CT VScore+ is a software
application intended for non-
invasive evaluation of calcified
lesions of the coronary arteries
based on ECG-gated, non-
contrast cardiac CT images for
patients aged 30 years or older.
The device automatically
generates calcium scores for the
coronary arteries (combined
LM+LAD, RCA, LCX) and
highlights the segmented calcium
on the original CT image. The
device also offers the option for
the user to display the calcium
scores in the context of reference
data from the MESA and Hoff-
Kondos databases.
The segmented arteries include
combined LM+LAD, RCA, and
LCX. To obtain separate LM and
LAD results, the user must
perform manual segmentation.
The segmentation map of
calcifications is intended for
informational use only and is not
intended for detection or
diagnostic purposes. The 3D
Calcium View output is provided
strictly as an informational and
supplementary output and should
never be used alone as the
method of reviewing the calcium
segmentation.

CardIQ Suite is a non-invasive
software application designed to
provide an optimized application
to analyze cardiovascular
anatomy and pathology based
on 2D or 3D CT cardiac non
contrast and angiography
DICOM data from acquisitions of
the heart. It provides capabilities
for the visualization and
measurement of vessels and
visualization of chamber mobility.
CardIQ Suite also aids in
diagnosis and determination of
treatment paths for
cardiovascular diseases to
include, coronary artery disease,
functional parameters of the
heart, heart structures and
follow-up for stent placement,
bypasses and plaque imaging.
CardIQ Suite provides calcium
scoring, a non-invasive software
application, that can be used
with non-contrasted cardiac
images to evaluate calcified
plaques in the coronary arteries,
heart valves and great vessels
such as the aorta. Calcium
Scoring may be used to monitor
the progression/regression of
calcium in coronary arteries
overtime, which may aid in the
prognosis of cardiac disease.

The subject device has
a more restrictive IFU
and includes a subset
of the predicate
functionality, non-
invasive evaluation of
calcified lesions of the
coronary arteries based
on ECG-gated, non-
contrast cardiac CT
images for patients
aged 30 years or older.
The predicate device
also performs other
functions, including
analysis of CCTA
images. This change in
the IFU does not create
a new intended use.

predicate device and the proposed device:


The device is intended for use in
adults aged 30 years and older. It
automatically generates calcium
scores for the coronary arteries
and highlights the segmented
calcifications on the original CT
images. The intended use is to
quantify coronary artery calcium
from ECG-gated, non-contrast
cardiac CT images to support
clinical interpretation of
c ardiovascular risk.

CardIQ Suite is a collection of
non-invasive software features
intended to analyze CT
cardiovascular anatomy and
pathology and aid in determining
treatment paths.

Subject device has a
more restrictive IFU,
subset of predicate
functionality. No new
intended use.

Non-contrast, ECG-gated cardiac
CT scans

Non-contrast, ECG gated cardiac
CT scans or coronary CT
angiography images (CCTA)

The subject device
processes noncontrast
cardiac CT coronary
artery calcium (CAC)
scans only, and does
not operate on CCTA
images.
CAC and CCTA scans
serve different clinical
functions. This does not
impact the safety or
effectiveness of the
subject device
compared to the
p redicate.

DICOM images with calcium
highlighted. Total calcium scores
as well as per-vessel scores for
the LM+LAD, LCX, and RCA

Comparison
of calcium
scores to
cited
literature

Similar. Both devices
offer the option for the
user to display the
calcium scores in the
context of data from
well-established
reference population
databases (MESA;
Hoff-Kondos).

Similar. Both devices
use deep learning
methods to segment the
coronary artery regions.

Deep learning methods used to
segment coronary regions and
assign calcium

Exact predicate
methods are unknown
but likely similar.

CT VScore+ Traditional 510(k) Submission


Semi-automatic; Software
initializes coronary calcium
results.

Interactive
definition of
ROIs and
assignment of
calcium

Users can manually edit results,
including manually assigning
calcifications to anatomical
structures

Same. Both devices
allow users to manually
edit calcifications and
assign them to labeled
anatomical structures

A 3D rendered image, showing
an optional supplemental view of
segmented calcium in relation to
full anatomy.

Subject device output is
supplementary only and
should not be used for
primary review.


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


Intended Use
Non-invasive
Same
Identical

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

Scan type
Ultrasound (ultrasound
Same
Identical

Algorithm
Deep Convolutional
Same
Identical


Clinically relevant
measurements of
known structure for
management support
in cardiac patients.
Measurement:
Distance between the
aortic annulus and the
PVAD inlet

Similar output with
different
measurements


Model retraining for enhanced output
accuracy and MLM performance,
refining annotations, and bounded
architecture optimization, to enhance
robustness, generalizability, and
performance, as part of the device
life cycle management.
No changes to device input or output

Verification testing will assess non-
clinical performance using the
established acceptance criteria. The
evaluation includes distance
measurement between the aortic
annulus and the PVAD inlet, clip
acceptability classification, inlet and
aortic-annulus visibility, and inlet and
aortic-annulus landmark position.
These metrics will be tested using the
same methods and thresholds defined

ensure substantial equivalence of the device after the modification.


for the authorized device to confirm
that retraining maintains performance
within the validated boundaries.
Software testing: Per FDA’s Guidance
“Content of Premarket Submissions for
Device Software Function”
Cybersecurity testing: Per FDA’s
Guidance “Cybersecurity in Medical
Devices: Quality System
Considerations and Content of
Premarket Submissions”.

MLM will include datasets with
Percutaneous Ventricular Assist
Devices, that were not previously tested
with the authorized device, in order to
expand the use of the PVAD IQ software.
The additional PVAD devices should be
FDA approved, indicated to support the
left ventricle, and within the device
patient population.

Verification testing will assess non-
clinical performance using the
established acceptance criteria. The
evaluation includes distance
measurement between the aortic
annulus and the PVAD inlet, clip
acceptability classification, inlet and
aortic-annulus visibility, and inlet and
aortic-annulus landmark position.
These metrics will confirm that adding
new PVAD devices maintains
performance within the established
boundaries.
Software testing: Per FDA’s Guidance
“Content of Premarket Submissions for
Device Software Function”
Cybersecurity testing: Per FDA’s
Guidance “Cybersecurity in Medical
Devices: Quality System
Considerations and Content of
Premarket Submissions”.

MLM will include datasets from other
cardiac ultrasound views (AP3, AP5, and
Subcostal LVOT), allowing healthcare


The subject device is substantially equivalent to the predicate device in the following ways:


InferCare RECIST is a post-
processing software
application used to display,
process, analyze, quantify
and manipulate multi-time-
point CT images. It is
intended to be used by
trained medical
professionals in evaluating
and managing tumors in
various organs, tissues, and
other anatomical structures
based on RECIST criteria.
InferCare RECIST offers
functionalities for lesion
measurement, registration,
tracking, and RECIST
report. It provides tools for
interactive segmentation,
and 3D reconstruction
visualization.
The software utilizes
artificial intelligence
algorithms for automated
lesion segmentation and
registration, and the results
require confirmation by a
medical professional. The
software's artificial
intelligence algorithms are
intended for patients aged 21
years and older.

Multi-Modality Tumor
Tracking (MMTT)
application is a post
processing software
application used to display,
process, analyze, quantify
and manipulate anatomical
and functional images, for
CT, MR PET/CT and
SPECT/CT images and/or
multiple time-points. The
MMTT application is
intended for use on tumors
which are known/confirmed
to be pathologically
diagnosed cancer. The results
obtained may be used as a
tool by clinicians in
determining the diagnosis of
patient disease conditions in
various organs, tissues, and
other anatomical structures.

Same. Both
devices are
used for
medical image
processing to
display,
process,
analyze,
quantify,
manipulate
multi-time-
point images
and enable
RECIST 1.1
categorization.

Radiologists, oncologists,
and others trained in reading
medical images and
applying RECIST criteria

Healthcare facilities such as
hospitals and clinics

CT, MR, PET/CT and
SPECT/CT DICOM images

Similar. The
predicate
device includes
a wider variety


Registration
Automatic and manual
Automatic and manual
Same

Automatic
software
measurement of
segmented lesion

Long axis, short axis,
volume, key slice.

Long axis, short axis,
volume, key slice, density,
etc.

The algorithm
performance of
the subject
device has been
validated
through
performance
testing.


Segmentation Accuracy
Long/Short Diameter

Item
N
Mean (Dice)
95% CI
MAE (%)
95% CI

node lesions. The result is listed below:


BrightHeart
7-11 boulevard Haussmann
Paris 75009, France

Christophe Gardella
Chief Technical Officer
Tel. +0033686543950
Email. christophe@brightheart.fr

Submission Correspondent:
Christophe Gardella


Device Trade Name:
Fetal EchoScan (v1.2)

Device Common Name:
Medical image analyzer

Radiological computer-assisted diagnostic software for
lesions suspicious for cancer 21 CFR 892.2060


Software algorithm
Machine Learning Model
Machine Learning Model

Imaging Modality
Fetal Ultrasound
Fetal Ultrasound


Fetal ultrasound studies containing
the following views in recordings: 4
chamber, left ventricular outflow
tract, right ventricular outflow tract

Fetal ultrasound recordings
containing the following views: 4
chamber, left ventricular outflow
tract, right ventricular outflow tract

Suspicious radiographic findings
categorized into 2 groups:
● “classification” features are
based on the identification of
morphological features within
the video clip.
● “measurement” features are
based on the detection and
segmentation of key anatomic
points

Video clips featuring the fetal heart.
Identifiable suspicious radiographic
findings of the fetal heart:
● overriding artery
● septal defect at the cardiac crux
● abnormal relationship of the
outflow tracts
● enlarged cardiothoracic ratio
● right ventricular to left
ventricular size discrepancy
● tricuspid valve to mitral valve
annular size discrepancy
● pulmonary valve to aortic valve
annular size discrepancy
● cardiac axis deviation

Identifiable suspicious radiographic
findings of the fetal heart
● overriding artery
● septal defect at the cardiac crux
● abnormal relationship of the
outflow tracts
● enlarged cardiothoracic ratio
● right ventricular to left
ventricular size discrepancy
● tricuspid valve to mitral valve
annular size discrepancy
● pulmonary valve to aortic valve
annular size discrepancy
● cardiac axis deviation

For each frame the software
evaluates whether the findings are:
present, absent, or inconclusive.
An “exam summary table” displays
a summary of the results for the
overall study.

For each frame the software
evaluates whether the findings are:
present, absent, or inconclusive.
A “record summary table” displays
a summary of results for each video
clip. An “exam summary table”
displays a summary of the results for
the overall study.

● Annotated DICOMs within the
user PACS viewer
● third-party user interface

● Annotated DICOMs within the
user PACS viewer
● Device web interface


analyzed finding but possibly positive to other findings.


No.
Specification
Predicate Device (Primary)
Device Under Evaluation
Equivalency

syngo.via RT Image Suite (VB80)
Syngo.via RT Image Suite VC10
Syngo.via RT Image Suite VC10

Image analysis software for evaluating image
data sets and preparing them for further use
in therapy.

syngo.via RT Image Suite is intended to be
used by trained medical professionals
including, but not limited to, medical
physicists, RT technologists, physicians,
radiologists, nuclear medicine physicians, and
radiation oncologists.
syngo.via RT Image Suite is a medical
application for viewing, manipulation, 3D and
4D visualization, and comparison of medical
images from multiple imaging modalities. The
application enables the registration of images
and provides tools to help the user to identify
volumes, regions and points of interest inside
the patient anatomy. The application also
enables the creation of simple geometric
plans. The application may assist in the
preparation of further radiation therapy
treatment planning. The application supports
anatomical datasets from CT, MR, CBCT, as

Comparison of Subject Device to Predicate Device syngo.via RT Image Suite VC10 and syngo.via RT Image Suite VB80


• Visualization and contouring of moving
tumors and organs
• Management of points of interest
including but not limited to the isocenter
• Creation of simple geometric treatment
plans
• Generation of a synthetic CT based on
multiple pre-define MR acquisitions

• Visualization and contouring of moving
tumors and organs
• Management of points of interest including
but not limited to the isocenter
• Creation of simple geometric treatment
plans
• Generation of a synthetic CT based on pre-
define MR acquisitions

Clinical condition
intended to
diagnose, treat or
manage

The intended patient population is not
subject to any restrictions. However,
automation support provided works best
with adult patients.

The intended patient population is not
subject to any restrictions. However,
functionality like Auto-Contouring and Lung
Ventilation work best with adult patients.

Substantially
Equivalent
minor
deviation in
wording

There are no known specific situations that
contraindicate the use of this device.

The task is designed to be used embedded in
the scanner console or in a workstation next
to it to perform patient virtual simulation in
the radiation therapy context. It can also be
used in the contouring room or in the
physicians' office to perform treatment
preparation (registration, contouring,

The task is designed to be used embedded in
the Reading/Control Room on a scanner
console or in a workstation next to it to
perform patient virtual simulation in the
radiation therapy context. It can also be used
in the Reading Room (contouring room or in
the physicians' office) to perform treatment
preparation (registration, contouring,


adaption) or treatment assessment (decision
on treatment strategy).

Trained medical professionals including, but
not limited to,
medical physicists, RT technologists,
physicians, radiologists, nuclear medicine
physicians, and radiation oncologists.

Qualified persons with the necessary
knowledge in accordance with country-
specific regulations, for example,
radiotherapy technologists, medical
physicists, clinical administrators, or radiation
oncologists

VRT, MPR, MIP, thin VRT, thick MPR,
Interaction: zoom, pan, rotate

Parallel Image Display, Image Visualization,
Image Fusion, Correlated Cursors

Distance line, ROI, VOI, pixel lens, angle;
findings

Rigid registration of images of the same
patient acquired with the same or different
modalities within the same or different
imaging sessions. The transformation includes
only translation and rotation (6 degrees of
freedom).

Deformable registration of images of the same
patient acquired with the same or different
modalities within different imaging sessions.


sessions. The transformation allows for local
deformation to adapt to changing anatomy
(many degrees of freedom). The feature
includes a tool for visual quality assurance.

Routine Contouring tools (e. g. freehand
drawing tools, creation of margins, tool to
convert isodose lines from a DICOM RT dose
file to contours etc.)

Routine Contouring tools (e. g. freehand
drawing tools, creation of margins etc.)
A tool was added to convert isodose lines
from a DICOM RT dose file to contours.

Advanced Contouring tools (automatic
contouring of structures, nudge 3D tool, etc.).
Support of Rapid Results Technology.
Streamlined workflow to adapt contours
from a prior to a current planning CT
(“adaptive contouring”).
Additional structures and organs were added
for Auto Contouring to extend the organ
coverage and included additional
segmentation guidelines for CT images and
Inclusion of MR Images (MR autocontouring
including brain metastasis) (The underlying
deep learning technology for this extension is
unchanged. It reuses the same technology
which was available in prior versions of the
auto segmentation feature).


Brain metastasis specific contouring model is
limited to previously diagnosed metastases,
using deep learning segmentation. Note:
Brain metastases – only on MR Images)

Contouring tools on 4D image data (e. g.
display of a cine loop of images acquired
through gated CT etc.)

Structure editing tools (e. g. expansion or
shrinking of a structure).

Duplication of structures and POIs inside the
same structure set.

• Loading and storing of DICOM RT structure
sets, creating, editing and deletion of
structures and POIs.
• Creating, editing and deletion of structure
templates.
• Customize predefined structure database
with mapping to international
nomenclature schemes.

Sending of reference points with offset
details to a laser system.

Sending and receiving of reference points
with offset details and coordinates to and


from a movable laser system.
• Inclusion of a simplified workflow to place
isocenters in the spine
• Consistent use of user-configured default
names throughout the application

Loading of any existing dose files; addition or
subtraction of two dose; show Dose Volume
Histograms

Creation of new geometric treatment
plans for photon radiotherapy

Generation of CT-density image series (2D
model) out of multiple MR-image series for
Pelvis and Brain.

New Synthetic CT algorithm (3D model) for
Pelvis and Brain was provided

Calculation of lobe-based lung ventilation
from an inspiration and expiration CT scan

User Interface
syngo.via based GUI
syngo.via based GUI
Equivalent

Archiving / Storing
MOD, CD-R, film; DVD
MOD, CD-R, film; DVD
Equivalent

Communication
DICOM Compatible
DICOM Compatible
Equivalent

Model support
CT Scanners, syngo.via platform
CT Scanners, syngo.via platform
Equivalent


Specification
Reference Device
Device Under Evaluation
Equivalency

VBrain (K203235)
syngo.via RT Image Suite VC10

Assists trained medical
professionals in RT planning by
providing initial contours for
known brain tumors.

Software for multimodality viewing,
registration, and autocontouring
including brain metastases.

Provides initial GTV contours
requiring clinician review.

Provides editable automated contours;
clinician review required.

Comparison of Contour Metastases for VBrain and syngo.via RT Image Suite VC10


Brain metastases, meningiomas,
acoustic neuromas.

Imaging Modalities
T1 CE MRI
CT, MRI, CBCT, PET/CT, etc.
Similar

AI/ML Technology
Deep learning neural networks.
3D UNet/DI2IN-based DL models.
Similar

User Control
Fully editable
Fully editable
Similar

False Positive Rate
0.681 tumors/case
1.30
Similar

Hausdorff Distance
5.0% in terms of lesion size
1.41 mm (HD95)
Similar


Semi-automated isocenter estimation / laser-based isocenter definition (breast)

Semi-automated isocenter estimation / laser-based isocenter definition (vertebra)

subjects, distributed across the following models and features:


Europe: 110, US: 149, Canada: 56, South America: 83, Australia:
29, Asia: 33, unknown: 13

Head&Neck: 114, Thorax&Abdomen: 266, Pelvis: 93

Gender
Male: 199, female: 216, Unknown: 58

Siemens: 148, GE: 100, Philips: 149, unknown/others: 76

Distribution of test data across subgroups for CT auto-contouring


Test results: passed for all organs. Table below shows the performance results.


LN Level Ib Submandibular Triangle Right

LN Level IVb Medial Supraclavicular Group
Left

LN Level IVb Medial Supraclavicular Group
Right

LN Level V Posterior Triangle Group Left

LN Level V Posterior Triangle Group Right

LN Level Vc Lateral Supraclavicular Group
Left

LN Level Vc Lateral Supraclavicular Group
Right

LN Level VIb Prelaryngeal, Pretracheal, &
Paratracheal Nodes

LN Level VIIa Retropharyngeal Nodes Left

LN Level VIIa Retropharyngeal Nodes Right


LN Level Xa Retroauricular & Subauricular
Nodes Left

LN Level Xa Retroauricular & Subauricular
Nodes Right


CA Left Anterior Descending Artery (LAD)


1 structures
with secondary guideline suppor
t added in
subject de
vice.


Test result: Passed for all new organs. The quantitative performance is shown in the table below.


N2 Station 1: Highest Mediastinal
Nodes Left

N2 Station 1: Highest Mediastinal
Nodes Right

N2 Station 2: Upper Paratracheal
Nodes Left

N2 Station 2: Upper Paratracheal
Nodes Right

N2 Station 4: Lower Paratracheal
Nodes Left

N2 Station 4: Lower Paratracheal
Nodes Right

N2 Station 9: Pulmonary Ligament
Nodes Left

N2 Station 9: Pulmonary Ligament
Nodes Right


Robustness assessment of
structures/OAR for different
image contrasts

and image resolutions, grouped by body region are shown in the tables below.


Robustness assessment of structures for
different
image resolutions


Distribution of test data across subgroups for MR auto-contouring of brain metastasis


Data Source
USA: 26, EU: 35, Unknown: 20

Gender
Male: 30, female: 31, Unknown: 20


US (4 sites): 78; Europe (7 sites): 75; Australia (1 site): 1

Manufacturer
Siemens: 66, GE: 18, Philips: 69

Distribution of test data across subgroups for MR auto-contouring of pelvis OAR


Test result: Passed for all organs. Quantitative performance results for both Brain and Pelvis OARs are shown in the tables below.


Structure (Pelvis #
Test
9
5th %
9
5th %

syngo.via RT Image Suite VC10
Page 37 o
f 42


Data Source
US: 17; Europe: 18; Unknown: 16

Distribution of test data across subgroups for synthetic CT


MAGNETOM Aera: 24, MAGNETOM Skyra: 5, MAGNETOM Vida:
13, MAGNETOM Sola: 1, MAGNETOM Sola Fit: 1


Europe: 142, US: 126, Asia: 131, unknown: 27

Head&Neck: 113, Thorax&Abdomen: 264, Pelvis: 92

Gender
Male: 187, female: 74, Unknown: 165

Distribution of test data across subgroups for Semi-automated isocenter estimates or defining isocenters via RTP lasers


Siemens: 369, GE: 10, Philips: 28, Toshiba: 2; NeuoLogica: 14
unknown/others: 3


Medical devices - Application of risk management to medical
devices

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

Information supplied by the manufacturer of medical devices

Medical Device Software - Software Lifecycle processes


Application of Usability Engineering to Medical Devices

Health software Part 1: General requirements for product
safety

Radiotherapy Equipment, Coordinates, Movements and
Scales

Medical electrical equipment – Requirements for the safety
of radiotherapy treatment planning systems

Standard for Software Cybersecurity for Network-
Connectable Products, Part 1: General Requirements

Software Cybersecurity for Network-Connectable Products,
Part 2-1: Particular Requirements for Network Connectable
Components of Healthcare and Wellness Systems

Health Software and Health IT Systems Safety, Effectiveness
and Security - Part 5-1: Security - Activities In The Product
Life Cycle


Note: Update to predicate Device only in Section Data Collection:


- Systems: GE Voluson V730, P8, S6/S8, E6, E8,
E10, Expert 22, Philips Epiq 7G
- Formats: Still images were obtained in DICOM
& JPEG format, cine loops in RAW data
format.
- Countries: UK, Austria, India and USA
- For training 122,711 labelled source images
from 35,861 patients
- For testing the following number of images
were used:
SonoLyst 1st Trim IR: 7970
SonoLyst 1st Trim X: 4931
SonoLyst 1st Trim Live: 9111
SonoBiometry CRL: 243
- For Probegroup CL2 (which includes C1-6-D
Probe) Data was collected from 396 patients.
Data was sampled so that a maximum of one sample
was taken per patient per view. The final samples
evaluated were:
Images per dataset for
CL2 Probetypes
View Category
SonoLystIR SonoLyst
& X Live
Sagittal Fetus
(Crown rump 133 164
length)
Sagittal Bladder 140 142
Sagittal Head 153 164
148 164
Trans-thalamic 132 164
Axial orbits 149 164
Coronal orbits 164 164
Coronal palate 150 164
Coronal lips 147 165
Axial heart 155 165
Axial cord
163 163
insertion
Axial abdomen 133 164
Axial kidneys 164 164
Axial bladder 161 164
Coronal kidneys 122 164
Sagittal spine 72 164

510(k) Premarket Notification Submission


Upper limbs 115 164
Lower limbs 130 164
Others 168 180
Total images 2699 3111
- Demographic distribution including Gender,
Age, and Ethnicity:
We are not able to provide exact demographic
distributions due to data privacy constraints.
Where collected, patient age is grouped into <20, 20-
29, 30-39, 40-49, >50. The data set contains known
patient ages from <20 till 30-39 and has been limited
to reproductive ages of at least 18 years of age.
Data was collected from 396 patients for CL2
evaluation, with 201 (51%) patients from USA sites and
195 (49%) from European sites.
From our earlier evaluation across the probe groups,
we also conducted a subset evaluation across different
regions. In all cases the confidence intervals cross zero,
indicating no statistically significant difference. These
findings demonstrate that the algorithm performs
consistently across regions.
See also Table: “performance across regions”

In general for Sonolyst Software:
To ensure the quality of the curated data for
verification, the following strategy is employed:
1. The images were curated (sorted and
graded) by a single sonographer
2. The images were sorted and graded by
SonoLyst IR/X First Trimester.
This process resulted in some images
being reclassified during sorting.
3. The sorting process resulted in some
images being reclassified based upon the
majority view of the panel.Where they
differed from the ground truth, the
graded images from step 1 were
reviewed by a 5-sonographer review
panel, in order to determine the grading
accuracy of the system
especially for C1-6-D probe impmentation:
A standardized imaging protocol was used based on
internationally recognized guidelines to ensure
consistency and quality across all scans. Specifically,
the following sources were used to inform the
protocol:


• AIUM Practice Parameter for the
Performance of Standard Diagnostic
Obstetric Ultrasound
This U.S.-based guideline defines the
minimum criteria for a complete first-
trimester scan, with flexibility for
transabdominal or transvaginal approaches.
• AIUM Detailed Protocol (12 Weeks 0 Days to
13 Weeks 6 Days)
Used in cases where abnormalities were
suspected or in high-risk pregnancies, this
protocol provides more detailed criteria for
diagnostic imaging.
• ISUOG Practice Guidelines: Performance of
First-Trimester Fetal Ultrasound Scan
This guideline outlines the standard approach
for confirming fetal viability, accurately
establishing gestational age and determining
the number of fetuses.
• ISUOG Detailed Protocol
Applied in cases involving higher-risk
pregnancies, this protocol provides additional
detail to ensure comprehensive assessment
during the first-trimester scan.
• Routine First-Trimester Ultrasound Screening
Using a Standardized Anatomical Protocol
(Yimei Liao et al.)
This study informed the development of a
routine first-trimester scan protocol for
detecting structural abnormalities.
Test data was collected on:
GEHC Voluson Expert 22
GEHC Voluson Expert 10

All training data is independent from the test data at a
patient level.
A statistically significant subset of the test data is
independent from the training data at a site level, with
no test data collected at the site being used in training.
Independence of the test data has been insured by
collecting test data only from sites which were not
i nvolved in the collection of data used for training.


Medical image management and processing system


Manufacturer
Galileo CDS Inc
Galileo CDS Inc

Medical image management
and processing system.

Automated Radiological Image
Processing Software

MR imaging data post
processing software


GBrain MRI is a post
processing medical device
software intended for
analyzing and quantitatively
reporting signal
hyperintensities in the brain on
T2w FLAIR MR images and
T1w post contrast images in
the context of diagnostic
radiology.
GBrain MRI is intended to
provide automatic
segmentation, quantification,
and reporting of derived image
metrics. It is not intended for
detection or specific diagnosis
of any disease nor for the
detection of signal
hyperintensities.
GBrain MRI should not be
used in-lieu of a full evaluation
of the patient’s MRI scans. The
physician retains the ultimate
responsibility for making the
final patient management and
treatment decisions.

GBrain MRI is a post
processing medical device
software intended for
analyzing and quantitatively
reporting signal
hyperintensities in the brain
on FLAIR MR images in the
context of diagnostic
radiology.
GBrain MRI is intended to
provide automatic
segmentation, quantification,
and reporting of derived
image metrics. It is not
intended for the detection or
specific diagnosis of any
disease nor for the detection
of signal hyperintensities.
GBrain MRI should not be
used in-lieu of a full
evaluation of the patient’s
MRI scans. The physician
retains the ultimate
responsibility for making the
final patient management and
treatment decisions.

The indications for use are identical
to the predicate device with the
addition of post-contrast images.
The subject device analyzes signal
hyperintensities regardless of
location similar to the predicate.
As with the predicate, the subject
device has a general indication and
is not specific to a particular
disease or condition.

Hospital, Clinic, Imaging
Center, Medical Offices

Adult patients aged 21
and above with a brain MRI
study.

Radiologists, Imaging
Professionals, and other
Clinicians working with
radiological images. The
application should be
used as a support
tool in assessment of
structural MRIs. Patient
management decisions should
not be based solely on the
results of the device.


Automated segmentation using
deep learning followed by
volume calculations, and
report generation.
Results displayed on PACS.

Software package- Operates on
off-the-shelf hardware
(multiple vendors).

Software package- Operates
on on off-the-shelf hardware
(multiple vendors).

Supports DICOM format as
input from the MRI scanner or
the PACS.

Supports DICOM format as
input from the MRI scanner.

Provides volumetric
measurements of regions with
hyperintense signal on T2w
FLAIR and post contrast T1w
images.
Includes segmented color
overlays and volumetric
reports

Provides volumetric
measurements of regions with
hyperintense signal on T2w
FLAIR images.
Includes segmented color
overlays and volumetric
reports

The subject device extends the
functionality of the predicate
device to include the measurement
of contrast enhancing regions on
T1w images.

Results displayed in text and
graphical formats

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


Distribution of Validation cases across Age, Gender, and Ethnicity are shown in the tables below.


Contrast Enhancement Measurement performance related to Hyperintensity Size


Contrast Enhancement Measurement performance related to Hyperintensity Brightness


Indications for Use
Please type in the marketing application/submission number, if it is known. This
K252366 ?
textbox will be left blank for original applications/submissions.
Please provide the device trade name(s). ?

Please type in the marketing application/submission number, if it is known. This
textbox will be left blank for original applications/submissions.

Please provide your Indications for Use below.

a2z-Unified-Triage is a radiological computer-aided triage and notification software indicated for use in the
analysis of abdominal/pelvic CT images in adults aged 22 and older. The device is intended to assist
hospital networks and appropriately trained medical specialists in workflow triage by flagging and
communicating suspected positive cases of the 7 specified abdominopelvic findings: Acute Cholecystitis,
Acute Pancreatitis, Unruptured Abdominal Aortic Aneurysm, Acute Diverticulitis, Free Air, Hydronephrosis,
and Small Bowel Obstruction. These findings are intended to be used together as one device. The device
supports both cloud-based and on-premises deployment, with integration either directly with healthcare
facility systems or through third-party healthcare technology platforms.
a2z-Unified-Triage uses an artificial intelligence algorithm to analyze images and flag cases with detected
findings in parallel to the ongoing standard of care image interpretation. The device provides analysis
results that enable client systems to generate notifications for cases with suspected findings. These results
can include DICOM instance UIDs for key images, which are meant for informational purposes only and not
intended for primary diagnosis 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 a2z-Unified-Triage are intended to be used in conjunction with other patient information and
based on clinicians' professional judgment, to assist with triage/prioritization of medical images. Notified
clinicians are resoonsible for viewinQ full imaQes per the standard of care.
Please select the types of uses (select one or both, as [gJ Prescription Use (Part 21 CFR 801 Subpart D)
D ?
applicable). Over-The-Counter Use (21 CFR 801 Subpart C)


The uMR 680 system is indicated
for use as a magnetic resonance
diagnostic device (MRDD) that
produces sagittal, transverse,

Table 1 Comparison of Indication for Use


Type of Magnet
Superconducting
Superconducting
Same

Resonant frequencies
63.87 MHz
63.87 MHz
Same


Device Change
Risks
Verification/Validation Method(s)
Acceptance Criteria
Summary of Results

 Biocompatibility
 EMC-immunity,
electrostatic discharge
 SNR and Uniformity
 Surface Heating
 Clinical image quality
 General
electrical/mechanical
safety
 SAR

 Biocompatibility evaluation in
agreement with
recommendations in Use of
International Standard ISO
10993-1, "Biological
evaluation of medical devices -
Part 1: Evaluation and testing
within a risk management
process".
 Perform test according to IEC
60601-1-2 and IEC 60601-4-2.
 Perform test according to
NEMA MS 1, NEMA MS 3,
NEMA MS 6 and NEMA MS
9.
 Perform test according to
NEMA MS 14.
 Evaluate the image generated
by RF coils: Tx/Rx Head Coil
with the same method as the
predicate device.
 Perform test according to
ANSI/AAMI ES60601-1.
 Perform test according to IEC
60601-2-33 and NEMA MS 8.

 Materials of construction and
manufacturing materials do not
introduce chemicals that raise a
biocompatibility concern.
 Conform with IEC 60601-1-2 and
IEC 60601-4-2.
 SNR and Uniformity shall fulfill
with the design specification.
 The maximum temperature of all
temperature probes shall not
exceed 41℃.
 Image quality is sufficient for
diagnostic use.
 Conform with ANSI/AAMI
ES60601-1.
 Conform with IEC 60601-2-33.

 Biocompatibility testing is
not needed because device
does not introduce a
biocompatibility risk.
 Pass
 Pass
 Pass
 The U.S. Board Certified
radiologist confirms that
image quality is sufficient
for diagnostic use.
 Pass
 Pass

The following performance data were provided in support of the substantial equivalence determination.


GE Medical Systems, LLC
3200 N. Grandview Blvd.
Waukesha, WI 53188

Andrew Turner
Regulatory Affairs Leader
484-630-7798
Andrew.Turner@gehealthcare.com

Glen Sabin
Regulatory Affairs Director
262-894-4968
Glen.Sabin@gehealthcare.com

Classification Name
Magnetic Resonance Diagnostic Device

Predicate Device(s)
AIR Recon DL (K213717)

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


Please type in the marketing application/submission number, if it is known. This
textbox will be left blank for original applications/submissions.

Please provide the device trade name(s).

Please provide your Indications for Use below.

.
xt
JLK-NCCT is a radiological computer-aided triage and notification software designed for analyzing non-
.
contrast head CT (NCCT) images. The software assists hospital networks and trained clinicians by flagging
He
and communicating them of findings suggestive of (1) Intracranial Hemorrhage (ICH) and (2) large vessel
lp
occlusion (LVO) involving the internal carotid artery (ICA), middle cerebral artery M1 (MCA-M1) and middle
Te
cerebral artery M2 (MCA-M2) on NCCT images.
xt
JLK-NCCT employs an artificial intelligence (AI) algorithm to analyze images and highlight cases with
detected (1) ICH or (2) LVO on an on-premises or cloud-based JLK server. This occurs in parallel with the
ongoing standard of care image interpretation. Users receive notifications for cases with suspected ICH and
LVO findings via mobile devices. Notifications include compressed preview images for informational
purposes only and not intended for diagnostic use beyond notification.
The device does not modify the original medical image, and is not intended to be used as a primary
diagnostic device. The results of JLK-NCCT are intended to be used in conjunction with other patient
information and professional judgment to assist with triage and prioritization of medical images. Clinicians
who receive notifications are responsible for reviewing full images per the standard of care. JLK-NCCT is
intended for adults use only.
Limitations and warnings:
• All patients should receive appropriate care, including a CT angiography (CTA) and/or relevant treatments
as part of the standard stroke workup. The device is not intended to rule out any medical conditions,
therefore, cases without a “Suspected ICH” or "Suspected LVO" notification should not be assumed to
exclude ICH or LVO. All cases should undergo a CTA as part of the standard stroke workup.
• The device does not replace the need for CTA or MRA in ischemic stroke workup, it provides workflow
prioritization and notification only.
Contraindications/Exclusions:
• Patient Motion: excessive motion leading to artifacts that make the scan technically inadequate.
• Hemorrhagic Transformation
• Very thin or no Ventricles

Please select the types of uses (select one or both, as
applicable).



Contact Email
john.smith@hoganlovells.com


Common Name
Radiological computer aided


Classification Name
Radiological computer aided

lly M
arketed Predicate Devices 21 CF
R 807.92(a)(3)


Predicate Trade Name
Rapid NCCT Stroke


Sonio Detect is intended to analyze fetal ultrasound images and clips using machine learning
techniques to automatically detect views, detect anatomical structures within the views and verify
quality criteria and characteristics of the views.
The device is intended for use as a concurrent reading aid during the acquisition and interpretation of fetal
ultrasound images.


Sonio
147 rue d’Aboukir,
75002, Paris France

Florian Akpakpa
Director Regulatory Affairs and Compliance Officer
Sonio
Phone: +33 6 66 66 19 89
Email: florian.akpakpa@sonio.ai


21 CFR 892.1550 - accessory to Ultrasonic Pulsed Doppler Imaging System
21 CFR 892.1560 - accessory to Ultrasonic Pulsed Echo Imaging System
21 CFR 892.2050 - Medical Image Management and Processing System


1. Choroid Plexus
2. Profile/Nuchal translucency
3. Abdominal circumference
4. Hand
5. Foot
6. Crown Rump Length
7. Doppler 3VT
8. Lower Arm
9. Upper Leg
10. Lower Leg
11. Doppler 4CH
12. Adnexa
13. Ovaries
14. Umbilical cord insertion

1. Transthalamic or Cavum septum pellucidum or Midline
falx/Transventricular or Choroid Plexus
2. Transcerebellar view
3. Profile
4. Lips and Nose
5. Orbits
6. 4 Chambers
7. LVOT
8. Sagittal Spine
9. Abdominal circumference
10. Axial Bladder
11. Axial Kidneys
12. Cervix/Placenta
13. Hand
14. Foot
15. External genitalia (female or male)
16. 3 Vessels/3 Vessels and Trachea (3VV)
17. 3 Vessels and Trachea (3VT)
18. Doppler 3VT

Table 1: List of views per trimester that can be automatically detected by Sonio Detect


19. Humerus
20. Femur
21. Radius/Cubitus
22. Tibia/Fibula
23. Upper arm
24. Lower arm
25. Upper leg
26. Adnexa
27. Ovaries
28. Placenta Cord Insertion
29. Coronal Face
30. Doppler 4CH
31. Umbilical Cord Insertion
32. Coronal Kidneys
33. Diaphragm
34. Maxilla
35. Mandible
36. Aortich Arch
37. Ductal Arch
38. Bicaval View
39. Palate
40. Corpus Callosum


Transthalamic
Transventricular
Transcerebellar
Choroid plexus

4 chambers
3VV
3VT
RVOT
LVOT
Abdominal
circumference
Axial view of the
kidneys
Diaphragm
Crown Rump length
Abdominal cord
insertion


Pulmonary artery trunk on 3 vessels View

Pulmonary artery with visible bifurcation


Thalami/cerebral peduncles on axial view


Quality criteria
1st trimester
2nd/3rd Trimester

Presence of the cavum septum
pellucidum or of the pillars of
the fornix

Table 3: List of quality criteria that can be automatically verified by Sonio Detect


Table 4 : List of characteristics that can be automatically verified by Sonio Detect


Items
Predicate device: Sonio Detect v2
Proposed device : Sonio Detect v3

21 CFR 892.1550 - accessory to
Ultrasonic Pulsed Doppler Imaging
System
21 CFR 892.1560 - accessory to
Ultrasonic Pulsed Echo Imaging
System
21 CFR 892.2050 - Medical Image
Management and Processing System

Table 5: Comparison of technological characteristics


- Sonio Detect automatically detects
views
- Sonio Detect automatically detects
anatomical structures within the
supported views
- Sonio Detect automatically verifies
the quality criteria and characteristics
of the supported views.

- Sonio Detect automatically detect
views
- Sonio Detect automatically detect
anatomical structures within the
supported views
- Sonio Detect automatically verifies
the quality criteria and characteristics
of the supported views.
- Sonio Detect automatically localizes
views and anatomical structures

Artificial Intelligence
Lecture of biometrics
Colorimetry for 3D and Doppler

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


Items (fetal ultrasound views, anatomical structures and characteristics
automatically detected)

Automatic detection of 14 T1 fetal ultrasound images

Automatic detection of 40 T2/T3 fetal ultrasound images

Automatic localization of 1 view (T1)
mIoU: 0.777 (0.743-0.811)

Automatic detection of 1 fetal brain anatomical structure on the view “Transthalamic” at
T1

Automatic detection of 7 fetal brain anatomical structures on the views “Transthalamic”,
“Transventricular”, “Transcerebellar” at T2/T3

Automatic detection of 8 fetal thorax and heart anatomical structures on the views “4
chambers”, “LVOT”, “RVOT”, “Three vessels”, “Three vessels and trachea”, “Abdominal
Circumference”, “Axial view of the kidneys”, “Diaphragm”, “Abdominal cord insertion”
at T1

Automatic detection of 24 fetal thorax and heart anatomical structures on the views “Four
chambers”, “LVOT”, “RVOT”, “Three vessels”, “Three vessels and trachea”, “Abdominal
Circumference”, “Axial view of the kidneys”, “Diaphragm”, “Abdominal cord insertion”
at T2/T3

Automatic detection of 3 fetal placenta anatomical structures on the view “Placenta /
Cervix” at T2/T3

Table 6: results of the standalone performance testing


Automatic detection of 6 fetal Sagittal Fetus anatomical structures on the views “Crown
Rump Length”, “Profile” at T1

Automatic detection of 1 fetal Sagittal Fetus anatomical structures on the views “Profile”
at T2/T3

Automatic detection of 5 fetal Coronal Face anatomical structures on the views “Lips and
nose”, “Orbits”, “Coronal face” at T2/T3

Automatic detection of 3 fetal Spine anatomical structures on the view “Sagittal Spine” at
T2/T3

Automatic localization of 1 brain anatomical structures (on T1)

Automatic localization of 3 thorax and heart anatomical structures (on T1)

Automatic detection of the Anterior placenta location for the view “Placenta / Cervix” at
T2/T3

Automatic detection of the Posterior placenta location for the views “Placenta / Cervix”at
T2/T3

Automatic detection of the “Female sex” for fetal sex for the view “External Genitalia” at
T2/T3

Automatic detection of the “Male sex” for fetal sex for the view “External Genitalia” at
T2/T3


Medical Image Management And
Processing System

PeekMed web is a system designed to
help healthcare professionals carry out
pre-operative planning for several surgical
procedures, based on their imported
patients’ imaging studies. Experience in
usage and a clinical assessment are
necessary for the proper use of the system
in the revision and approval of the output
of the planning. The multi-platform system
works with a database of digital
representations related to surgical
materials supplied by their manufacturers.
This medical device consists of a decision
support tool for qualified healthcare
professionals to quickly and efficiently
perform the pre-operative planning for
several surgical procedures, using medical
imaging with the additional capability of
planning the 2D or 3D environment. The

Table 1: Summary of Predicate and Subject Device Characteristics to Demonstrate Substantial Equivalence


system is designed for the medical
specialties within surgery, and no specific
use environment is mandatory, whereas
the typical use environment is a room with
a computer. The patient target group is
adult patients who have an injury or
disability diagnosed previously. There are
no other considerations for the intended
patient population.

No contraindications specific to this device.

The PeekMed web allows the surgeon to
perform orthopedic pre-surgical planning
efficiently in the musculoskeletal system
(e.g., Hip procedures, Knee procedures)

The PeekMed web allows the surgeon to
perform pre-surgical planning efficiently in
the following anatomical regions:
- Hip
- Knee
- Upper limb
- Foot

Patient Population
Adults
Adults
Yes
---

End users
Healthcare Professionals
Healthcare Professionals
Yes
---

Software is cloud-based (not installable)
and can be displayed on any personal
device or workstation that can run a web


Distributed system (cloud-based). This
distributed system is a combination of
software modules placed on servers that
are able to communicate with each other.

The workflow is as follows: Import case
images, configure images, identify the
case, pre-surgical planning, and export the
case.

Internet connection
Required
Required
Yes
---

Receives medical images from various
sources

The software processes data to provide an
overlap and dimensioning of digital
representations of the prosthetic material

Allows the overlap of models and the
intersection of the models


Human
intervention for
image
interpretation

Ability to add
additional modules
when available

Yes
- Hip (X-ray and CT scan)
- Knee (X-ray, CT scan, and MRI)
- Upper limb (CT scan)
- Foot (X-ray and CT scan)

Yes
- Hip (X-ray and CT scan)
- Knee (X-ray - AP & LAT view, CT scan,
and MRI)
- Upper limb (CT scan)
- Foot (X-ray and CT scan)

The subject device includes new ML
model variants for the segmentation of
the Knee in a Lateral view.
Both devices allow planning for the
knee region with X-rays in an AP view,
but the subject device also offers a


new image perspective (Lateral view).
This does not constitute an intended
purpose update, nor does it raise
questions of safety and performance,
since the development, verification,
validation, and deployment processes
are the same for both devices.
Compared to the predicate device, the
subject device includes updates to
existing ML variants and the
introduction of new ML variants, such
as the segmentation and landmarking
for the knee region. In addition, certain
previously cleared variants have
undergone updates to improve
performance while maintaining the
same intended use. These
modifications do not alter the overall
intended use of the device

Automatic or Manual
- Hip (X-ray and CT scan)
- Knee (X-ray, CT scan, and MRI)
- Upper limb (CT scan)
- Foot (X-ray and CT scan)

Automatic or Manual
- Hip (X-ray and CT scan)
- Knee (X-ray - AP & LAT view, CT scan,
and MRI)
- Upper limb (CT scan)
- Foot (X-ray and CT scan)

The subject device includes new ML
model variants for landmarking for the
Knee in a Lateral view.
Both devices allow planning for the
knee region with X-rays in an AP view,
but the subject device also offers a
new image perspective (Lateral view).
This does not constitute an intended


purpose update, nor does it raise
questions of safety and performance,
since the development, verification,
validation, and deployment processes
are the same for both devices.
Compared to the predicate device, the
subject device includes updates to
existing ML variants and the introduction
of new ML variants, such as the
segmentation and landmarking for the
knee region. In addition, certain
previously cleared variants have
undergone updates to improve
performance while maintaining the same
intended use. These modifications do
not alter the overall intended use of the
device


IEC 62304
Software Development Life Cycle

FDA Regulatory Requirements and Design Controls

Digital Image and Communications in Medicine


Trade Name
CogNet AI-MT+
CogNet QmTRIAGE

The MedCognetics CogNet AI-MT+
software is a passive notification
for prioritization- only, parallel-
workflow software tool used by
MQSA qualified interpreting
physicians to prioritize patients with
suspicious findings in a medical
care environment. CogNet AI- MT+
utilizes an artificial intelligence
algorithm to analyze DBT
screening mammograms and flags
those that are suggestive of the
presence of at least one suspicious
finding at the exam level. CogNet
AI-MT+ produces an exam level
output to a PACS/Workstation for
flagging the suspicious study and
allows for worklist prioritization.
MQSA qualified interpreting
physicians are responsible for
reviewing each exam on a display
approved for use in
mammography, according to the
current standard of care. The
CogNet AI-MT+ device is limited to
the categorization of exams, does
not provide any diagnostic
information beyond triage and
prioritization, does not remove
images from the interpreting
physician’s worklist, and should not
be used in lieu of full patient
evaluation, or relied upon to make
or confirm diagnosis.
The CogNet AI-MT+ device is
intended for use with DBT
mammography exams acquired
using validated equipment systems,
only.

The MedCognetics (CogNet)
QmTRIAGE software is a passive
notification for prioritization only,
parallel workflow software tool used
by MQSA qualified interpre-ting
physicia-ns to prioritize patients with
suspicious findings in the medical care
environment.
QmTRIAGE utilizes an artificial
intelligence algorithm to analyze 2D
FFDM screening mammograms and
flags those that are suggestive of the
presence of at least one suspicious
finding at the exam level.
QmTRIAGE produces an exam level
output to a PACS/Workstation for
flagging suspicious study and allows
for worklist prioritization.
MQSA qualified interpreting
physicians are responsible for
reviewing each exam on a display
approved for use in mammography,
according to the current standard of
care. The QmTRIAGE device is
limited to the categorization of
exams, does not provide any
diagnostic information beyond triage
and prioritization, does not remove
images from the interpreting
physician’s worklist, and should not
be used in lieu of full patient
evaluation, or relied upon to make or
confirm diagnosis.
The QmTRIAGE device is intended for
use with 2D FFDM mammography
exams acquired using validated FFDM
systems, only.

User
MQSA Interpreting physician
MQSA Interpreting physician
Same

Yes.
Passive notification flagged for
review


510(k) Submitter
MedCognetics
MedCognetics

Modality
DBT screening mammograms
FFDM screening mammograms
Different

• Standard DBT
screening mammograms
• Biopsy proven cancer
studies (soft tissues and
microcalcifications)
• BIRADS 1 and 2 normal/benign
cases with 2 year follow up of a
negative diagnosis
• Female patie-n ts 22 and o-lder
• Bilateral Studies with 4
standard views (LCC, LMLO,
RCC, RMLO)

• Standard 2D FFDM
screening
mammograms
• Biopsy proven cancer studies
(soft tissues and
microcalcifications)
• BIRADS 1 and 2 normal/benign
cases with 2 year follow up of a
negative diagnosis
• Female patien-ts 22 and old-er
• Bilateral Studies with 4 standard
views (LCC, LMLO, RCC, RMLO)

Aids in prompt
identification of
cases with
indicated findings

Secondary Capture stored with
original DICOM study and may be
viewed with the study. The device
operates in parallel with the
standard of care, which remains
the default option.

Encapsulated PDF stored with
original DICOM study and may be
downloaded and viewed as a PDF.
The device operates in parallel with
the standard of care, which remains
the default option.


Cleaning
Not applicable
Not applicable
Same

Sterilization
Not applicable
Not applicable
Same


Region
Total Patients
Positives
Negatives

training set of modalities is beneficial to improving generalization.


Automated Radiological Image Processing Software

3.Identification of Proposed Device(s) [21 CFR 807.92(a)(2)]


Medical image management and processing system


Regulation
21 CFR 892.1650
21 CFR 892.1650
Identical

Regulatory Class
Class II
Class II
Identical

The Cara System is intended
for preplanning and
guidance of medical
interventions in an area
known to contain or be
adjacent to the cardiac
conduction system, such as
percutaneous or surgical
procedures, for example,
transcatheter aortic valve
replacement (TAVR), as well
as medical procedures
where the physician desires
to deliver therapy to the
patient's cardiac conduction
system or to a targeted
location within it (CSP).
The Cara System uses
computed tomography
angiography (CTA)-based
and user manually marked
landmarks to identify the
cardiac conduction axis
and generate a three-
dimensional (3D) map of
the individual patient’s
cardiac conduction system.
The system also overlays
the anatomical location of
the cardiac conduction
system (generated by the
Cara Metis Simulator using
pre-procedure CT data)
onto live fluoroscopic
images.

Cydar EV provides tools to:
• Import and visualise CT
data
• Segment and annotate
vascular anatomy from
CT data
• Place and edit virtual
guidewires and measure
lengths on them
• Make measurements of
anatomical structures on
planar sections of the CT
data
• Produce an operative
plan from
measurements and
segmentation of
preoperative vessel
anatomy
• Overlay planning
information such as
preoperative vessel
anatomy onto live
fluoroscopic images,
aligned
• based on the position of
anatomical features
present in both
• Non-rigidly transform
the visualisation of
anatomy when intra-
operative vessel
deformation is observed
• Post-operatively
review data relating to
procedures where the
system was used
Cydar EV is intended to assist
fluoroscopic X-ray guided
endovascular procedures in
the chest, abdomen, and

Substantially equivalent.
Both devices utilize the
same fundamental
technology (CT-to-
fluoroscopy overlay).
The anatomical focus
difference (cardiac
conduction system vs.
general vasculature)
represents a subset
application that does not
introduce new safety or
effectiveness concerns, as
both rely on identical image
registration within the
vascular system and overlay
principles.

Substantial Equivalence Comparison Table


The software utilizes AI/ML
algorithms to provide OCR
detection, automated
segmentation of anatomical
structures, and detection of
catheters.
The CARA System is
intended for use in adult
patients (18 years of age
and older).

pelvis by presenting the
operative plan in the context
of intraoperative fluoroscopy.
Cydar EV is intended to be
used for patients undergoing
a fluoroscopic X-ray guided
endovascular surgery in the
chest abdomen and pelvis,
and who have had a pre-
operative CT-scan.
The performance of the Cydar
EV software in the presence
of immature vertebral
anatomy is unknown. The
Instructions for Use explicitly
state this uncertainty and that
the software is therefore not
recommended for use in
patients under the age of 18.
IMPORTANT: Pre-
Operative Maps show static
anatomy derived from the
pre-operative CT. Real-time
anatomy moves with the
cardiorespiratory cycle;
progressive disease may
cause the anatomy to change
over time; and stiff wires,
stents or other surgical
instruments, may straighten
and displace blood vessels
from the preoperative
position
It is therefore mandatory to
check the real-time anatomy
with a suitable imaging
technique, such as contrast
angiography, before
deploying any invasive
m edical device.

User Population
Patients above the age of 18
Patients above the age of 18
Identical

Operating room, office
(during planning)

Standard workstation (Cara
Box)
Standard Fluoroscopy
splitter
Standard Monitor

Standard workstation
Standard Fluoroscopy splitter

Similar, both use standard
hardware; the addition of a
standard commercially
available monitor for the
Cara Medical System does
not raise new questions of
safety or effectiveness.

Local DICOM files or distant
PACS server.

Similar, both systems
accept DICOM files and
provide comparable image
source integration.

Similar, both systems
import patient data from


DICOM or PACS files with no
functional differences
impacting safety or
effectiveness.

Similar; while these devices
target different regions,
they are both designed to
segment and annotate
vascular anatomy.

Place and edit anatomical
landmarks and Make
measurements of
anatomical structures on
planar sections of the CT
data.

Place and edit virtual
guidewires and measure
lengths on them Make
measurements of anatomical
structures on planar sections of
the CT data.

Overlay planning
information onto live
fluoroscopic images, aligned
based on the position of
anatomical features and/or
catheters present in both.

Overlay planning information
such as preoperative vessel
anatomy onto live fluoroscopic
images, aligned based on the
position of anatomical features
present in both.

Similar, both systems
overlay planning
information onto
fluoroscopic images based
on anatomical alignment,
with no differences
affecting the intended
purpose.

DICOM Support
Import DICOM files
Import DICOM files
Identical

Import and visualize CT
data; Segment and annotate
cardio-vascular anatomy
from CT data; Place and edit
anatomical landmarks;
Make measurements of
anatomical structures on
planar sections of the CT
data; Visualize the
segmented cardio-vascular
anatomy.

Import and visualize CT data;
Segment and annotate vascular
anatomy from CT data; Place
and edit virtual guidewires and
measure lengths on them;
Make measurements of
anatomical structures on
planar sections of the CT data;
Visualize the segmented
vascular anatomy, annotations
+/-
measurements together (the
‘Operative Plan’)

Similar, both systems
support CT-based
preoperative planning with
annotation and
measurement tools.
Differences reflect clinical
focus but do not alter core
functionality or intended
use.

Human Intervention
for Interpretation of
Images

Yes
The information and
measurements displayed,
exported or printed are
validated and interpreted by
Physicians.

Generates a detailed 3D
model of the patient's
cardiac conduction system
tailored to planned
procedure.
Overlays the personalized
anatomical location of the

Intra-operative (Fusion imaging
functions): Overlay planning
information such as
preoperative vessel anatomy
onto live fluoroscopic images,
aligned based on the position
of anatomical features present
in both; Non-rigidly transform

Similar, both systems
overlay anatomical models
onto live fluoroscopy based
on shared landmarks.
Differences reflect clinical
application but maintain
the same core imaging
guidance function.


cardiac conduction system
onto live fluoroscopic
images.

the visualization of anatomy
when intra-operative vessel
deformation is observed.
Post-operative (Review
functions): Postoperatively
review data relating to
procedures where the system
was used.

AI / ML
Segmentation and
Image Processing

AI/ML algorithms are used
for OCR metadata
extraction, automated CT-
based anatomical
segmentation and
fluoroscopic device
detection. Outputs require
physician review prior to
clinical use.

Utilizes deep learning
algorithms for CT-based
vascular segmentation and
automated volume analysis.
Fluoroscopic image registration
and overlay are performed
using image processing and
tracking methods as described
in the User Manual.

Both devices incorporate
automated CT-based
segmentation and image
registration technologies to
generate 3D anatomical
models and support
fluoroscopic overlay.
Differences relate to
anatomical target and
algorithm implementation
and do not alter the
fundamental technological
principles.


Manual
verification of
extracted
parameters

Anatomical
Segmentation
(Cardiac
Chambers)

Manual
segmentation
by trained
technologists
with physician
adjudication

Dice
Similarity
Coefficient;
Average
Surface
Distance

Fluoroscopic
aortic
segmentation
for
registration

Manual
contour
annotation
with physician
adjudication


CBCT, intraoral
scans (IOS),
facial scans (FS)

Similar – Both use CBCT as the primary input.
CEPHX3D’s exclusive use of CBCT aligns with its
validated scope and does not affect safety or
effectiveness. See Section 8.

Similar – Both share STL and DICOM, sufficient
for treatment planning. Relu Creator’s
additional PLY and OBJ formats are optional
and do not impact safety or effectiveness. See
Section 8.

Table 1: Comparison of Technological Characteristics with Predicate Device


Key Differences in Technological Characteristics

Processes and
displays digital
images
(AI-driven)

CEPHX3D emphasizes AI processing; Relu
Creator prioritizes enhancement. Both achieve
comparable 3D modeling outcomes. See
Section 8.


Intended to be used as a support tool by trained
healthcare professionals to aid in diagnosis. It is
intended to provide image and related
information that is interpreted by a trained
professional to render findings and/or
diagnosis.

Pediatric (neonate, infant, child and adolescent)
and adult populations. For Pixel Lung only, the
lung detection module is intended for the
asymptomatic patient population 18 years of
age and older.

Viewer/Pixel: Supported DICOM modalities
Cardio: MR
Onco/Therapy Response Evaluation: MR and CT

Yes. DICOMweb and legacy upload methods.
JPEG and JPEG 2000 image compression.

Yes, including 2D, 3D, MIP, MPR display and
numerous layout options.

Pan, zoom, rotate, slice/time scroll, W/L, slab
thickness, cine, flow direction, flip, W/L invert &
presets.

Patient, study, orientation, and pixel information.

Measuring tools
Yes - linear, area, and volume.
Same

All modules. Secondary captures. Dictation
integration.

Yes - automatically identified + user editable

Table 1. Comparison of Key Technological Characteristics


For short-axis stack. Automatically identified +
user editable.

Yes. 2D phase contrast, 4D flow, perfusion,
delayed enhancement, T1, T2, and T2*
workflows. Editable flow correction mask using
ML model.

For T1, the contouring of the LV Endo and LV Epi
can be obtained automatically using a deep
learning model or can be drawn manually by the
user. For T2, the user can manually contour the
myocardium structures using the same tools.
Each step of the workflow for each of these
features can be reviewed and edited by the user.
T1 and T2 maps are limited to user-defined
regions of interest (ROIs) within the
myocardium and the left ventricle blood pool on
the original (raw) T1 and T2 series.

For T1, the contouring of the LV Endo and LV Epi
can be obtained automatically using a deep
learning model or can be drawn manually by the
user. For T2, the user can manually contour the
myocardium structures using the same tools.
Each step of the workflow for each of these
features can be reviewed and edited by the user.
Full device-generated T1 and T2 inline map
functionality. One generated inline map is
produced for each slice of the raw input series.

Yes, with numerous content configurations by
user.

Yes, with editing, creating, deleting, with sorting
options for list of nodules.

Yes, with a graphical display. Improved workflow
and linking.


Test
Summary of Methods
Acceptance Criteria
Results

Validation was performed by comparing the Tempus Pixel
generated inline maps to maps generated from FDA-cleared
MR scanners using the same input series. For each test
series, the Pixel inline map was compared to the MR scanner
generated inline map by placing a minimum of four regions
of interest (ROI) on different parts of the myocardium.
Sample Size:
T1: 30 series spread evenly across manufacturer and
scanner type. There were 4 ROIs assessed per series,
resulting in a total of 120 ROIs for T1.
T2: 30 series spread evenly across manufacturer and
scanner type. There were 4 ROIs assessed per series,
resulting in a total of 120 ROIs for T2.
FDA-cleared scanners used for comparison included:
GE 1.5T
Signa HDxt, SIGNA Artist,
SIGNA Voyager, Optima MR450
GE 3T
SIGNA Premier, DISCOVERY MR750w, SIGNA Pioneer
Philips 1.5T
Ingenia Ambition X, Achieva
Philips 3T
Ingenia Elition X, Ingenia
Siemens 1.5T
MAGNETOM Sola, MAGNETOM Aera
Siemens 3T
MAGNETOM Vida, MAGNETOM Skyra

T1: The 95th percentile deviation
(absolute error) between the Tempus
Pixel inline maps and scanner generated
inline maps, as assessed across means
from all 120 ROIs, must be within the
range from -30ms to +30ms. All
measurements must be within 1.5 times
this range (-45ms to +45ms).
T2: The 95th percentile deviation
(absolute error) between the Tempus
Pixel inline maps and scanner generated
inline maps, as assessed across means
from all 120 ROIs, must be within the
range from -3ms to +3ms. All
measurements must be within 1.5 times
this range (-4.5ms to +4.5ms).

T1: Across all 120 ROIs
● Mean absolute error = 4.76 ms
● 95th percentile absolute error =
13.82 ms
● Max absolute error = 28.57 ms
PASS
T2: Across all 120 ROIs
● Mean absolute error = 0.85 ms
● 95th percentile absolute error =
2.48 ms
● Max absolute error = 2.97 ms
PASS

Table 2. Summary of Non-Clinical Studies - Methods and Results


The Pixel user interface remains largely identical for the new
T1/T2 inline mapping as compared to that for T1/T2 ROI
mapping in the predicate device. As a conservative measure,
a limited-scope external validation of T1 and T2 inline map
generation was conducted by three physicians experienced
in assessing and quantifying parametric mapping data within
Pixel Cardio software. The physicians were instructed to
open studies, find automatically generated inline maps,
create new inline maps and locate them in the series list.

● When a study with a parametric
mapping raw data series (T1/T2) is
opened, there will automatically be an
inline map generated by Tempus
located in the series list within the
study.
● When the user loads a parametric
mapping raw data series into the
viewer and opens the corresponding
module, they will have the option to
generate a new inline map.
● When the user generates a new inline
map, the inline map will populate
within the series list and will have a
distinct series description.
● Users are able to quantify T1/T2
values from the Pixel generated inline
map and verify these values against
expected anatomical/ pathological
findings.

PASS. No deviations or unexpected
Results.

Software unit and system verification testing for the T1/T2
inline mapping was performed in accordance with Arterys
software design control procedures.

All test cases were required to meet
pre-established expected software
outputs.

PASS. All testing passed and no
new anomalies were identified.


Philips Ultrasound LLC
22100 Bothell Everett Hwy
Bothell, WA 98021-8431

Gina Quiram
Principal Regulatory Affairs Specialist
gina.quiram@philips.com
Phone: 1-310-745-2695

Priscilla Herpai
Senior Regulatory Affairs Manager
Priscilla.Herpai@philips.com
Phone: 1-610-533-7984

1. Submitter’s name, address, telephone number, contact person(s)


Classification Description
21 CFR Section
Product

System, imaging, pulsed doppler,
ultrasonic

System, imaging, pulsed echo,
ultrasonic

Transducer, ultrasonic, diagnostic
892.1570
ITX

Automated Radiological Image
Processing Software

Common Name: Diagnostic ultrasound system and transducers


Standard
Lumify
Lumify Diagnostic
Comparison

Scientific
Ultrasound Imaging
Ultrasound Imaging
Remains unchanged

Philips Lumify Diagnostic
Ultrasound System is
intended for diagnostic
ultrasound imaging in B(2D),
Color Doppler, Combined
(B+Color), Pulsed Wave
Doppler, and M-modes.

Lumify Diagnostic
Ultrasound System is
indicated for diagnostic
ultrasound imaging and fluid
flow analysis in the following
applications:
Fetal/Obstetric, Abdominal,
Pediatric, Cephalic, Urology,
Gynecological, Cardiac Fetal
Echo, Small Organ,
Musculoskeletal, Peripheral
Vessel, Carotid, Cardiac, Lung.
The Lumify system is a
transportable ultrasound
system intended for use in
environments where healthcare
is provided by healthcare
professionals.

Table 1: Summary of changes between proposed and predicate devices.


The pleural line tool detects
and assesses the appearance of
pleural lines in incoming
ultrasound frames. The lung
view quality tool indicates
whether incoming ultrasound
frames are adequate for
supporting interpretation of
lung features.
Cineloop-level reporting is
provided by averaging results
from each frame. Both features
allow manual adjustment of
software results.

The B-lines tool detects B-lines
in incoming ultrasound frames
and classifies them as merged
or not merged.
Cineloop-level reporting is
provided by averaging results
from each frame. The feature
allows manual adjustment of
software results.

Similar to predicate device.
The difference between the
subject device and the
predicate device is the
addition of the lung view
quality and pleural line
features. The principles of
operation for the two added
features, including live
frame-level display and
cineloop-level reporting,
remain unchanged compared
to the predicate device. The
core system software
architecture remains
unchanged.

Lumify Ultrasound System is
used by healthcare
professionals.

The Lumify system is a
transportable ultrasound
system intended for use in
environments where healthcare
is provided by healthcare
professionals.

Not applicable.
Transducers previously
cleared.

Secondary
IYO, ITX, QIH
IYO, ITX, QIH
Remains unchanged

Lumify Diagnostic Ultrasound System with Lung Application 3


SOFTLINK INTERNATIONAL PRIVATE LIMITED
2, Anand Park, Aundh, Pune - 411 007, India.


510(k) Owner’s Name
:
S
OFTLINK INTERNATIONAL PRIVA

Address
:
2
Anand Park, Aundh, Pune 41100

Contact Email
:
p
rakash@softlinkinternational.com

Device Common Name
:
Medical image management and p

Device Classification Name
:
Automated radiological image pro


Medical image management and processing system

Automated radiological image processing software


Automated Radiological Image Processing Software


Predicate Device Name
:
Syngo.via View&GO (Version VA40A)

Device Classification Name
:
System, image processing, radiological


Sl.
Features compared
Proposed Device
Primary Predicate
Result

3.
Classification
Class II
Class II
Same

Same
LLZ product code
is covered in the
other modules. i.e.
IES_ENTVIEWER,
IES_ECHOVIEWER,
IES_RADVEIWER,
IES_ZFPVIEWER.

Imagine® Enterprise
Suite (IES) is a medical
diagnostic device that
receives, stores, and
shares the medical
images from and to
DICOM-compliant
entities such as
imaging modalities
(such as X-ray
Angiograms (XA),
Echocardiograms (US),
MRI, CT, CR, DR, IVUS,
OCT, PET and SPECT),
external PACS, and
other diagnostic
workstations. It is used
in the display and
quantification of
medical images, after
image acquisition from
modalities, for post-
procedure clinical
decision support. It
constitutes a PACS for
the communication
and storage of medical
images and provides a
worklist of stored
medical images that
can be used to open
patient studies in one
of its image viewers. It

MPXA-2000 is software
intended to be used for
performing calculations
in X-ray angiographic
images of the coronary
arteries. These
calculations are based on
vessel contours which
are automatically
detected by the software
and subsequently
presented for review and
manual editing. The
analysis results obtained
with MPXA-2000 are
intended for use by
cardiologists and
radiologists:
- to support clinical
decisions concerning the
coronary arteries.
- to support the
evaluation of
intervention or drug
therapy applied for
conditions of the
coronary arteries.

Same
The intended use
of IES remains
consistent for both
the interpretation
and quantification
of images.


is intended to display
images and related
information that are
interpreted by trained
professionals to render
findings and/or
diagnosis, but it does
not directly generate
any diagnosis or
potential findings. Not
intended for primary
diagnosis of
mammographic
images. Not intended
for intra-procedural or
real-time use. Not
intended for diagnostic
use on mobile devices.

Imagine® Enterprise
Suite is a software
package that aids the
visualization and
quantification of
various medical
images in DICOM
format, when the
patient is NOT in life-
threatening state of
health, and the time
for medical decision is
NOT critical. Typical
users of this system
are trained
professionals,
including but not
limited to physicians,
radiologists,
cardiologists, nurses,
medical technicians,
and assistants.

MPXA-2000 is indicated
for use in clinical settings
where validated and
reproducible quantified
results are needed to
support the calculations
in X-ray angiographic
images of the coronary
arteries, for use on
individual patients with
coronary artery disease
(CAD). MPXA 2000 is
indicated for use in adult
patients only. When the
quantified results
provided by MPXA-2000
are used in a clinical
setting on X ray images
of an individual patient,
they can be used to
support the clinical
decision-making for the
diagnosis of the patient
or the evaluation of the
treatment applied. In this
case, the results are
explicitly not to be
regarded as the sole,
irrefutable basis for
clinical diagnosis, and
they are only intended


7.
Prescription Use or OTC Use
Prescription use
Prescription use
Same

9.
List of Modality support
X-ray Angiograms (XA)
X-ray
Same

10.
Web Diagnostic Viewer
Standalone Software
Standalone software
Same

X-Ray angiography in
DICOM format (vendor
independent)

Manual editing of automatic results
by user

Automatic calibration
based on calibration
factor in DICOM file OR
automatic
segmentation of
catheter from image.

Automatic calibration
based on isocenter
calibration factor in
DICOM file
Other manual calibration
options (catheter
calibration)

Automatic angiographic series
loading into the software from the
angiography equipment

The angiographic
equipment or modality
first exports the
angiographic series to
the IES PACS in DICOM
format. Angiographic
series are loaded into
the software from the
IES PACS.

Zooming, Panning,
Editing Vessel
Contours.

Zooming, Panning,
Editing Vessel Contours,
Angle, Length, Area, Text
Annotation

Automated 2D
arterial contour
segmentation

Optional Stent
analysis including
stent edges


Sl.
Features compared
Proposed Device
Primary Predicate
Result

Results for multiple
lesions and
additional user-
defined ROIa)

Results for multiple
lesions. No user-
defined ROIs are
supported.

Results for multiple
lesions and additional
user-defined ROIa)

Vessel analysis
Main and side branch
main and side branch
Same

% Diameter Stenosis,
Minimum Lumen
Diameter (MLD),
Proximal and Distal
Diameters (at P- and D
marker positions),
Stenosis Length,
Reference Diameter

% Diameter Stenosis,
Minimum Lumen
Diameter (MLD),
Proximal and Distal
Diameters (at P- and D
marker positions), ROIa)
Length, Reference
Diameter

Automatically load and visualize
ECG data acquired from the DICOM
file

Automatic EoD (End of Diastole)
phase detection in DICOM file based
on ECG.

Patient and Study
Details, Calibration,
Annotation,
Measurement, and
Analysis Details.

Patient and Study
Details, Calibration,
Annotation,
Measurement, and
Analysis details.

22.
Export file formats
PDF
PDF, Excel
Same


Sl.
Features compared
Proposed Device
Reference Predicate
Result

2.
Manufacturer
Softlink International Pvt Ltd
Siemens Healthcare GmbH

modules of the Imagine® Enterprise Suite (IES) and the Syngo.via View&GO system.


3.
Classification
Class II
Class II
Same

Same
QIH product
code is covered
in the
AngioQuant
Module

Imagine® Enterprise Suite (IES)
is a medical diagnostic device
that receives, stores, and shares
the medical images from and to
DICOM-compliant entities such
as imaging modalities (such as
X-ray Angiograms (XA),
Echocardiograms (US), MRI, CT,
CR, DR, IVUS, OCT, PET and
SPECT), external PACS, and
other diagnostic workstations. It
is used in the display and
quantification of medical images,
after image acquisition from
modalities, for post-procedure
clinical decision support. It
constitutes a PACS for the
communication and storage of
medical images and provides a
worklist of stored medical
images that can be used to open
patient studies in one of its
image viewers. It is intended to
display images and related
information that are interpreted
by trained professionals to
render findings and/or
diagnosis, but it does not directly
generate any diagnosis or
potential findings. Not intended
for primary diagnosis of
mammographic images. Not
intended for intra-procedural or
real-time use. Not intended for
diagnostic use on mobile
devices.

Syngo.via View&GO is a
software solution intended to
be used for viewing,
manipulation, communication,
and storage of medical
images. It can be used as a
stand-alone device or together
with a variety of cleared and
unmodified syngo-based
software options. syngo.via
View&GO supports
interpretation and evaluation
of examinations within
healthcare institutions, for
example, in Radiology, Nuclear
Medicine and Cardiology
environments. The system is
not intended for the displaying
of digital mammography
images for diagnosis in the
U.S.


Imagine® Enterprise Suite is a
software package that aids the
visualization and quantification
of various medical images in
DICOM format, when the patient
is NOT in life-threatening state of
health, and the time for medical
decision is NOT critical. Typical
users of this system are trained
professionals, including but not
limited to physicians,
radiologists, cardiologists,
nurses, medical technicians, and
assistants.

Syngo.via View&GO is
indicated for image rendering
and post-processing of DICOM
images to support the
interpretation in the fields of
radiology, nuclear medicine,
and cardiology.

Server: Windows Server OS
2022/Windows 11 Pro, 64 bit
Client: Windows 11 Pro, 64 bit

Microsoft Windows 11- 64-bit
or higher
Microsoft Windows 10- 64-bit
or higher

System is logically broken down
into IES PACS and IES viewers
subsystems. The IES Viewers
further contain the
IES_EntViewer, IES_RadViewer
and IES_EchoViewer modules.

System that is logically broken
down to syngo.via View&GO
subsystems. Subsystems are
further broken down to syngo
modules.

X-ray Angiograms (XA),
Echocardiograms (US), MRI, CT,
CR, DR, IVUS, OCT, NM (PET and
SPECT).

CT Image (Computed
Tomography), MR Image
(Magnetic Resonance), NM
Image (Nuclear Medicine), XA
Image (X-Ray Angiography),
US Image (Ultrasound), DX
Image (Digital Radiography),
DICOM secondary capture
objects.

Standalone software for
diagnostic use.
Web-based IES ZeroFootPrint
Viewer (IES_ZFPViewer) is
designed solely for browser-
based viewing and is not
intended for diagnostic purposes.


Multiplanar reconstruction
(MPR), Maximum and Minimum
Intensity Projection (MIP/MinIP),
Basic Volume Rendering with
ClipBox, Multimodality
registration, automatic spine
labeling (no rib labeling),
Cardiothoracic Ratio.

Multiplanar reconstruction
(MPR), Maximum and
Minimum Intensity Projection
(MIP/MinIP), Volume
Rendering Technique (VRT)
with additional edge and
surface enhancements and
control over rendering
parameters, Shaded Surface
Display (SSD), Digitally
Reconstructed Radiograph,
Editor functionality (e.g.
ClipBox), Auto-Contour,
Registration, Anatomical
registration, Region growing,
Automatic Spine Labeling, also
for ribs in CT thorax scans
(“Rib labeling”), Reprocessing
X-ray projection images into
3D image and Topograms,
FASTAlign, Cinematic VRT1

IES_EntViewer: Line
Measurement for ECG
IES_Radviewer: Distance, Angle,
Cobb Angle, ROI
IES_EchoViewer: Distance,
Angle, Point, Area-MOD (method
of disks), VTI (Velocity Time
Integral)

Distance, Angle & Angle [1]
on-stack, VOI, and ROI
Measurement

Receive & Store: Images are
received and stored as received
without any change in the
compression format.
Display: Images are displayed
as received without any change
in the compression.
Lossy compression images are
displayed with an indication to
the user with the compression
type (Transfer Syntax) used.
Export: To DICOM Node:
Images are sent as per the
DICOM negotiation.
Uncompressed is preferred and
lossy compression is not
supported. To Exchangeable
media: Images exported as
stored in the local storage.

Receive & Store: Images are
received and stored as
received without any change in
the compression format.
Display: Images are
displayed as received without
any change in the
compression.
Lossy compression images are
displayed with an indication to
the user with the compression
ratio.
Export: To DICOM Node:
Images are sent as per the
DICOM negotiation.
Uncompressed is preferred
and lossy compression is not
supported. To Exchangeable


Sl.
Features compared
Proposed Device
Reference Predicate
Result

Supported Compressions for
export:
• JPEG 2000 Image
Compression
• JPEG Baseline (Process 1):
Default Transfer Syntax for
Lossy JPEG 8 Bit Image
Compression
• JPEG Extended (Process 2 &
4): Default Transfer Syntax
for Lossy JPEG 12 Bit Image
Compression - (Process 4
only)
• JPEG Lossless, Non-
Hierarchical (Process 14)
• JPEG Lossless, Non-
Hierarchical, First-Order
Prediction (Process 14
[Selection Value 1]): Default
Transfer Syntax for Lossless
JPEG Image Compression
• JPEG-LS Lossless Image
Compression
• JPEG-LS Lossy (Near-
Lossless) Image
Compression
• RLE Lossless.

media: Images exported as
stored in the local storage.
Supported Compressions
for export: lossless
compression algorithms, JPEG,
JPEG 2000, and RLE.

Return valid DICOM error codes
to the associated Application
Entity in case the data transfer is
interrupted.
Hardware/Operating System/
Framework Compatibility Check
during Installation.
Display Compatibility Check
supports the end user to qualify
the system for hardware-
accelerated graphics using a
GPU.

Alert the user in case the data
transfer is interrupted to the
connected DICOM node.
Hardware / Operating System
compatibility check during
Installation.
Display Compatibility Check
supports the end user to
qualify the system for proper
diagnostic use.

Yes, with text labels, and
appropriate tooltips.

Yes, with a reduced color
palette, clearer structure, and
text labels on icons.

Yes, with simplified search
functionality, Study & series level
results, and paging.

Yes, with simplified search
functionality, clearer structure
of search results, image
preview, unlimited search
results, and periodic updates
of search results.


Series-level results can be
enabled by the user. Shown in
patient worklist with series
description.

The Series Navigator lists all
currently loaded data within a
workflow. Studies are marked
with colorized time points.

No, reporting support is
provided to create reports
using any 3rd party reporting
tool. Hence the findings also
cannot be navigated.

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

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

Data stored on IES PACS archive
storage.

Data can be sent to an archive
if syngo.via View&GO is
connected to a PACS or
corresponding DICOM node.

Yes, with suggested spine labels
to be confirmed by the user, and
additional smart placement of
labels. No rib labeling.

Yes, with suggested spine
labels to be confirmed by the
user, and additional smart
placement of labels, also in
inter-vertebra regions,
support of 2D images, support
of multi-series studies, and
added support for rib labels.

Context-enabled help for the IES
PACS worklist, ENT viewer and
ZFP Viewer. User manuals for
RadViewer and EchoViewer.

Yes, with a reduced color
palette, clearer structure, and
text labels on icons

Yes, with support for marking a
position on an image and textual
annotations

Show or hide graphical objects
such as annotations and
markers, show or hide reference
lines

Show or hide image text, show
or hide custom image text,
show or hide graphical objects
such as annotations and
markers, show or hide
reference lines, show or hide
shutter


n the marketing application/submission number, if it is known. This

e left blank for original applications/submissions.
e the device trade name(s).

der Positioning is intended to be used as an information tool to assist in the preoperative
ng and visualization of a primary total shoulder replacement.

t the types of uses (select one or both, as

Prescription Use (Part 21 CFR 801 Subpart D)
[] Over-The-counter Use (21 CFR 801 Subpart C)


FRANCOIS Adeline
VP QARA and Clinical Affairs

Contact Information
Email: adeline@avatarmedical.ai


Medical Image Management and Processing System

QIH, automated radiological image processing software

LLZ, system, image processing, radiological


Planning software for Total Shoulder Arthroplasty

Classification Name
System, Image Processing, Radiological


Subject Device:
brAIn™ Shoulder Positioning

Intended Users
Healthcare Professionals
Healthcare Professionals

Healthcare facilities such as hospitals and
clinics

Type of software
Web-based software
Web-based software

Use time
Pre-operatively
Pre-operatively

Imaging used
CT-scan images
CT-scan images

Anatomical and reverse shoulder implants

User profiles define the authorized actions
for the user.

Case management
List of cases
List of cases

Tools
Milling tool
Milling tool (called reaming)

Patient last name
Patient first name
Patient sex
Patient birthdate
Shoulder side
Surgery date

Patient last name
Patient first name
Patient sex
Patient birthdate
Patient age
Patient height
Patient weight


3D and 2D representation of the
humerus and the scapula.

2D DICOM view
Axial views are displayed in 2D DICOM
Viewer interfaces.

The 2D DICOM Viewer Interface
displays 2D DICOM images
Axial and coronal views are
displayed in two separate 2D
DICOM Viewer interfaces.

3D reconstruction in an
interactive viewing interface with
soft tissues (Pre-Position
Interface only)

Case details,
Segmentation,
Glenoid
Planning Report

Create plan,
Validate Segmentations,
Pre-position with the
validation of Shoulder
Landmarks,
Implant,
Glenoid/humerus,
Post position,
Surgical planning Report

Manual Landmarks/Measurements
performed:
Shoulder Measurements, scapula neck
length, posterior subluxation, glenoid
reaming depth.
Manual measurements performed:
Feature that displays and calculates
the distance between two points.

Pre-positioning or Semi-
automatic Landmarks/
Measurements performed:
Shoulder Measurements,
backside seating, 3D subluxation,
glenoid reaming depth.
Manual measurements
performed (Ruler Tool):
Feature that displays and
calculates the distance
between two points.

Pre-positioning or Semi-
automatic initial placement
performed.

Planning report comprising patient
information, implant and measurements
name, its preoperative value, and the
planned value. It also comprises images
from the preoperative situation with
different camera angles, image showing
the drilling simulation pin, images showing
the scapula and the Glenoid implant.

Planning report comprising
patient information, implant and
measurements name, its value in
pre-position, and the value in
post-position. It also comprises
images from the Pre-Position tab
with different camera angles,
images showing the K-Wire
placement, images showing the


Subject Device:
brAIn™ Shoulder Positioning

Glenoid implant, and images
from the Post-Position Interface
showing both the scapula and
humerus and the chosen implant
components.

Does not include any predictions and
recommendations.


Common Name
Medical Image Processing Soft

Classification Name
Medical Image Management a

Regulation Description
Picture Archiving and Commun


Common Name
Medical Image Processing Software

Medical Image Management and Processing System

Picture Archiving and Communications System


Details
Subject Device
Predicate Device
Remark

14 Havelock Place (MS), Harrow,
United Kingdom, HA11LJ

4918 September Street, San Diego, CA
92110, USA

Generic Name
Medical Image Processing Software
Medical Image Processing software
Equivalent

Classification
Class II
Class II
Equivalent

Alzevita is intended for use by
neurologists and radiologists

The NEUROShield medical image
processing software is intended for

VI. Comparison of Technological Characteristics with the Predicate Device


experienced in the interpretation and
analysis of brain MRI scans. It enables
automated labelling, visualization, and
volumetric measurement of the
hippocampus from high-resolution T1-
weighted MRI images. The software
facilitates comparison of hippocampal
volume against a normative dataset
derived from MRI scans of healthy
control subjects aged 55 to 90 years,
acquired using standardized imaging
protocols on 1.5T/3T MRI scanners.

automatic labelling, visualization, and
volumetric quantification of the
Hippocampus brain structure from a
set of MR images

• A Software as a Medical Device
(SaMD) designed for imaging and
quantitative analysis of the
hippocampal brain structure from
MRI scans.
• It is a fully automated, geometry-
based brain analytics tool and cloud
platform developed using advanced
3D U-Net++ methodologies

• Software as a medical device to be
used in the process of Imaging and
quantification of the Hippocampus
brain structure from a set of MR
images
• Fully automated brain geometry -
based quantifying analytics
tool/cloud platform developed using
DeepNet / U-Net methodologies

• Software Package (Accessible via
Web Browser)
• Operates on off the shelf hardware
(multiple vendors)

Supports Linux, Windows and Mac OS
latest

Alzevita requires compressed DICOM
files or NIFTI files as Input

NEUROShield requires uncompressed
DICOM files as input

Software provides volumetric
measurements of Hippocampus brain
structures

Provides volumetric measurements of
Hippocampus brain structures

• Automated quality control functions -
Scan protocol verification
• Results must be reviewed by a
trained clinicians/ Radiologist/
Neurologist


Table 1 presents the distribution of subjects based on key imaging and demographic parameters.


The mean age of subjects is found to be 76 ± 7 years.


The table below provides the categorization of subjects according to selection criteria:


Hausdorff distance
6.1
(1.43, 1.59)
Pass

respectively. The following table shows the 95% confidence interval for both.


Region
East US
West US
Central US
Canada
East US
West US
Central US
Canada

Criteria Pass Pass Pass Pass Pass Pass Pass Pass


Classification Name:
Magnetic Res
onance Diagnostic
Device
(MRDD)


Predicate Devices
FDA Clearance
Product
Manufacturer

MAGNETOM Viato.Mobile with syngo MR
XA70A

XB10 are substantially equivalent to the following devices:


Siemens
Shenzhen
Magnetic
Resonance
Ltd.


Performance Test
Tested Hardware or Software
Source/Rationale for test

Guidance for the Content of
Premarket Submissions for Software
Contained in Medical Devices

Coils, new or modified
software features

Guidance for submission of Premarket
Notifications for Magnetic Resonance
Diagnostic Devices

Image quality
assessment by sample
clinical images

- new / modified pulse
sequence types.
- comparison images between
the new / modified features
and the predicate device
features

Biocompatibility
surface of applied parts
ISO 10993-1

Electrical safety and
electromagnetic
compatibility (EMC)

Complete systems
MAGNETOM Flow. Platform

The following performance testing was conducted on the subject devices:


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 normalized mean squared error (NMSE). Additionally,
images were inspected visually to ensure that potential artefacts are
detected that are not well captured by the metrics.
After successful passing of the quality metrics tests, work-in-progress
packages of the network were delivered and evaluated in clinical settings
with collaboration partners.

Equipment: 3T and 1.5T MRI scanners.
Protocols: representative protocols (T1, T2*, T2, T2 FLAIR).
Body region: brain.

Sample source: in-house measurement.
Note: Due to the network architecture, attributes like gender, age and
ethnicity are not relevant to the training data.

Dataset split:
Training:
20,076 slices
Validation:
1.5T Validation: 3,616 slices (1.5T validation);
3T Validation: 6,048 slices (3T validation).
Note: Data split maintained similar data distribution (e.g. contrast,
orientation, field strength, …) in both training and validation datasets.

No clinical subgroups have been defined for the datasets.

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 Gaussian noise to k-space data and uniformly-
random cropping of the training data along the readout direction.

Datasets for training and testing were split prior to training (see Test Setup
field above).


Quantitative evaluations of structural similarity index (SSIM), peak signal-
to-noise ratio (PSNR) and mean squared error (MSE) metrics showed a
convergence of the training and improvements compared to conventional
parallel imaging. An inspection of the test images did not reveal any
negative impact to the image quality. The function has been used either to
acquire images faster or to improve image quality.

Test setup
Equipment: 0.55T, 1.5T and 3T scanners

- Deep Resolve Boost for FL3D_VIBE and Deep Resolve Boost for SPACE:


Protocols: representative measurement protocols (T1, T2 and PD with and
without fat saturation) which have been altered for training (e.g. to
increase SNR, increase resolution or reduce acceleration).
Body regions: broad range of body regions
Used coils: broad range of coils to cover the dedicated body regions

Sample size: 27,679 3D patches from 1265 measurements

Dataset split: Training: 81% of the 1265 measurements
Validation: 19% of the 1265 measurements
Note: Data split maintained similar data distribution (e.g., contrast,
orientation, field strength, …) in both training and validation datasets.

Sample source: in-house measurements (training and validation) and
collaboration partners (testing)

Gender distribution:
- Male: 53%
- Female 47%
Age: for training and validation.
- 19 - 45: 14%
- 46 - 65: 43%
- 66 - 89: 43%
Clinical subgroups: No clinical subgroups have been defined for the
datasets.

The acquired datasets (as described above) 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 undersampling of the data by discarding k-space lines as
well as creating sub-volumes of the acquired data.

Datasets determined for training and validation were split prior to training
along individual acquisitions to ensure that there is no mixture of sub-
volumes stemming from the same acquisition.


The impact of the Deep Resolve Sharp network has been characterized by
several quality metrics such as peak signal-to-noise ratio (PSNR), structural
similarity index (SSIM), and perceptual loss. The tests include 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 and reduced Gibb’s artifacts.

Equipment: 0.55T, 1.5T and 3T MRI scanners
Protocols: representative measurement protocols (T1, T2 and PD with and
without fat saturation) which have been altered (e.g. to increase SNR,
increase resolution or reduce acceleration)
Body regions: broad range of different body regions.
Used coils: broad range of coils to cover the dedicated body regions

Sample size: approx. 13,000 high resolution 3D patches from 500
measurements.

Dataset split: Training: 70% of the 500 measurements.

- Deep Resolve Sharp for FL3D_VIBE and Deep Resolve Sharp for SPACE


Validation: 30% of the 500 measurements
Note: Data split maintained similar data distribution (e.g., contrast,
orientation, field strength, …) in both training and validation datasets.

Gender distribution:
- Male: 66.6%
- Female 33.4%
Age: for training and validation.
- 19 - 45: 8.4%
- 46 - 65: 40.2%
- 66 - 89: 51.4%
Clinical subgroups: No clinical subgroups have been defined for the
datasets.

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.

The high-resolution datasets were split to 70% training and 30% validation
datasets before training to ensure independence of them. The input and
output variables of the network have been derived from the same dataset
so that no confounders exist for the training methodology.