
Management,2.1;
Ethos Treatment
Planning,1.1

Referencedevice
Eclipse Treatment
PlanningSystem

Standalone
softwareapplication
accessed via a
compliant browser
(Chrome, Mozilla
Firefox and Edge)
on a personal
computer, tablet or
phone (In case of
connection to the
platform with a
screen of a phone
oratablet,theuser
must choose the
option for the
desktop site of his
communication
device. The
platformisoptimally
usedwith17inches
and up screen.
Facilitates display
and visualization of
databyuser.

Standalone
software application
accessed via a
compliant browser
(Chrome or Mozilla
Firefox) on a
personal computer,
tablet or phone (In
case of connection
to the platform with
ascreenofaphone
or atablet,theuser
mustchoosethe
option for the
desktop site of his
communication
device.Theplatform
is optimally used
with 17 inches and
upscreen.
Facilitates display
and visualization of
databyuser.

The proposed device andtheprimary
predicate are both standalone
software. More details have been
found on the reference device
ART-Plan v1.10.1whichhas identical
methods of use than the proposed
device. An improvement has been
introduced with ART-plan v2.1.0 as it
canbealsousedonEdgebrowser.

Data
Visualizatio
n /
Graphical
Interface

The proposed device, the primary
predicates and all references devices
haveadatavisualisationandgraphical
interface

Generation of CT
density image
series out of
multiple MR-image
series and CBCT
images

Generation of CT
densityimageseries
outofCBCTimages

The proposed device and primary
predicate can generate synthetic-CT
fromCBCTimage.
The proposed device can generate
synthetic CT from CBCT and MR
imageswhereasitisnotpossiblewith


theprimarypredicatetodosowithMR
images. This does not represent an
additional claim as the technological
characteristics are the same and it
does not raise different questions of
safety and effectiveness. Also, this
feature is covered by the reference
device ART-Planv1.10.1,whichisthe
previous version cleared of the
proposeddeviceART-Planv2.1.0.

Dose computation
on CT and/or
synthetic-CT
images for external
beam irradiation
withphotonbeams

Dose calculation
with AAA dose
calculation model
andAcurosXBdose
calculationalgorithm
on CT and / or
syntheticCTimages

The AAA dose
calculation model is
a 3D
convolution/superpos
ition algorithm that
models primary
photons, photons
scattered in the
medium,
contamination
electrons and
transport electrons
near tissue
heterogeneities. The
AAAdosecalculation
model is comprised
of two main
components, one
being the
configuration
algorithm and the
other one the actual
dose calculation
algorithm.

The proposed device, the primary
predicateandsomereferencedevices
ECLIPSE WITH AAA and Eclipse
Treatment Planning System can
performdosecomputation.

Assisted
CBCT-basedoff-line
adaptation
decision-making for
supported
anatomies

Ethos Treatment
Management allows
the physician to do
initial planning,
review and approve
candidateplans,and
monitor ongoing
treatments. Support
for adaptive
radiotherapy

The proposed device andtheprimary
predicatecanassistoff-lineadaptation
decision-making.


treatment planning
and automated plan
generation

Registration:
StaticandgatedCT,
MR, PET (via the
registration of the
CT of said PET),
4D-CT, CBCT and
synthetic-CT
generated from
CBCT
Segmentation:
CT(injectedornot),
MRimages,DICOM
RTSTRUCT,
synthetic-CT from
CBCT

Registration:
CT (including
synthetic CT from
CBCT), MR and
PET
Segmentation:
CTandsyntheticCT
fromCBCT

Registration:
StaticandgatedCT
(including 4D-CT
and CBCT), MR,
PET (via the
registration of the
CTofsaidPET)
Segmentation:
CT(injectedornot),
MRimages,DICOM
RTSTRUCT

Segmentation:
Eclipsealsoincludes
tools for treatment
preparation
(diagnostic image
and analysis,
contouring and
segmentation) and
planreview.

Registration:
CT/MR/PET Image
Registration
4D image display
(registration of time
yes yes seriesof3D
images)
Segmentation:
Geometrical shapes,
Manual editing and
manipulation tools,
Automatic
/semi-automatic
tools,
Automatic/semi-auto
matic on-demand
and post-processing
tools for individual
organs/structures,
Automatic
on-demand and
pre-processing tools
for multiple
organs/structures,3D
Autornargin, Logical
operators

The proposed device, the primary
predicate and most of the reference
devices propose both registrationand
segmentation on medical images of
differentmodalities.

Distribution of
DICOM compliant
Images into other
DICOM compliant
systems.

ARIA RadOnc
integration, DICOM
RT, other image
formats, eclipse
scripting API
(ESAPI) read only
access, eclipse
scripting API
(ESAPI) write
access, Eclipse
automation, Export
field coordinates to
laser system, basic

DICOM includingRT
objects,MDCshaper
files, blocks and
compensator datato
Par Scientific, plan
and dose data to
picker AcQSim,
integrated with Varis
verification, ASCIIfile
to laser system,
Varian CadPlan plus
6.0

VARiS/Vision
database integration,
DICOMRT/3.0,other
image formats,
export field
coordinates to laser
system

The proposed device, the primary
predicate and reference deviceshave
identical data export capabilities with
DICOMformat.


Compatible with
data from any
DICOM compliant
scanners for the
applicable
modalities.

ARIA RadOnc
integration, DICOM
RT, other image
formats,
electromagnetic
digitizer, eclipse
scripting API
(ESAPI) read only
access, eclipse
scripting API
(ESAPI) write
access, eclipse
automation, Basic
RT prescription
informationavailable

DICOM includingRT
objects, CART
format, TIFF format,
CMP format,
Configurable pure
pixel data,
PortalVision MArk 1
& 2, Varian CT
option,
Electromagnetic
Digitilizer, Film
scanner

VARIs/Vision
database integration,
DICOMRT/3.0,other
image formats,
Electromagnetic
digitizer,filmscanner

The proposed device, the primary
predicate and reference deviceshave
identicalcompatibility(DICOMformat)


PrimaryPredicate
Ethos Treatment
Management,2.1;
Ethos Treatment
Planning,1.1

Referencedevice
Eclipse Treatment
PlanningSystem

The proposed device, primary predicate and
one of the reference devices (ART-Plan
v1.10.1)shareanAIdelineationmethod.

Multi-modal and
mono-modal.
Rigidanddeformable
Automatic and
manual initialization
(landmarks, fusion
box,alignment).
Registration for the
purposes of
replanning/

Registration:
CT (including
synthetic CT from
CBCT), MR and
PET

CT/MR/PET Image
Registration
4D image display
(registration of time
yesyes
seriesof3Dimages)

Theproposeddevice,theprimarypredicateand
most of the reference devices propose
registration of medical images of different
modalities.


recontouring and
AI-based automatic
contouring.

replanning/
recontouring
and AI-based
automaticcontouring.

Automatically
delineates OARs and
healthylymphnodes
Deep learning
algorithm.
Automatic
segmentation
includes the following
localizations:
* head and neck (on
CTimages)
* thorax/breast (for
male/female and on
CTimages)
* abdomen (on CT
images and MR
images)
* pelvis male (on CT
images and MR
images)
*pelvisfemale(onCT
images)
*brain(onCTimages
andMRimages)

Automatically
delineates OARsand
healthylymphnodes
Deep learning
algorithm
Automatic
segmentation
includesthefollowing
localizations:
* head and neck (on
CTimages)
* thorax/breast (for
male/female and on
CTimages)
* abdomen (on CT
images and MR
images)
* pelvis male (on CT
images and MR
images)
* pelvis female (on
CTimages)
*brain(onCTimages
andMRimages)

Eclipse also
includes tools for
treatment
preparation
(diagnostic image
and analysis,
contouring and
segmentation) and
planreview.

Geometrical shapes,
Manual editing and
manipulation tools,
Automatic
/semi-automatic
tools,
Automatic/semi-auto
matic on-demand
and post-processing
tools for individual
organs/structures,
Automatic
on-demand and
pre-processing tools
for multiple
organs/structures,3D
Autornargin, Logical
operators

Theproposeddevice,theprimarypredicateand
most of the reference devices propose
segmentation on medical images of different
modalitiesusingAI.

Window and level,
pan, zoom,
cross-hairs,slice
navigation.
Colorrendering,fused
views,galleryviews.

Window and level,
pan, zoom,
cross-hairs, slice
navigation.
Color rendering,
fused views, gallery
views.

Window and level,
pan, zoom,
cross-hairs,slice
navigation.
Maximum, average
and minimum
intensity projection
(MIP,AVG,MinIP),
colorrendering,
multi-planar
reconstruction(MPR),
fused views, gallery
views.


AI Based
autocontouring,
Registration based
contour projection
(re-contouring),
Manual ROI
manipulation and
transformation
(margins, booleans
operators,
interpolation).

AI based
autocontouring
Registration based
contour projection
(re-contouring)
Manual ROI
manipulation and
transformations
(margins, booleans
operators,
interpolation).

Both the proposed device and the primary
predicate allow AI automatic contouring and
manualcontouring.

Region/volu
meof
interest
measuremen
tsand
size
measuremen
ts

Intensity and
Hounsfieldunits.
Size measurements
include 2D and 3D
measurements
(number of slices,
volumeofastructure,
staticruler)

Intensity, Hounsfield
units, Size
measurement
include 2D and 3D
measurements
(number of slices,
volume of a
structure, static
ruler)

Intensity, Hounsfield
units and SUV
measurements
Size measurements
include 2D and 3D
measurements
(number of slices,
volumeofastructure,
staticruler)

The proposed device offers the same kind of
region/volumeofinterest
measurements and size measurements asthe
primarypredicate.


Intervariabilitycomparisonto
experts
CriterionC
Minsamplesizeforevaluation
method:20

Samplesize:25
whichisabovethe
minimumdatasample
size

Qualitativeevaluationbyexperts
CriterionD
Minsamplesizeforevaluation
method:15

Samplesize:20
whichisabovethe
minimumdatasample
size


Samplesize:38
whichisabovethe
minimumdatasample
size


Qualitativeevaluationbyexperts
Criterion1D
Minsamplesizeforevaluation
method:15

Rigid:A+B%=95.56%
Deformable:
A+B%=97.78%
Passed

Samplesize:45
whichisabovethe
minimumdatasample
size

Qualitativeevaluationof
propagatedcontoursbyexperts
Criterion1C
Minsamplesizeforevaluation
method:17

Samplesize:30
whichisabovethe
minimumdatasample
size

Qualitativeevaluationbyexperts
Criterion1D
Minsamplesizeforevaluation
method:17

Samplesize:27
whichisabovethe
minimumdatasample
size


DVHparameters<
0.199%in100%ofthe
cases
Passed

Samplesize:272total
Brain:42
H&N:70
Chest:44
Breast:26
Pelvis90


This document is intended to document the usability test
results for the ART-Plan v2.1.0 for compliance with IEC
62366-1:2015+AMD1:2020 - Medical devices -
Applicationofusabilityengineeringtomedicaldevices.

The ART-Plan was assessed with regards to usabilityfor
compliancewitheachsectionofIEC62366.

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

LiteratureReviewand
PerformanceCriteria
ExtractionReportforART-Plan
(V2.1.0)

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

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

The literature review was performed to estimate the
appropriate sample size of the testing data set towards
demonstrating the performance of theimageregistration,
segmentation and synthetic-CT generation solutions on
the basis of the most recent and most relevant scientific
literature.

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

Protocolforqualitative&
quantitativevalidationoffusion
performancesfor
tCBCT_sCT_replanning
modality(V2.0.0)

This study evaluated the quality of the rigid and the
deformable fusion algorithms of the SmartFuse module
forreplanificationofCT-basedtreatments.


Protocolforqualitative&
quantitativevalidationoffusion
performances for
tsynthetic-CT_sCT_replanning
modality(V2.1.0)

This study evaluated the quality of the rigid and the
deformable fusion algorithms of the SmartFuse module
forreplanificationofCT-basedtreatments.

Protocolforqualitative&
quantitativevalidationoffusion
performances for
tCT_ssynthetic-CT_replanning
modality(V2.1.0)

StudyProtocolandReport
Annotate Performances
Summary(V2.1.0)

Thepurposeofthisdocumentistodescribeallthetesting
protocols and testing results for validating the
performanceoftheAnnotatemodule.
To this aim, this document gathers information of all the
different testing conducted on Annotate for the CT, MR
and synthetic-CT from CBCT autosegmentation
algorithmsforv2.1.0:
● qualitative evaluation for autosegmentation
performance - pelvis male organs on
synthetic-CTfromCBCT-seesection18.13
● autosegmentation performances against
inter-expert variability - bowel loopstructureson
CT -seesection18.14
● qualitative evaluation for autosegmentation
performance - H&N lymph nodes (CT) - see
section18.15
● Non regression testing for integration in
ART-Plan v1.11.0 (CT and MR models) - see
section18.7
● Non regression testing for integration in
ART-Plan v2.1.0 (CT and MR models) - see
section18.16
Since all organs added in v.2.1.0 of Annotate have
passed at least onetestandmetatleastoneacceptance
criteria,allorganshavebeenreleased.

StudyProtocolandReport
Qualitative Validation of
AnnotateinART-Planforpelvis
male organs on synthetic-CT
fromCBCT(V2.1.0)

This test demonstrates that the module Annotate
provides acceptablecontoursfortheorgansevaluatedon
synthetic-CT from CBCT images of patients. All organs
that have passed the acceptance criterion of reaching a
percentage of at least 85% of A or B (qualitative
evaluation)havebeenreleasedinv.2.1.0.

Study Protocol and Report-
Autosegmentation
performances against
inter-expert variability - bowel
loopstructures(V2.1.0)

This test demonstrates that the module Annotate
provides clinically acceptable (compared to inter-expert
variability) for bowel loops structure. All organsthathave
passed theacceptancecriterionofreachingapercentage
of a DSC(mean)≥ 0.8 or DSC(mean)≥0.54) or
DSC(mean)≥mean(DSC inter-expert)+5% relative error
(quantitativeevaluation)havebeenreleasedinv.2.1.0.

StudyProtocolandReport
Qualitative Validation of
Annotate in ART-Plan for H&N
lymphnodes (V2.1.0)

This test demonstrates that the module Annotate
provides acceptablecontoursfortheorgansevaluatedon
CT images of patients. All organs that have passed the
acceptance criterion of reaching a percentage of at least
85%ofAorB(qualitativeevaluation)havebeenreleased
inv.2.1.0.


This test demonstrates equivalence between the version
v.2.1.0 and v.2.0.0 of the auto-segmentation models for
all structures and showsthattheupdatedmodelsprovide
clinically acceptable contours. All organs passed the
defined criteria, and were hence accepted for release in
thev.2.1.0.

Studyprotocolandreport
Dose engine measurements
validation (V2.1.0)

The evaluation demonstrated the dose calculation
accuracy by carrying out measurements on a Linac and
comparing the results with thedoseenginegivenvarious
metricsandacceptancecriteria.

Studyprotocolandreport
Dose engine clinical validation
(V2.1.0)

The evaluation demonstrated the non-inferiority of the
dose engine function of the AdaptBoxmoduleintermsof
dosimetric measures compared to other commercially
availabledoseengines.

Studyprotocolandreport
Dose engine performance
against reference devices
(V2.1.0)

The evaluation demonstrated the non-inferiority of the
dose engine function of the AdaptBoxmoduleintermsof
dosimetricmeasurescomparedtoFDAcleareddevices.

Studyprotocolandreport
Clinical validation of
synthetic-CTs from CBCT
(V2.1.0)

The evaluation demonstrated the non-inferiority of using
synthetic-CT from CBCT for treatment replanning in
terms of dosimetric measures as compared to CT-based
treatment replanning. Our synthetic-CT from CBCT for
pelvis has shown to produce results that meet the
acceptance criteria derived from clinical practice and
literaturereview.

Study protocol and report
Performance against Ethos
(Varian)(V2.1.0)

The evaluation demonstrated the non-inferiority of the
AdaptBoxmodulecomparedtoaFDAcleareddevice.

Studyprotocolandreport
Performance on US data
(V2.1.0)

The evaluation demonstrated the equivalent
performancesbetweennon-USandUSpopulation.

StudyProtocolandReportfor
CBCT-based synthetic-CT
evaluation(V2.1.0)

The evaluation demonstrated the anatomical and
geometricalofthesynthetic-CtgeneratedfromCBCT

AI-powered decision making
process for RT re-planning
(V2.1.0)

This study demonstrated that AdaptBox is an effective
tool to assist physicians and physicists in the decision
makingprocessforre-planning

The system verification and validation testing was
performedtoverifythesoftwareoftheART-Plan.


Predicate Device
FDA Clearance Number
Product
Manufacturer

equivalent to the following predicate device:


Reference Devices
FDA Clearance Number
Product
Manufacturer

MAGNETOM Aera with
software syngo MR XA30A
Please note: the Mobile Solution,
as part of MAGNETOM Aera with
software syngo MR XA30A, is the
reference device related to
modifications performed to fit the
subject device to the mobile
environment.

MAGNETOM Avanto Fit
with software syngo MR
XA50A
Please note: reference device
related to the cover

already cleared on the following reference devices:


Medical electrical equipment -
Part 1: General requirements for
basic safety and essential
performance (IEC 60601-
1:2005, MOD)

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

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

Medical devices - Application of
risk management to medical
devices

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

Medical device software -
Software life cycle processes

62304 Edition 1.1
2015-06
CONSOLIDATED
VERSION

Biological evaluation of medical
devices - part 1: evaluation and
testing within a risk
management process

Digital Imaging and
Communications in Medicine
(DICOM) Set

Determination of Signal-to-
Noise Ratio (SNR) in Diagnostic
Magnetic Resonance Images

Determination of Two-
dimensional Geometric
Distortion in Diagnostic
Magnetic Resonance Images


Determination of Image
Uniformity in Diagnostic
Magnetic Resonance Images

Acoustic Noise Measurement
Procedure for Diagnosing
Magnetic Resonance Imaging
Devices

Determination of Slice
Thickness in Diagnostic
Magnetic Resonance Imaging

Determination of Signal-to-
Noise Ratio and Image
Uniformity for Single-Channel,
Non-Volume Coils in Diagnostic
Magnetic Resonance Imaging
(MRI)

Characterization of the Specific
Absorption Rate for Magnetic
Resonance Imaging Systems

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

Determination of Local Specific
Absorption Rate (SAR) in
Diagnostic Magnetic Resonance
Imaging Systems

Quantification and Mapping of
Geometric Distortion for Special
Applications


Philips Healthcare (Suzhou) Co., Ltd.
No. 258, Zhongyuan Road, Suzhou Industrial Park,
Suzhou Jiangsu, CHINA, 215024
Establishment Registration Number: 3009529630
Additional Manufacturing Site:
Philips Medical Systems Technologies, LTD.
Advanced Technology Center MATAM, Building 34,
3100202 Haifa Israel.
Establishment Registration Number: 9617978

Primary Contact
Person:
Secondary Contact
Person

Shiguang An
Regulatory Affairs Engineer
Phone: +86-0-13940106467
E-mail: shiguang.an@philips.com
Erhong Wang
Senior Manager Regulatory Affairs
Phone : +86-0-13021019589
E-mail : erhong.wang@philips.com

Classification Regulation:
21CFR 892.1750


The CT 5300 on Trailer Kit has the same fundamental
design characteristics and technologies as the current
marketed Philips Incisive CT on trailer (K211168 -
November 22, 2021). The CT on Trailer configuration is
identical to the K211168 trailer configuration. The CT
system should only be used in designated locations for
use with appropriate radiation controls and safety
measures.
In addition to the above components and the software
operating them, each system includes hardware and
software for data acquisition, display, manipulation, storage
and filming as well as post-processing into views other than
the original axial images.
Upgrades Kit is available to upgrade earlier Incisive CT
installations to latest version.

The CT 5300 is a Computed Tomography X-Ray System
intended to produce images of the head and body by
computer reconstruction of x-ray transmission data taken at
different angles and planes. These devices may include
signal analysis and display equipment, patient and
equipment supports, components and accessories. The CT
5300 is indicated for head, whole body, cardiac and
vascular X-ray Computed Tomography applications in
patients of all ages.
These scanners are intended to be used for diagnostic
imaging and for low dose CT lung cancer screening for the
early detection of lung nodules that may represent cancer*.
The screening must be performed within the established
inclusion criteria of programs / protocols that have been
approved and published by either a governmental body or
professional medical society.
*Please refer to clinical literature, including the results of the
National Lung Screening Trial (N Engl J Med 2011;
365:395-409) and subsequent literature, for further
information.


design of the AI algorithm, the body joints detection
algorithm including CNN architecture, model parameters,
inference pipeline, pre- and post-processing is same as
what is used in the predicate Incisive CT. The original model
was trained using a broad dataset and performance data
using clinical images demonstrate the model can further
support more exams (cardiac, spine, runoff).
Evaluation/assessment of the modified Precise Position
algorithm, system level verification and validation activities
have been properly carried out to demonstrate it is as safe
and effective as the predicate to process the newly
integrated exams (cardiac, spine, runoff), hence the
modified Precise Position in the CT 5300 is substantially
equivalent to the predicate Incisive CT (K212441), which
integrates Precise Position (originally cleared in K203514).
Therefore, the proposed device is substantially equivalent to
the primary currently marketed and predicate device
Predicate Philips Incisive CT (K212441(April 27, 2022) in
terms of safety and effectiveness.

The proposed device did not require clinical study since
substantial equivalence to the legally marketed predicate
device was proven with the verification/validation testing,
bench testing, retrospective clinical data, and other
evidence as outlined


Proposed CT 5300
Predicate Philips
Conclusion
Conclusion

Identical.
Therefore, substantially
equivalent.

High resolution mode: 16
lp/cm
Standard resolution
mode: 13 lp/cm

0.27% at 120 kV, 230
mAs, 10 mm slice
thickness

Helical: 0.67mm – 5mm
Axial: 0.625 mm –
10.0mm

the proposed CT 5300 device and predicate Philips Incisive CT (K212441).


Compliance with NEMA
XR25, XR26, XR28 and
XR29

Compliance with NEMA
XR25, XR28 and XR29

Compliance with more
NEMA standard.
Safety and
effectiveness are not
affected.
Therefore,
demonstrating
substantial
equivalence.