evaluation and documentation of CT lung tissue images by providing image segmentation of
sub-structures in lung, lobe, airways and cardiac, registration of inspiration and expiration
which could analyze quantitative information such as air trapping volume, air trapped index,
and inspiration/expiration ratio. And, volumetric and structure analysis, density evaluation and
reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on
premise and as cloud environment as well to allow users to connect by various environment
such as mobile devices and chrome browser. Characterizing nodules in the lung in a single
study, or over the time course of several thoracic studies. Characterizations include nodule type,
location of the nodule and measurements such as size (major axis, minor axis), estimated
effective diameter from the volume of the nodule, volume of the nodule, Mean HU(the average
value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass(mass calculated
from the CT pixel value), and volumetric measures(Solid major; length of the longest diameter
measured in 3D for solid portion of the nodule, Solid 2nd Major: The length of the longest
diameter of the solid part, measured in sections perpendicular to the Major axis of the solid
portion of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposed
to aid with findings). The system automatically performs the measurement, allowing lung
nodules and measurements to be displayed and, integrate with FDA certified Mevis CAD
(Computer aided detection) (K043617).It also provides CAC analysis by segmentation of four
main artery (right coronary artery, left main coronary, left anterior descending and left
circumflex artery then extracts calcium on coronary artery to provide Agatston score, volume
score and mass score by whole and each segmented artery type. Based on the score, provides
CAC risk based on age and gender.

ClariCT.AI, is a software device intended for networking, communication, processing, and
enhancement of CT images in DICOM format regardless of the manufacturer of CT scanner or
model.

Broncholab provides physicians with reproducible CT values for pulmonary tissue for
providing quantitative support for diagnosis and follow-up examination. Broncholab can be
used to support physicians in the diagnosis and documentation of pulmonary tissues images
(e.g., abnormalities) from CT thoracic datasets. Three-D segmentation and isolation of
subcompartments, volumetric analysis, density evaluations, low density cluster analysis,
fissure evaluation and reportingtools are combined with a dedicated workflow.

The AVIEW is a software product that can be installed on a PC. It shows images taken with the
interface from various storage devices using DICOM 3.0, the digital image and communication
standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-
processing, saving, and sending images by using software tools. And is intended for use as a
quantitative analysis of CT scanning. It provides the following features such as segmentation
of lung, lobe, airway, fissure completeness, semi-automatic nodule management, maximal
plane measure, 3D measures and volumetric measures, automatic nodule detection by
integration with 3rd party CAD. It also provides the Brocks model, which calculates the
malignancy score based on numerical or Boolean inputs. Follow-up support with automated
nodule matching and automatically categorize Lung-RADS score, which is a quality assurance
tool designed to standardize lung cancer screening CT reporting and management
recommendations that are based on type, size, size change, and other findings that are reported.
It also provides a calcium score by automatically analyzing coronary arteries from the
segmented arteries.

The AVIEW is a software product which can be installed on a PC. It shows images taken with
the interface from various storage devices using DICOM 3.0 which is the digital image and


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

ClariCT.AI software is intended for denoise processing and enhancement of CT DICOM
images when higher image quality and/or lower dose acquisitions are desired. ClariCT.AI
software can be used to reduce noises in CT images of the head, chest, and abdomen, in
particular in CT images with a lower radiation dose. ClariCT.AI may also improve the image
quality of low dose nonpdiagnostic Filtered Back Projection images as well as lterative
Reconstruction images.
The system enables the receipt of DICOM images from CT imaging devices (modalities),
enables their denoise processing and enhancement, and transmission to a PACS workstation.

Broncholab is a SaMD (Software as Medical Device) which provides quantitative CT values
that are intended to support the physician in the diagnosis and documentation of pulmonary
tissues images (abnormalities) from CT scans. The CT scan images are transformed into 3D
models of the patient-specific lungs using several image processing steps. Broncholab can be
used to assess the effectiveness of therapy based on CT scan data. It is used along with the
followingaccessories:
(cid:130) Web Portal: Enables the uploading of CT scans and patient data
(cid:130) ClientReport: Enables the conversion of the output of Broncholab (CT values) into a
desired digital format (PDF
Report) and transfers this Report to the physician via email.
Inspiratory CT scan images uploaded by the users are converted into quantitative CT values
using a combination of software tools. Quality checks (both manual and automated) are
implemented to assure the quality of the final data.
The outputs are provided as absolute values and as a percentage of the total airway volume/
lung volume/ lobarvolume depending on the parameter. The device can be used on a computer
with a web browser installed and consistsof two accessories:
(cid:130) An online portal to upload the CT scans
(cid:130) An accessory that enables the creation of the Report
The CT values include:
1) Lung and Lobar Volume is the volume of the 3D model of each lung lobe.
2) Airway Volume is defined as the region from the trachea until the segmental bronchi.
3) Lung Density Scores/ Volumes is defined as all the intrapulmonary voxels with
Hounsfield Units between -1024
and -950 using the inspiratory scans:
• Low attenuation areas below -950 HU (LAA-950HU)
• 15th percentile of density histogram: Percentile density (PD) can also be used to
express Emphysema.
• Blood vessel density: Blood vessel density can be determined through segmentation
and 3-D reconstruction of the blood vessels. The segmentation is based on local


(cid:130) PANCAN risk
calculator
Auto detect nodule
location by lobe

Supports kernel
conversion of LAA on
LCS Page

Noise reduction is
performed with the
use of pre-trained
deep learning
models.

Extracting Calcium on
Coronary Artery and
provides Agatston
score, volume score
and mass score.

Automatically
segments calcium area
of coronary artery
based on deep
learning.

• Connected from
anywhere,
anyplace, anytime
• Supports mobile
view through
various mobile
devices served by
ios and Android.
• Comparable with
Chrome browser

Rule-based automatic
processing server
(APS)


Predicate Device
Behold.ai red dot™ device (K191556)

Subject Device
Aidoc Briefcase (K214043)

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

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

Appropriately trained medical specialists

Notification-
only, parallel
workflow tool

No. No cases are removed from
Worklist or de-prioritized.

No. No cases are removed from
worklist or de-prioritized

Artificial intelligence algorithm with database
of images

- Image input, validation and anonymization
- Image Analysis Algorithm
- PACS Integration Feature

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


Table 4 . TTN Comparison Subject & Predicate


Comparison of the primary currently marketed and predicate device,

MRCAT Brain versus the proposed MRCAT Head & Neck

Device
Device
MRCAT Brain
MRCAT Head & Neck
Similarities

MRCAT imaging is
intended to provide the
operator with
information of tissue
properties for radiation
attenuation estimation
purposes in photon
external
beam radiotherapy
treatment planning.

MRCAT Brain is
indicated for
radiotherapy treatment
planning for primary
and metastatic brain
tumor patients.

MRCAT Head and
Neck is indicated for
radiotherapy
treatment planning
for patients with soft
tissue tumors in the
Head and Neck
region

No significant
difference.
MRCAT Brain
and MRCAT
Head & Neck
are both
indicated for
radiotherapy
treatment
planning in a
defined
region.
Brain tumors
are soft tissue
tumors.


Identical
MR images
obtained in
the same
imaging
session are
inherently in
the same
frame of
reference.

Registration
between
primary and
secondary
image
datasets

Secondary mDixon MR
image, source data to
MRCAT, is inherently
registered as part of
MRCAT algorithm with
MRCAT image, which
simplifies workflow.
Other MR images, like
T2w and fiducial
marker detection
images are registered
using tools available in
RTP system

Secondary mDixon
MR image, source
data to MRCAT, is
inherently registered
as part of MRCAT
algorithm with
MRCAT image, which
simplifies workflow.
Other MR images,
like T2w images are
registered using tools
available in RTP
system

No significant
difference
Secondary MR
images are
obtained in
the same
imaging
session
reducing the
possibility of
patient
motion
between
images.

MRCAT image intensity
information is provided
in Hounsfield Unit (HU)
values.

Identical.
MRCAT Brain
and MRCAT
Head & Neck
both have
continuous
HU value
approach.


Conversion
from primary
image to
density
values used
in dose
calculation

Primary image HU
values are converted to
densities through
density table specific
for the MRCAT.

No significant
difference
MRCAT has
specific
density table
that is used in
a similar
manner to CT
specific
density tables.

Bones are segmented
from mDixon in-phase
and water images using
machine learning based
segmentation. The
segmented bones are
in skull, upper C-spine
and jaw.
Body outline is
segmented using in-
phase and water
images.
Bones are assigned a
continuum of HU
values between dense
cortical bone and light
spongy bone depending
on the fat and water
intensities of the
voxels.
Soft tissue are assigned
a continuum of HU
values depending on
the fat and water
intensities of the
voxels.
The HU values for the
MRCAT Brain are
calibrated using
registered CT images
from several sites.
MRCAT Brain algorithm
is fully trained before
product release, after

Bones are segmented
from mDixon in-
phase and water
images using
machine learning
based segmentation.
The segmented
bones are in skull, C-
spine, jaw, and
shoulder girdle.
Body outline is
segmented using in-
phase and water
images.
Bones are assigned a
continuum of HU
values between
dense cortical bone
and light spongy
bone depending on
the fat and water
intensities of the
voxels.
Soft tissue are
assigned a
continuum of HU
values depending on
the fat and water
intensities of the
voxels.
The HU values for the
MRCAT Head & Neck
are calibrated using

No significant
difference
Segmentation
is done for
both MRCAT
Brain and
MRCAT Head
& Neck using
the mDIXON
image
contrasts.
HU value
assignment is
done based
on mDixon
image
intensities.
The models
used are
equivalent in
relation to
dose and
positioning
accuracy.
Both
algorithms are
locked; they
do not change
after
installation
based on new
data during
the use.


registered CT images
from several sites.
MRCAT Head & Neck
algorithm is fully
trained before
product release, after
which the algorithm
is locked.

Ingenia MR-RT with
MRCAT Brain supports
MR Only simulation
with relative patient
marking.
Patient positioning in
the treatment machine
must be checked either
with cone beam
computed tomography
(CBCT) or plain
radiographs by
registering bone
structures.

Ingenia MR-RT with
MRCAT Head & Neck
supports MR Only
simulation with
relative patient
marking.
Patient positioning in
the treatment
machine must be
checked either with
cone beam
computed
tomography (CBCT)
or plain radiographs
by registering bone
structures.

No significant
difference
The visibility
of bone
structures is
equivalent for
both
products.


The simulated dose
based on MRCAT Brain
images shall not differ
in 95% of the indicated
patients (gamma
analysis criterion
2%/2mm realized in
98% of voxels within
the PTV or exceeding
75% of the maximum
dose) when compared
with CT-based plan.
The average simulated
dose based on MRCAT
Brain shall not deviate
more than 5% or 1 Gy,
which ever is greater, in
99% of the indicated
patients in the volume
of sensitive organs
when compared with
CT based plan.

The simulated dose
based on MRCAT
Head & Neck images
shall not differ in
95% of the indicated
patients (gamma
analysis criterion
2%/2mm realized in
98% of voxels within
the PTV or exceeding
75% of the maximum
dose) when
compared with CT-
based plan.
The average
simulated dose based
on MRCAT Head &
Neck shall not
deviate more than
5% or 1 Gy, which
ever is greater, in
99% of the indicated
patients in the
volume of sensitive
organs when
compared with CT
based plan.

Identical
The same
dose
evaluation
methodology
is used for
both
products. The
criteria are
selected
based on the
needs of the
application.

MRCAT accuracy:
± 1 mm accuracy: 200
mm diameter sphere
± 2 mm accuracy: 400
mm diameter sphere
(limited in the bore
direction by +/- 160
mm from the z=0 mm
plane)
± 5 mm accuracy: 500
mm diameter sphere
(limited in the bore
direction by +/- 160
mm from the z=0 mm
plane)


Common Name
Medical Image Processing Software

Medical image management and processing system

Picture archiving and communications system


Organization
In Med Prognostics L3C
CorTechs Labs, Inc

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

Automatic labelling, visualization
and volumetric quantification of
segmentable brain structures
and lesions from a set of MR
images. Volumetric data may be
compared to reference
percentile data.

•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.

•Automated measurement of
brain tissue volumes and
structures and lesions
•Automatic segmentation and
quantification of brain structures
using a dynamic probabilistic
neuroanatomical atlas, with age
and gender specificity, based on
the MR image intensity.

• Software package
(accessible via web browser)
• Operates on off-the-shelf
hardware (multiple vendors)

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

Supports Windows and Mac
OS latest (No older than
Catalina)

An automated internal
pipeline that performs:
- segmentation
- volume calculation
- report generation

Automated internal pipeline that
performs:
- artifact correction
- segmentation
- lesion quantification

VI. Comparison of Technological Characteristics with the Predicate Device:


- volume calculation
- report generation

• MRI scanner: 3D T1 MRI
scans acquired with specified
protocols
• NEUROShield requires
uncompressed DICOM files
as input.

• MRI scanner: 3D T1 MRI
scans acquired with specified
protocols • NeuroQuant
Supports DICOM format as input

Provides volumetric
measurements of
Hippocampus brain structure.

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

• Automated quality control
functions
- Scan protocol verification
• Results must be reviewed
by a trained physician.

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


The distribution for age bands is as follows:


Hausdorff distance
6.1
(3.57, 4.06)
Pass

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


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


HERA W9/HERA W10 (K211824)
Primary Predicate

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

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

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

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

to intended use, imaging capabilities, technological characteristics and safety and effectiveness.


- HDVI
- RealisticVue
- CEUS+
- HQ-Vision
- MV-Flow
- CrystalVue
- CrystalVue Flow*
- 5D CNS+
- 5D Follicle
- 5D Heart Color
- 5D Limb Vol
- 5D LB
- 5D NT
- 2D NT
- IOTA-ADNEX
- BiometryAssist
- E-Cervix
- LumiFlow
- ShadowHDR
- MPI+*
- Slice A
- HeartAssist *
- ViewAssist

HERA W9/ HERA W10 Diagnostic Ultrasound Systems


Software
CrystalVue Flow
Not Supported
Supported

Hardware
Internal DVD
Not Included
Included

Active array probe port
3 port (default), 4 port(option)
4 port

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


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

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

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

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

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

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

applications comply with voluntary standards.


ButterflyiQ/iQ+
Ultrasound System
(Subject Device)

ButterflyiQ
Ultrasound System
(PrimaryPredicate)
(K202406)

Hand heldportable
diagnosticultrasound
system

TheButterflyiQ/iQ+
UltrasoundSystemis
indicatedfor use by
trainedhealthcare
professionalsin
environmentswhere
healthcareisprovidedto
enablediagnostic
ultrasoundimagingand
measurementof
anatomicalstructuresand
fluidsof adultand
pediatricpatientsfor the
followingclinical
applications:
PeripheralVessel
(includingcarotid,deep
veinthrombosisand
arterialstudies),
ProceduralGuidance,
SmallOrgans (including
thyroid,scrotumand
breast),Cardiac,
Abdominal,Lung,
Urology,Fetal/Obstetric,
Gynecological,
Musculoskeletal
(conventional),
Musculoskeletal
(superficial)and
Ophthalmic.
Modes of operation
includeB-mode,B-mode
+M-mode,B-mode+
ColorDoppler,B-mode+

TheButterflyiQ
UltrasoundSystemis
indicatedfor use by
trainedhealthcare
professionalsin
environmentswhere
healthcareisprovided
toenablediagnostic
ultrasoundimagingand
measurementof
anatomicalstructures
andfluidsof adultand
pediatricpatientsfor the
followingclinical
applications:
PeripheralVessel
(includingcarotid,deep
veinthrombosisand
arterialstudies),
ProceduralGuidance,
SmallOrgans
(includingthyroid,
scrotumandbreast),
Cardiac,Abdominal,
Urology,
Fetal/Obstetric,
Gynecological,
Musculoskeletal
(conventional),
Musculoskeletal
(superficial)and
Ophthalmic.
Modes of operation
includeB-mode,
B-mode+M-mode,
B-mode+Color

Additionof “lung”
toIndicationsfor
Use andSpectral
PulsedWave
Dopplerto
operatingmode.


Principlesof
Operation
(subjectAuto
B-LineCounter)

Automaticdetectionand
countingof B-linesfrom
lungultrasoundimages

AutoB-Line
Counteraddedto
existingButterfly
iQ/ButterflyiQ+
UltrasoundSystem
App


Predicate Device [K200905]
HealthMammo ‐ Zebra Medical Vision Ltd.

Characteristics
CogNet QmTRIAGE ‐ MedCognetics

The MedCognetics (CogNet) QmTRIAGE
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 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 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
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
complete 2D FFDM mammography exams
acquired using validated FFDM systems only.

The Zebra HealthMammo is a passive
notification for prioritization‐only, parallel‐
workflow software tool used by MQSA‐
qualified interpreting physicians to prioritize
patients with suspicious findings in the
medical care environment. HealthMammo
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. HealthMammo produces an
exam level output to a PACS/Workstation for
flagging the suspicious case and allows
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.
HealthMammo 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 HealthMammo device is intended for use
with complete 2D FFDM mammography
exams acquired using validated FFDM systems
only.

User
Interpreting physician
Interpreting physician
Same

Yes.
Passive notification flagged for review


Modality
FFDM screening mammograms
FFDM screening mammograms
Same

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

 2D FFDM screening
mammograms
 Biopsy proven cancer studies
(soft tissues and microcalcifications)
 BIRADS 1 and 2 normal cases with 2 year
follow‐up

 Studies with 4 standard views (LCC,
LMLO, RCC, RMLO) BIRADS 1 and 2
normal cases with 2 year follow‐up

 Digital breast tomosynthesis images
 2D synthetic views from tomosynthesis

 Digital Breast tomosynthesis studies
 3D studies
 Studies that did not include all four views
 Studies that do not comply with the
inclusion criteria

Aids in prompt
identification of
cases with indicated
findings

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

Presentation of notification and preview of
the study for initial assessment not meant for
diagnostic purposes. The device operates in
parallel with the standard of care, which
remains the default option for all cases.


Predicate Device
FDA Clearance Number
Product
Manufacturer

equivalent to the following predicate device:


Reference Devices
FDA Clearance Number
Product
Manufacturer

MAGNETOM Avanto with
software syngo MR E11D

MAGNETOM Avantofit
with software syngo MR
E11E

software already cleared on the following reference devices:


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

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

Medical devices - Application of
risk management to medical
devices

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

Medical device software -
Software life cycle processes

62304 Edition 1.1
2015-06
CONSOLIDATED
VERSION

following FDA recognized and international IEC, ISO and NEMA standards:


Acoustic Noise Measurement
Procedure for Diagnosing
Magnetic Resonance Imaging
Devices

Digital Imaging and
Communications in Medicine
(DICOM) Set


FractureDetect (FX)
is a computer-
assisted detection and
diagnosis (CAD)
software device to
assist clinicians in
detecting fractures
during the review of
radiographs of the
musculoskeletal
system. FX is
indicated for adults
only.

Rayvolve is a
computer-assisted
detection and
diagnosis (CAD)
software device to
assist radiologists
and emergency
physicians in
detecting fractures
during the review of
radiographs of the
musculoskeletal
system. Rayvolve is
indicated for adults
only (≥ 22 years old).

Ankle
Clavicle
Elbow
Femur
Forearm
Hip
Humerus
Knee
Pelvis
Shoulder
Tibia / Fibula
Wrist

Ankle
Clavicle
Elbow
Forearm
Hip
Humerus
Knee
Pelvis
Shoulder
Tibia / Fibula
Wrist
Hand
Foot

Rayvolve covers 2 more
anatomical regions than FX;
but the intended use is
similar since both FX and
Rayvolve are intended to
identify fractures in
radiographs, and all those
anatomical regions are
included in the definition of
anatomic area of interest and
radiographic views
consistently with the
American College of
Radiology (ACR) standard
and guidelines.


Clinicians (MSK and
non-MSK radiologist
and emergency
physicians)

Secure local
processing and
delivery of DICOM
images

DICOM node (e.g.,
imaging device,
intermediate DICOM
node, PACS system,
etc.)

PACS system, image
annotations toggled
on or off

PACS system, image
annotations made on
a copy of the original
image

Privacy
HIPAA compliant
HIPAA compliant
Same


mic Area (Nb of images)
AUC (B
ootstrap
ped CI)

nder (Nb of images)
AUC (B
ootstrap
ped CI)

iew (Nb of images)
AUC (B
ootstrap
ped CI)


Classification Name:
Magnetic Resonance Diagnostic Device

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

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


System
Predicate Device
Reference Device

Vantage Galan 3T, MRT-3020, V7.0 with AiCE
Reconstruction Processing Unit for MR

Vantage Fortian 1.5T, MRT-1550, V8.0 with AiCE
Reconstruction Processing Unit for MR

Marketed By
Canon Medical Systems USA, Inc.
Canon Medical Systems USA, Inc.

Clearance Date
August 4, 2021
December 1, 2021

Reference Device: Vantage Fortian, MRT 1550, V8.0 (K213305)


Subject Device:
Vantage Galan 3T, MRT-3020, V8.0

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

Allows screen access to 1st level
operating mode

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

Potential emergency
condition and means
provided for shutdown

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


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

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


EFAI RTSuite CT HN-Segmentation System v1.0

Picture Archiving and Communications System

Medical Image Management and Processing System


Company
Ever Fortune.AI Co., Ltd. (EFAI)
Xiamen Manteia Technology LTD.

EFAI HNSeg is a software device
intended to assist trained radiation
oncology professionals, including,
but not limited to, radiation
oncologists, medical physicists,
and dosimetrists, during their
clinical workflows of radiation
therapy treatment planning by
providing initial contours of organs
at risk in the head and neck region
on non-contrast CT images. EFAI
HNSeg is intended to be used on
adult patients only.
The contours are generated by
deep-learning algorithms and then
transferred to radiation therapy
treatment planning systems. EFAI
HNSeg must be used in conjunction
with a DICOM-compliant
treatment planning system to
review and edit results generated.
EFAI HNSeg is not intended to be
used for decision making or to
detect lesions.
EFAI HNSeg is an adjunct tool and
is not intended to replace a
clinician's judgment and manual
contouring of the normal organs on
CT. Clinicians must not use the
software generated output alone
without review as the primary
interpretation.

It is used by radiation oncology
department to register
multimodality images and segment
(non-contrast) CT images, to
generate needed information for
treatment planning, treatment
evaluation and treatment adaptation.
The product has two image process
functions:
(1)Deep learning contouring: it can
automatically contour the organ-at-
risk, including head and neck,
thorax, abdomen and pelvis (for
both male and female),
(2)Automatic Registration, and
(3)Manual Contour.
It also has the following general
functions:
(1)Receive, add/edit/delete,
transmit, input/export, medical
images and DICOM data;
(2)Patient management;
(3)Review of processed images;
(4)Open and save of files.

Segmentation
Deep learning
Deep learning

Operating System
Linux Ubuntu 20.04
Microsoft Windows

Table - Comparison with the Predicate Device.


Trained medical professionals
including, but not limited to,
radiation oncologists, medical
physicists, and dosimetrists.

It is used by radiation oncology
department.

Segmentation Features: Non-
Contrast CT
Registration Features: CT, MRI,
PET

Image Input
Complies with DICOM standard
Complies with DICOM standard

No Limitation on scanner model
DICOM 3.0 compliance required.

Organ-at-risk, including head and
neck, thorax, abdomen and pelvis
(for both male and female)

No Limitation on TPS model,
DICOM compliance required.

No Limitation on TPS model,
DICOM 3.0 compliance required.

EFAI HNSeg automatically
processes input image data and
sends the results as DICOM-RT
Structure Sets to a user-
configurable
target node.

AccuContour automatically
processes input image data


Regulation No.
21 CFR 892.1000
21 CFR 892.1000
Same

The uMR Omega system is
indicated for use as a
magnetic resonance
diagnostic device (MRDD)
that produces sagittal,
transverse, coronal, and
oblique cross sectional
images, and spectroscopic

Table 1 Comparison of Hardware configuration


The intended use of Head
& Neck Coil - 48 is
equivalent to previously
cleared Head & Neck
Coil - 24. More coil
elements in the new coil
can better cover the
scanning parts.

The intended use of Spine
Coil - 48 is equivalent to
previously cleared Spine
Coil - 32. More coil
elements in the new coil
can better cover the
scanning parts.

The intended use of Head
Coil - 64 is equivalent to
previously cleared Head
Coil - 32. More coil
elements in the new coil
can better cover the
scanning parts.

The intended use of
SuperFlex Body - 24 is
essentially identical to
previously cleared Body
Array Coil - 12. The
differences are the
number of channels of the
coil and the material used
on the surface of the coil.
More coil elements in the
new coil can better cover
the scanning parts and the
flexible material is
beneficial to wrap the
scanning parts.

The intended use of
SuperFlex Large - 12 is
essentially identical to
previously cleared Flex
Coil Large - 8. The
differences are the
number of channels of the
coil
and the material used on
the surface of the coil.
More coil elements in the
new coil can better cover
the scanning parts and the


The intended use of
SuperFlex Small - 12 is
essentially identical to
previously cleared Flex
Coil Small - 8. The
differences are the
number of channels of the
coil and the material used
on the surface of the coil.
More coil elements in the
new coil can better cover
the scanning parts and the
flexible material is
beneficial to wrap the
scanning parts.

width 640mm, height
880mm, length 2620mm

ECG, Peripheral Pulse Gating,
Respiratory Gating

Patient Contact Materials
were tested and
demonstrated no
cytotoxicity (ISO 10993-
5), no evidence for
irritation and sensitization
(ISO 10993-10).

Patient Contact
Materials were tested
and demonstrated no
cytotoxicity (ISO
10993-5), no evidence
for irritation and


The NEMA standards
publication MS 14-
2019 describes the
procedure for heating
RF coil heating under
worst-case normal
operating conditions.
The results for the
surface heating test
showed that proposed
devices perform as
well as or better than
predicate devices.


Fat Analysis
and
Calculation
Technique
(FACT)

FACT is short for fat analysis and calculation
technique and substantially equivalent to WFI.
It not only separates water and fat signal and
quantifies fat fraction and R2* mapping
according to chemical shift effect and T2*
effect.

Inline T1/T2* Mapping is substantially equivalent to

T1/T2* Mapping processed by post-processing

module. The map result displays inline without extra

Inline T2 Map is substantially equivalent to T2 Map

processed by post-processing module. The map result

displays inline without extra operation by post-

ASL is substantially equivalent to FSE and uses extra

arterial spin labeling preparation and imaging

processing for cerebral blood flow (CBF) imaging

FQ is substantially equivalent to GRE and uses extra
flow encoding and imaging processing for flow
quantification.

Table 2 Comparison of the new Application Software Features


Cardiac T1 mapping is substantially equivalent to

GRE and uses multiple TI acquisitions with IR

preparation and imaging processing for cardiac T1

Cardiac T2 mapping is substantially equivalent to

GRE and uses multiple T2-prep duration preparation

acquisitions and imaging processing for cardiac T2

Cardiac T2* mapping is substantially equivalent to

GRE and uses multiple TE acquisitions and imaging

Easy Scan feature allows automatic slice positioning
for Shoulder, Abdomen, L-spine and T-spine
imaging. The positioning can also be adjusted
manually by user. The final positioning effect is
equivalent to manual operation without Easy Scan
feature.

In MR conditional implant mode，user can set the
safety conditions of the MR conditional implant, and
uMR Omega system ensure the scanning complies
with the conditions.

Remote Assistance intends for remote support and
service.

Liver spectroscopy is substantially equivalent to
Spectroscopy and uses multi-echo acquisition and
post-processing instead of single echo for fat
quantification of liver.

Prostate spectroscopy is substantially equivalent to
Spectroscopy and uses characteristic metabolites
detection post processing for prostate spectroscopy.

Breast spectroscopy is substantially equivalent to
Spectroscopy and uses characteristic metabolites
detection post processing for breast spectroscopy.


Table a. DeepRecon American Volunteers' Demographic Distribution


DeepRecon images achieve higher
SNR compared to the images
without DeepRecon (NADR)

Uniformity difference between
DeepRecon images and NADR
images under 5%

Intensity difference between
DeepRecon images and NADR
images under 5%

Table b. The performance evaluation report criteria of DeepRecon


Provides spectroscopy
protocol of the body
including prostate,
breast and liver, which
does not affect safety
and effectiveness.

Table 3 Comparison of the new Post Processing Features


AI-assisted compressed sensing
Yes
Yes
Same


Medical image management and
processing system

uWS-MR is a software solution
intended to be used for viewing,
manipulation, communication, and
storage of medical images. It
supports interpretation and
evaluation of examinations within
healthcare institutions. It has the
following additional indications:
 The MR Stitching is intended
to create full-format images
from overlapping MR volume
data sets acquired at multiple
stages.
 The Dynamic application is
intended to provide a general
post-processing tool for time
course studies.
 The Image Fusion application
is intended to combine two

uWS-MR is a software solution
intended to be used for viewing,
manipulation, communication,
and storage of medical images. It
supports interpretation and
evaluation of examinations
within healthcare institutions. It
has the following additional
indications:
 The MR Stitching is
intended to create full-
format images from
overlapping MR volume
data sets acquired at
multiple stages.
 The Dynamic application is
intended to provide a
general post-processing tool
for time course studies.

Table 5 Comparison of the Basic Functions of uWS-MR


different image series so that
the displayed anatomical
structures match in both
series.
 MRS (MR Spectroscopy) is
intended to evaluate the
molecule constitution and
spatial distribution of cell
metabolism. It provides a set
of tools to view, process, and
analyze the complex MRS
data. This application
supports the analysis for both
SVS (Single Voxel
Spectroscopy) and CSI
(Chemical Shift Imaging)
data.
 The MAPs application is
intended to provide a number
of arithmetic and statistical
functions for evaluating
dynamic processes and
images. These functions are
applied to the grayscale
values of medical images.
 The MR Breast Evaluation
application provides the user
a tool to calculate parameter
maps from contrast-enhanced
time-course images.
 The Brain Perfusion
application is intended to
allow the visualization of
temporal variations in the
dynamic susceptibility time
series of MR datasets.
 MR Vessel Analysis is
intended to provide a tool for
viewing, manipulating, and
evaluating MR vascular
images.
 The Inner view application is
intended to perform a virtual
camera view through hollow
structures (cavities), such as
vessels.

 The Image Fusion
application is intended to
combine two different
image series so that the
displayed anatomical
structures match in both
series.
 MRS (MR Spectroscopy) is
intended to evaluate the
molecule constitution and
spatial distribution of cell
metabolism. It provides a
set of tools to view, process,
and analyze the complex
MRS data. This application
supports the analysis for
both SVS (Single Voxel
Spectroscopy) and CSI
(Chemical Shift Imaging)
data.
 The MAPs application is
intended to provide a
number of arithmetic and
statistical functions for
evaluating dynamic
processes and images.
These functions are applied
to the grayscale values of
medical images.
 The MR Breast Evaluation
application provides the
user a tool to calculate
parameter maps from
contrast-enhanced time-
course images.
 The Brain Perfusion
application is intended to
allow the visualization of
temporal variations in the
dynamic susceptibility time
series of MR datasets.
 MR Vessel Analysis is
intended to provide a tool
for viewing, manipulating,
and evaluating MR vascular
images.


 The DCE analysis is intended
to view, manipulate, and
evaluate dynamic contrast-
enhanced MRI images.
 The United Neuro is intended
to view, manipulate, and
evaluate MR neurological
images.
 The MR Cardiac Analysis
application is intended to be
used for viewing, post-
processing and quantitative
evaluation of cardiac
magnetic resonance data.

 The Inner view application
is intended to perform a
virtual camera view through
hollow structures (cavities),
such as vessels.
 The DCE analysis is
intended to view,
manipulate, and evaluate
dynamic contrast-enhanced
MRI images.
 The United Neuro is
intended to view,
manipulate, and evaluate
MR neurological images.
 The MR Cardiac Analysis
application is intended to be
used for viewing, post-
processing and quantitative
evaluation of cardiac
magnetic resonance data.


4309 Emperor Blvd.,
Durham, NC 27703, USA

Email: msosebee@terarecon.com
Phone: 704-651-6828


Proprietary (Trade) Name of Subject Device

Common / Classification Name
System, Image Processing, Radiological


Proprietary (Trade) Name of Predicate Device


Subject Device
TeraRecon Neuro version 2.0.0
(TBD)

Predicate Device
Neuro.AI Algorithm version
1.0.0
(K200750)

(Used in Qualitative
(Used in Qualitative

FastStroke, CT Perfusion 4D
iSchemaView RAPID

Radiology and could also include
other clinical specialty areas
such as emergency, neurology,
surgery and more

Not specified in K193289’s
510(k) summary.

Hospital LAN, inside the
Hospital Firewall
To be used by trained
professionals.
Radiological data network.

Vendor-neutral - CT, MR and
other volumetric imaging
modalities.
Images are exposed over time.

Head – entire brain or from
lower edge of the base of nucleus
to upper edge of the ventricles.

Not specified in K182130’s
510(k) summary

Table 1: Technological Characteristics Comparison


Microsoft Windows® executable
on off the shelf hardware and
CentOS (Interoperability)

Not specified in K193289’s
510(k) summary

The software runs on a standard
off-the-shelf computer or a
virtual platform, such as
VMware,
and can be used to perform
image viewing, processing and
analysis of images. Data and
images
are acquired through DICOM
compliant imaging devices.
Linux-based server


Key
Functionality/
Feature and
Region-of-
Interest (ROI)
Markers

• Automatic arterial and venous
input function selection
• Ventricle subtraction

CT perfusion 4D is an image
analysis software package, which
allows the user to produce
dynamic image
data and to generate information
with regards to changes in image
intensity over time. It supports
the
analysis of CT Perfusion images
(in the head and body) after the
intravenous injection of contrast,
and
calculation of the various
perfusion-related parameters (i.e.
regional blood flow, regional
blood volume,
mean transit time and capillary
permeability).

The iSchemaView RAPID
provides both viewing and
analysis capabilities for
functional and
dynamic imaging datasets
acquired with CT Perfusion (CT-
P), CT Angiography (CTA), and
MRI including a Diffusion
Weighted MRI (DWI) Module
and a Dynamic Analysis Module
(dynamic contrast-enhanced
imaging data for MRI and CT).
The DWI Module is used to
visualize local water diffusion
properties from the analysis of
diffusion - weighted MRI data.
The Dynamic Analysis Module
is used for visualization and
analysis of dynamic imaging
data, showing properties of
changes in contrast over time.
This functionality includes
calculation of parameters related
to tissue flow (perfusion) and
tissue blood volume.


Subject Device
TeraRecon Neuro version 2.0.0
(TBD)

Predicate Device
Neuro.AI Algorithm version
1.0.0
(K200750)

(Used in Qualitative
(Used in Qualitative

FastStroke, CT Perfusion 4D
iSchemaView RAPID

• Same as predicate device and
• Time to Maximum (Tmax)
• Hypoperfusion maps and
volumes
• Mismatch maps
(penumbra/umbra maps that
are derived from combinations
of measurement parameters)
and related volumes and ratios

• Time to peak (or Time to
Minimum)
• Take off time (or Maximum
Slope of Increase)
• Recirculation time (RT)
• Mean transit time (MTT)
• Blood volume (BV/CBV)
• Blood flow (BF/CBF)
• User c onfigurable settings

• Blood Flow
• Blood Volume
• Mean Transit Time
• Capillary Permeability
• Time to Maximum

• Blood Flow
• Blood Volume
• Mean Transit Time
• Time to Maximum

Artery and Vein Fitted and Raw
curves – time/activity

Not specified in K182130’s
510(k) summary

• Same as predicate device and
• Artery Intensity Profile

Not specified in K193289’s
510(k) summary

Arterial and
Venous Input
Function
Selection

Arterial input function (AIF) and
Venous output function (VOF)


Containerization
/ dockerization
of algorithm that
enables
interoperability
with 3rd party
results including
viewing such
results

• Neuro.AI Algorithm is hosted
on the Eureka platform within
its own docker. The algorithm
is triggered based on input
data and generates result
which will be delivered to
third party system.
• CT and MR Scanners
• 3rd party hospital systems
such as a PACS server, EMR
or other
• TeraRecon Intuition
• Visualization system
• Other image viewing
systems that can support
results generated by the
Neuro.AI Algorithm
• Notification systems

The configuration of
NeuroPackage enables the user
to open a single application,
FastStroke, which provides them
access to both the updated CT
Perfusion 4D
and FastStroke applications.
The capabilities in CT Perfusion
4D
and FastStroke can be offered
independently.

RAPID is available in the
following configurations:
• Standard RAPID, which is
installed directly on a customer's
Linux-based server and
integrated with medical image
processing software such as
commercial PACS.
• Virtual RAPID, wherein the
user accesses RAPID online and
uses it to process DICOM
images otherwise available on
his/her computer.

Setting allows software to
calculate maps with or without
ventricle included.


Primary Predicate Device: SwiftMR – K210999 by AIRS Medical, Inc., Class II,

CFR 892.2050, classification with product code LLZ.

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

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

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

technologists in an imaging center, clinic, or hospital.


Item
Predicate Device
Subject Device
Differences

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

Computer
PC Compatible
Same as predicate
No Difference

The software processes
DICOM-compliant
image data

Modalities
MRI
Same as predicate
No Difference

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

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

The deep learning algorithm using
convolutional neural network-based
filtering performs denoising function
and newly added sharpness filter
performs sharpening function in the
subject device.

1 General sequence
model
1 TOF sequence model

3 General sequence
models
1 TOF sequence model

The general sequence model was
divided into three separate models
for each MRI manufacturer.
The TOF sequence model remains
the same as the predicate device.

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

Supported body parts have been
expanded to spine and MSK (knee,
ankle, shoulder, and hip) in addition
to brain.

The predicate software
operates on DICOM
files on the file system,
enhances the images,
and stores the
enhanced images on
the file system. The

SwiftMR operates on
DICOM files, enhances
the images, and stores
the enhanced images
on PACS.

The subject device can receive
DICOM files either from PACS or
from MR device.
It is possible to store only the
processed images.


receipt of original
DICOM image files and
delivery of enhanced
images as DICOM files
depends on other
software systems.
Enhanced images co-
exist with the original
images.

Enhanced images can
be stored with the
original images or only
the enhanced images
can be stored.


Subject Device
Primary predicate Device
Reference Device

Device name
Neurophet AQUA v2.1
NeuroQuant® v2.2
BrainInsight

Manufacturer
NEUROPHET, Inc.
CorTechs Labs, Inc
Hyperfine Research, Inc

Neurophet AQUA is
intended for Automatic
labeling, visualization
and volumetric
quantification of
segmentable brain
structures from a set of
MR images. Volumetric
data may be compared to
reference percentile data.

NeuroQuant is intended for
automatic labeling,
visualization and
volumetric quantification of
segmentable brain
structures and lesions from
a set of MR images.
Volumetric data may be
compared to reference
percentile data.

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

• Automated
measurement of brain
tissue volumes and
structures
▪ Automatic
segmentation and
quantification of brain
structures using deep
learning

• Automated measurement
of brain tissue volumes and
structures and lesions
• Automatic segmentation
and quantification of brain
structures using a dynamic
probabilistic
neuroanatomical atlas, with
age and gender specificity,
based on the MR image
intensity

▪ Automated measurement
of
brain tissue volumes and
structures
▪ Automatic segmentation
and quantification of brain
structures using machine
learning

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

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

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

Automated internal
pipeline that performs:
- segmentation
- volume calculation
- report generation

Automated internal pipeline
that performs:
- artifact correction
- segmentation
- lesion quantification
- volume calculation

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


• MRI scanner: 3D T1
scans acquired with
specified protocols
• Supports DICOM
format as input

• MRI scanner: 3D T1 and
FLAIR MRI scans acquired
with specified protocols
• Supports DICOM format
as input

▪ MRI scanner: Hyperfine
FSE MRI scans acquired
with
specified protocols
▪ Supports DICOM format
as
input

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

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

Provides volumetric
measurements of brain
structures
▪ Includes segmented color
overlays and
morphometric
reports
▪ Supports DICOM format
as
output of results that can
be
displayed on DICOM
workstations and Picture
Archive and
Communications Systems

• Automated quality
control functions
- Tissue contrast check
- Scan protocol
verification
• Results must be
reviewed by a trained
physician

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

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


Segmentation based on deep
learning tools, volume
calculation and report
generation.

Artifact correction, atlas-based
segmentation, lesion
quantification, volume calculation
and report generation.

Deployment
Installed
Cloud based or installed

Following are the differences between Neurophet AQUA and the predicate device:


Manufacturer
Device Name
Application No.


Neurovascular or
Neurosurgical Specialist

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

Computed Tomography, non-
contrast (NCCT)

Initial assessment; non-
diagnostic purposes

Initial assessment; non-diagnostic
purposes

DICOM Information about the
patient, study and current
image.

has the same non-diagnostic warning on the image viewing screen as the predicate.


Clinical Site
Sensitivity [95% CI]
Specificity [95% CI]


Age Range (Years)
Sensitivity [95% CI]
Specificity [95% CI]


Gender (Years)
Sensitivity [95% CI]
Specificity [95% CI]


Device Performance by Presence of Subdural Hemorrhage and Other Hemorrhage

Hemorrhage Subtypes
Sensitivity [95% CI]
Specificity [95% CI]

Subdural hemorrhage only
0.93 ['0.88', '0.97']

Subdural hemorrhage and
other hemorrhage present


Slice Thickness
Sensitivity [95% CI]
Specificity [95% CI]


Both (Acute and Chronic)
0.98 [0.88, 1.00]


Both (Acute and Chronic) 0.98 [0.88, 1.00]


Device Performance by Scanner Manufacturer

Manufacturer
Sensitivity [95% CI]
Specificity [95% CI]


Device Performance by Scanner Manufacturer/Model

Manufacturer
Model
Sensitivity [95% CI]
Specificity [95% CI]


Device Performance by Subdural Hemorrhage Volume


Device Performance by Scanner Reconstruction Method


DHT8B: Division of Radiological Imaging Devices

Office of Product Evaluation and Quality


Predicate Device -
AI-Rad Companion
Brain MR
(K193290)

Reference Device 1 -
AI-Rad Companion
(Cardiovascular)
(K183268)

Reference Device 2 -
AI-Rad Companion
(Musculoskeletal)
(K193267)

Radiologists &
Physicians from
emergency medicine,
specialty care, urgent
care, and general
practice

Radiologist and neuro-
and spine-surgeons

The intended patient
target group consists
of patients of age 2
years or higher. In
this age range the
brain segmentation
algorithm
works properly.

The intended patient
population is not
subject to any
restrictions.
Automation support
requires images of
patients of 22 years
and older.

The intended patient
population is not subject
to any restrictions.
Automation support
requires images of
patients of 18 years and
older, not pregnant,
without post-operational
complications, scoliosis,
tumors, infections,
fractures.

Similar to Reference
Devices;
Different from Primary
Device

Yes
Segmentation and
quantitative analysis

Yes
Quantitative
comparison of
structure with
normative data or
user-set thresholds

Yes
Volume
measurement of the
heart, total calcium
volume in the
coronary arteries,

Yes
Measure Hounsfield
values within the
vertebras

Table 8.1 – Comparison of Technological Characteristics with Predicate/Reference Devices


maximum diameters
of the aorta at typical
landmarks

Yes
Threshold-based
highlighting of
findings.
Classify each finding
(e.g., enlarged
diameters) by
comparing
measurements
against user-set
thresholds

Yes
Labeling of vertebras
based on the
individual heights of
the vertebras and
whether they
critically differ from
their direct neighbors

Yes
Exportation of results
with the findings for
further reporting

Yes
Exportation of results with
the measurements for
further reporting

Same
None of the reports are
to be used as final
reports. Trained
radiologist or neuro-
and spine-surgeons
need to review, edit and
approve the final report

Deep Convolutional
Image-to-Image Neural
Network

Same as Primary
Predicate Device 1;
Similar to Reference
Devices

CoLumbo 510(k) Premarket Notification Smart Soft Healthcar


IEC 62304:2006/AMD 1:2015 Medical device software — Software life cycle
processes — Amendment 1

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

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

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

NEMA PS 3.1 - 3.20 (2016) Digital Imaging and Communications in Medicine
(DICOM) Set

Table 8.2 - Voluntary Conformance Standards


Dural Sac Area (Axial)
14.8 mm2
12.4 - 17.3 mm2
20 mm2

Lordotic Angle (Sagittal)
2.6°
1.9 - 3.3°
6°

Listhesis/AP Slip (Sagittal)
0.9 mm
0.8 - 1.1 mm
2 mm

Primary end point results: all primary endpoints were met.


Middle VB Height
0.8 mm
0.7 - 0.9 mm
2 mm

Secondary endpoint results: all secondary endpoints on measurement and segmentation were met.


CoLumbo 510(k) Premarket Notification Smart Soft Healthcare


Vertebral Arch and
Adjacent Ligaments (Axial)

Disc Material Outside
Intervertebral Space (Axial)

Disc Mat. Outside IV Space
and/or Bulging Part (Sagittal)


Type Toshiba Siemens GE Toshiba Siemens GE

Compression-
related Tissue
Segmentations


Linear Measurements
0.9 mm
1.1 mm
0.9 - 1.0 mm
1.0 - 1.2 mm
2 mm


Linear Measurements
0.8 mm
0.9 mm
0.9 - 1.0 mm
0.9 - 1.0 mm
2 mm

MAE for software measurements and MDC for software segmentations by gender:


Angle-based Measurements
6°
2.43°
(1.94 – 2.93°)

Linear Measurements
2 mm
0.95 mm
(0.90 – 1.00mm)


Tissue Segmentation Type
Acceptance
51 years old and
Confidence Interval

Angle-based Measurements
6°
2.09°
(1.63 – 2.56°)

Linear Measurements
2 mm
0.97 mm
(0.87 – 1.06mm)


Parameter
K190072 - Predicate Device
Rapid PETN – Subject Device

BriefCase is a radiological computer
aided triage and notification software
indicated for use in the analysis of
non-enhanced head CT and CTPA
images. The device is intended to
assist hospital networks and trained
radiologists in workflow triage by
flagging and communication of
suspected positive findings of
Intracranial Hemorrhage (ICH) and
Pulmonary Embolism (PE)
pathologies. For the PE pathology,
the software is only intended to be
used on single-energy exams.

Rapid PE Triage and Notification (PETN)
is a radiological computer aided triage and
notification software indicated for use in
the analysis of CTPA images. The device
is intended to assist hospital networks and
trained clinicians in workflow triage by
flagging and communication of suspected
positive findings of central pulmonary
embolism (PE) pathology in adults. The
software is only intended to be used on
single-energy exams.
Rapid PETN uses an artificial intelligence
algorithm to analyze images and highlight
cases with detected findings on a server or

PETN that is the subject of this Traditional 510(k) submission.


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

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

Software package which interfaces
to a PACS or allows viewing within
the application

Computer Platform
PC
Server or Workstation

DICOM Compliance
Yes, using CT and CTPA
Yes, using CTPA

No, Device does not mark,
annotate, or direct users’ attention
to a specific location in the original
image

Presentation of a small,
compressed, black and white
preview image that is labeled “Not
for diagnostic use”; The device
operates in parallel with the
standard of care, which remains
the default option for all cases.


Table 1: Age Distribution for Development & Training

PE status
Mean age
Median age
Min age
Max age

Specificity = 0.97. The development data included the following distributions:


Table 2: Gender Distribution for Development & Training


Table 3: Scanner Distribution for Development & Training

PE status
GE
Siemens
Philips
Toshiba
Unknown


Table 4: Age Distribution for Development Validation

model. The data included the following distributions:


Table 5: Gender Distribution for Development Validation


Table 6: Scanner Distribution for Development Validation


Mfr
Measure
N
Estimat
Lower 95%
Upper 95%


Manufacturer
Reconstruction Method
N
% of Total

Table 6: Scanner Mix w/Convolution Kernel Type


• Scanner User Interface (SUI): introduce option to have two SUI set on both
sides of the scanner, while there is only one set available on the left hand
side as the standard configuration for the predicate device MAGNETOM
Free.Max with software version syngo MR XA40A (K210611); Swap the
orientation of patient pictogram on Select&GO is supported syngo MR
XA50A.
• myExam 3D Camera: auto registration with detection of patient height,
weight and orientation are supported in the subject device software version
syngo MR XA50A. The hardware remains unchanged as cleared with
K210611 on July 1, 2021.
• New Patient Video: A new patient video with 1920×1080 pixels is
introduced.

Applicable to the following subject device(s)

• New Patient table –
High-load patient table: a new
fixed patient table with vertical
movement for heavy load patient is
introduced.

New Software Platform/Workflow
myExam Autopilot is extended the supporting body region to shoulder:
• myExam Shoulder Autopilot: it helps users to automate a shoulder
examination.
Migrated Software feature
• EP2D_FID: Single-shot FID EPI pulse sequence type optimized for
perfusion-imaging in the brain.
• Inline Perfusion: Automatic real-time calculation of parameter maps with
Inline technology based on image data acquired with the ep2d_fid pulse
sequence type.
• Access-i: Provides an interface to enable the connection of a 3rd party
workstation to the MR syngo Acquisition Workplace via a network router
and secure local network connection.
Modified Software Platform/Workflow
Modify in Scan assistance: Modified guidance of off-center imaging is provided
to users who encounter the scan suspension by too off-centered shim volume.


Predicate Device
FDA Clearance Number
Product
Manufacturer

Siemens Shenzhen
Magnetic Resonance Ltd.

features from the following reference devices:


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

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

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

Medical devices - Application of
risk management to medical
devices

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

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

Acoustic Noise Measurement
Procedure for Diagnosing
Magnetic Resonance Imaging
Devices


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

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

biological evaluation of medical
devices - part 1: evaluation and
testing within a risk
management process
(Biocompatibility)


equivalent to the following predicate device:


Reference Devices
FDA Clearance Number
Product
Manufacturer

MAGNETOM Verio with
software syngo MR E11D

MAGNETOM Skyrafit with
software syngo MR E11C-
AP04

software already cleared on the following reference devices:


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

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

Medical device software -
Software life cycle processes

62304 Edition 1.1
2015-06
CONSOLIDATED
VERSION

Digital Imaging and
Communications in Medicine
(DICOM) Set

FDA recognized and international IEC, ISO and NEMA standards:


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

Alan Nelson
Chief Science Officer, Radformation


Radiological Image Processing Software For Radiation
Therapy

Medical image management and processing system


Table 3: Technological Characteristics
AutoContour RADAC V2 vs. AutoContour RADAC (K200323)

Predicate Device:
AutoContour RADAC
(K200323)

Manualand Automatic Rigid
registration.Automatic
Deformable Registration

CTor MRinput for contouring
or registration/fusion.
PET/CTinput for
registration/fusion only. DICOM
RTSTRUCT for output

CT input for contouring or
manual registration/fusion.
MR,PET/CTinput for
manual registration/fusion
only. DICOM RTSTRUCT
for output

CTor MRinput for contouring of
anatomical regions: Head and
Neck, Thorax, Abdomen and
Pelvis.
CT Models:
● A_Aorta
● A_Aorta_Asc
● A_Aorta_Dsc
● A_LAD
● Bladder
● Bone_Ilium_L
● Bone_Ilium_R
● Bone_Mandible
● Bowel_Bag
● BrachialPlex_L
● BrachialPlex_R
● Brain
● Brainstem
● Breast_L
● Breast_R
● Bronchus
● Carina
● CaudaEquina
● Cavity_Oral
● Cochlea_L
● Cochlea_R
● Ear_Internal_L
● Ear_Internal_R
● Esophagus
● External
● Eye_L
● Eye_R
● Femur_L
● Femur_R
● Femur_RTOG_L

CT input for contouring of
anatomical regions: Head
and Neck, Thorax,
Abdomen and Pelvis.
CT Models:
● Bladder
● Bone_Mandible
● Brain
● Brainstem
● Bronchus
● Bronchus_Main
● Carina
● Cavity_Oral
● Cochlea_L
● Cochlea_R
● Esophagus
● Eye_L
● Eye_R
● Femur_L
● Femur_R
● Glnd_Lacrimal_L
● Glnd_Lacrimal_R
● Glnd_Submand_L
● Glnd_Submand_R
● Heart
● Kidney_L
● Kidney_R
● Lens_L
● Lens_R
● Liver
● Lung_L
● Lung_R
● OpticNrv_L
● OpticNrv_R
● Parotid_L


● Trachea
● V_Venacava_S
MR Models:
● OpticChiasm
● OpticNrv_L
● OpticNrv_R
● Brainstem
● Hippocampus_L
● Hippocampus_R

Windows based .NET front-end
application that also serves as
agent Uploader supporting
Microsoft Windows 10 (64-bit)
and Microsoft Windows Server
2016.
Cloud-based Server based
automatic contouring application
compatible with Linux.
Windows python-based
automatic contouring application
supporting Microsoft Windows
10 (64-bit) and Microsoft
Windows Server 2016.

Agent Uploader supporting
Microsoft Windows 10
(64-bit) and Microsoft
Windows Server 2016.
Cloud-based Server based
automatic contouring
application compatible with
Linux.
Web application Server
based application
compatible with Linux with
frontend compatible with all
modern web browsers.


These DSC results were compared with state-of-the-art contouring results

published in the literature as well as to the Reference Device MIM Contour ProtégéAI

(K213976) and were found to be consistent with the state-of-the-art for those structures

The test datasets were independent from that used for training and consisted of

20% of the number of training images sets used as input for the model. For CT structure

models there were an average of 700 training and 140 testing image sets. Datasets

used for testing were removed from the training dataset pool before model training

began, and used exclusively for testing. Among the patients used for CT testing 51.7%

CT testing data spanned across treatment subgroups most typically found in a

radiation therapy treatment clinic with the most common diagnosis being cancers of the

Prostate (21%), Breast (21%), Lung (29%), Head and Neck (16%), Other (13%).

datasets used for testing were acquired using a Philips Big Bore CT simulator with the

majority of scans having an average slice thickness of 2mm, In-plane resolution between

1-1.2 mm, and acquisition parameters of 120kVp, 674+/-329 average mAs. Ground

truthing of each test data set were generated manually using consensus (NRG/RTOG)

guidelines as appropriate by three clinically experienced experts consisting of 2 radiation

therapy physicists and 1 radiation dosimetrist.

Sensitivity and specificity for each CT structure model was evaluated on 50

unique patients independent of the training data. 75 structure models had a sensitivity of

100%, 14 models had a sensitivity > 95%, 3 models had a sensitivity > 90%, and 1

For CT specificity, 29 structure models had a specificity of 100%, 14 models had

a specificity > 90%, 12 models had a specificity > 80%, and the remaining 38 models

ranged from 0 to 80%. Note that false positives generally occur when the structure is not

actually in the image, and in such cases this issue is mitigated by AutoContour’s

user-specified structure template system that filters structure results that are not selected


Bortfeld, T. (2021). Glioma Image Segmentation for Radiotherapy: RT targets, barriers to

cancer spread, and organs at risk [Data set]. The Cancer Imaging Archive.

image sets. These training sets consisted primarily of glioblastoma and astrocytoma -


models a mean DSC of 0.67+/-0.08 was found across all structure models.


Sensitivity and Specificity was evaluated on the 16 validation MR datasets that

were not included in the training. All structure models had a sensitivity of 100% and a

specificity of >85% in these test cases.

Limitations of AutoContour related to structure contouring performance and

limitations of the training data are disclosed in the product labeling. AutoContour also

mitigates risk of incorrect contours being propagated into a treatment plan through a

review process that requires users to review every slice and approve the structure prior

to being able to export it to a treatment planning system.

Hippocampus_L Medium 0.65 0.65 0.09 0.50


DHT8B: Division of Radiological Imaging Devices

Office of Product Evaluation and Quality


510(k) Sponsor
AI4MedImaging Medical Solutions S.A.

Rua do Parque Poente, lt 32
4705-002 Sequeira, Braga Portugal

Rory A. Carrillo
Quality and Regulatory Consultant
Cosm

Email: rory@cosmhq.com
Phone: 562-533-7010


Classification Name
System, Image Processing, Radiological

Medical Image Management and Processing System


1These values are derived by performing simple mathematicaloperations and are derived from EDV, ESV, EF, and

2These values are only provided if the patient'sheight and weight are included in the DICOM data.


AI4CMR software is designed to
report cardiac function
measurements (ventricle volumes,
ejection fraction, indices etc.) from
1.5T and 3T magnetic resonance
(MR) scanners. AI4CMR uses
artificial intelligence to
automatically segment and
quantify the different cardiac
measurements. Its results are not
intended to be used on a

The Imbio RV/LV Software device
is designed to measure the
maximal diameters of the right and
left ventricles of the heart from a
volumetric CTPA acquisition and
report the ratio of those
measurements. RV/LV analyzes
cases using an artificial
intelligence algorithm to identify
the location and measurements of
the ventricles. The RV/LV software

6. Substantial Equivalence & Technical Characteristics


stand-alone basis for clinical
decision-making.
The user incorporating AI4CMR
into their DICOM application of
choice is responsible for
implementing a user interface.

provides the user with annotated
images showing ventricular
measurements. Its results are not
intended to be used on a
stand-alone basis for clinical
decision-making or otherwise
preclude clinical assessment of
CTPA cases.


Indication for Use
See table above
See table above
Yes

Cardiovascular images:
multi-phase,
multi-slice acquired from
MRI scanners

Non-gated, CT Pulmonary
Angiography images


Predicate Device
Aidoc Briefcase (K203508)

Subject Device
Aidoc Briefcase (K220709)

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

BriefCase is a radiological computer
aided triage and notification software
indicated for use in the analysis of head
CTA images in adults or transitional
adolescents aged 18 and older.
The device is intended to
assist hospital networks and
appropriately trained medical
specialists in workflow triage
by flagging and communication of
suspected positive findings of complete
Large Vessel Occlusion (LVO) - MCA-
M1, PCA-P1, ACA-A1, ICA, Basilar;
and Medium Vessel Occlusions
(MeVO) - MCA-M2, MCA-proximal M3,
PCA-P2, PCA-proximal P3, ACA-A2,
ACA-proximal A3, and Vertebral-V4.
BriefCase uses an artificial intelligence
algorithm to analyze images and
highlight cases with detected findings
on a standalone desktop application in
parallel to the ongoing standard of care
image interpretation. The user is
presented with for cases with
suspected findings. Notifications
include compressed preview images
that are meant for informational
purposes only and not intended for
diagnostic use
beyond notification. The device does
not alter the original medical image
and is not intended to be used as a
diagnostic device.
The results of BriefCase are intended
to be used in conjunction with other
patient information and based on their
professional judgment, to assist with
triage/prioritization of medical images.


Notified clinicians are responsible for
viewing full images per the standard of
care.

Hospital networks and appropriately
trained medical specialists

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

No. No cases are removed from
Worklist or deprioritized.

No. No cases are removed from
desktop app or deprioritized

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

Inclusion criteria
Head CTA protocol with a 64-slice
●
scanner or higher.
Scans performed on adults/
●
transitional adults ≥ 18 years of age.
Slice thickness 0.5 mm – 1.25 mm.
●
Exclusion Criteria
All scans that are technically
●
inadequate, including motion
artifacts, severe metal artifacts,
suboptimal bolus timing or an
inadequate field of view.

Artificial intelligence algorithm with
database of images.

- AHS module (image acquisition);
- ACS module (image processing));

- AHS module (orchestrator, image
acquisition);
- ACS module (image processing));


- Aidoc Worklist application for
workflow integration (worklist and
non-diagnostic Image Viewer).

- Aidoc Desktop application for
workflow integration (feed and non-
diagnostic Image Viewer).


Table 2. Time-to- Notification Comparison for BriefCase Devices (minutes)


Table 4. Frequency Distribution of Gender


Table 5. Frequency Distribution of Manufacturer


Description and Comparison of the Subject Device to the Predicate Device

General Description:
Transmission of reference points with the offset details to a movable laser
system for patient marking.
Modification:
• Simplified workflow to place isocenters in the spine
• Consistent user-configured default names for POIs throughout the
application

General Description
With Contouring, the user can create, delete, and edit Volumes of Interest
(VOIs).
Modification:
• Tool to convert isodose lines from a DICOM RT dose file to contours
• Minor usability improvement in the structure display in the image
segment

General Description:
The subject device provides features to handle 4D image data, such as the
display of a cine loop of images acquired through gated CT.
Modification:
• Contouring on 4D Image Data improved
• Lobe-based Lung Ventilation implemented

Basic Feature of
syngo.via RT
Image Suite

General Description:
The set provides basic feature of the subject device.
Modification:
A software interface was added to provide additional advanced visualization
and measurement tools to extend the subject device

provided as Table 4 below for the software version SOMARIS/8 VB70:


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

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

Medical devices – Application of risk
management to medical devices; 14971 Second
Edition 2007-03-01

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

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


Medical image
management and
processing system


Multi-modal visualization and rigid- and deformable registration of anatomical and

functional images such as CT, MR, PET-CT, 4D-CT and CBCT

Display of fused and non-fused images to facilitate the comparison and delineation

Manual modification and semi-automatic generation of contours for the regions of

Automatic generation of contours for organs at risk and healthy lymph nodes, based

on medical practices, on medical images such as CT and MR images.

Generation of pseudo-CT for supported anatomies


Reference
device
Syngo.via RT
Image Suite

Radiological image
processing software for
radiation therapy

System,
Planning,
Radiation
Therapy
Treatment

The primary product code
is QKB “Radiological
Image Processing
Software For Radiation
Therapy” as the software
uses AI algorithms and is
intended for radiation
therapy, just like the
primary predicate device

As secondary product
code:
- LLZ (System, Image
Processing,
Radiological) was
included as the
software is used in


image processing and
some predicates use it
as primary or
secondary product
code;
- MUJ (System,
Planning, Radiation
Therapy Treatment)
was includes as it is a
software used in the
planning of
radiotherapy
treatment and some of
the reference devices
use it as their primary
code

Any patient type for
whom relevant modality
scan image data is
available

Any patient type for
whom relevant
modality scan data is
available

Any patient with
soft tissue
cancers in the
pelvic region for
whom
radiotherapy
treatment has
been planned

Any patient with
primary and
metastatic brain
tumor for whom
radiotherapy
treatment has
been planned

The proposed device has
identical target
populations to the primary
and reference devices.

The proposed device
and predicates have
identical target
environments

Intended Use
ART-Plan is a software
for multi-modal
visualization, contouring
and processing of 3D
images of cancer
patients for whom
radiotherapy treatment
has been prescribed.
It allows the user to
view, create and modify
contours for the regions
of interest. It also allows
to generate
automatically, and
based on medical

Intended Use
ART-Plan is a
software designed to
assist the contouring
process of the target
anatomical regions
on 3D-images of
cancer patients for
whom radiotherapy
treatment has been
planned.
The SmartFuse
module allows the
user to register
combinations of
anatomical and

Intended Use
Contour
ProtégéAI is an
accessory to
MIM software
used for the
contouring of
anatomical
structures in
imaging data
using
machine-learnin
g-based
algorithms
automatically.
Appropriate

Intended Use
MIM 4.1
(SEASTAR)
software is
intended for
trained medical
professionals
including, but not
limited to,
radiologists,
oncologists,
physicians,
medical
technologists,
dosimetrists and
physicists.

Intended Use:
MRCAT imaging
is
intended to
provide the
operator with
information of
tissue
properties
for radiation
attenuation
estimation
purposes
in photon
external beam
radiotherapy

Intended use:
Not available in
the summary
Indication for
use:
syngo.via RT
Image Suite is a
3D and 4D
image
visualization,
multimodality
manipulation
and
contouring tool
that helps the

The intended use and
indications for use of
the proposed device,
ART-Plan v1.10.0 and
the primary predicate
ART-Plan v1.6.1 are
similar as they are
both intended for
medical image
registration and
segmentation in the
context of radiotherapy
treatment planning:
they allow multi-modal
and mono-modal rigid


Reference
device
Syngo.via RT
Image Suite

Standalone
software
application
accessed via
a compliant
browser
(Chrome or
Mozilla
Firefox) on a
personal
computer,
tablet or

Provided as a
plug-in clinical
application to
Ingenia MR-RT. It
is compatible with
Ingenia 1.5T and
3.0T MR-RT,
Ingenia Ambition
1.5T MR-RT and
Ingenia Elition
3.0T MR-R. It runs
parallel to image

Provided as a
plug-in clinical
application to
Ingenia MR-RT. It
is compatible with
Ingenia 1.5T and
3.0T MR-RT,
Ingenia Ambition
1.5T MR-RT and
Ingenia Elition
3.0T MR-RT. It
runs parallel to

syngo.via can be
used as a
standalone device
or together with a
variety of
syngo.via-based
software options,
which are medical
devices in their
own right.

The proposed device and
predicates (especially the
primary predicate) have
identical methods of use


phone (In
case of
connection to
the platform
with a screen
of a phone or
a tablet, the
user must
choose the
option for the
desktop site of
his
communicatio
n device. The
platform is
optimally used
with 17 inches
and up
screen.
Facilitates
display and
visualization
of data by
user.

image acquisition
on the MR
console,
embedded
post-processing
generates MRCAT
images using: •
Automated
segmentation and
tissue
classification •
Automated
assignment of
CT-based density
values

Full web
platform
Launch from
Google
Chrome or
Mozilla Firefox
Available on
server-based
application or
Cloud-based
deployment

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

As the density
information is
generated directly
on the MR
console, the
resulting data is
available at the
console for
immediate review.

This solution is
also available
cloud-based,
providing
scalability with
flexible use
models and cloud
deployment1

The proposed device and
predicates are compatible with
identical operating systems.

Data
Visualization
/ Graphical
Interface

The proposed device and all
the predicates have a data
visualisation and graphical
interface

The proposed device and
reference devices such as


Compared to reference
devices, the proposed device
claims less supported
modalities

Distribution of
DICOM
compliant
Images into
other DICOM
compliant
systems.

The system has
the ability to
send data to
DICOM-ready
devices for
image storage,
retrieval and
transmission.

MRCAT images
can be exported in
DICOM format
enabling the use
as primary images
in the treatment
planning systems

DICOM, HL7 and
IHE-RO standard
compliance

The proposed device and
predicates (especially the
primary one) have identical
data export capabilities with
DICOM format.

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

The software can
receive, transmit,
store, retrieve,
display, print,
and process
DICOM objects
and medical
image modalities
including, but not
limited to, CT,
MRI, CR, DX,
MG, US, SPECT,
PET and XA as
supported by
ACR/NEMA
DICOM 3.0.

MR console:
Compatible with
Ingenia 1.5T and
3.0T MR-RT,
Ingenia Ambition
1.5T MR-RT and
Ingenia Elition
3.0T MR-RT
After export,
compatible with
any DICOM
compliant
scanners.

Compatible with
DICOM
Automatic send to
TPS configuration

The proposed device and
predicates (especially the
primary one) have identical
compatibility (DICOM format)


Reference
device
Syngo.via RT
Image Suite

Deep learning
autocontouring
for organs at risk
(incl. lymph
nodes)2

The proposed device,
primary predicate and
most of the reference
devices share an AI
delineation method.

Multi-modal
and
mono-modal.
Rigid and
deformable
Automatic and
manual
initialization
(landmarks,
fusion box,
alignment).
Registration for
the purposes
of replanning/
recontouring
and AI-based
automatic
contouring.

Registration, fusion
display, and review
of medical images
for diagnosis,
treatment
evaluation, and
treatment planning.

Image Fusion
Rigid and
Deformable
Registration with
region-of interest
based
registration and
multiple
registrations per
image pair
Manual editing
of registrations
Save
registrations and
save deformed
images as
reformatted
dataset2
Contour warping
and display of
prior and new
structure set
Registration
Quality Check
with spyglass,
deformation
vector map,

Both the predicate device
and ART-Plan offer
mono-modal (CT-CT) and
multi-modal (CT/MR,
CT/PET) rigid and
deformable registration.
However, the proposed
device supports 2
additional modalities:
- CBCT (covered
by the reference
deviceSyngo.via
RT Image Suite)
- 4D-CT (covered
by the reference
deviceSyngo.via
RT Image Suite
Both devices offer an
automatic solution for
registration and
semi-automatic registration
by including manual
initialization tools in
addition to automatic
initialization.


Reference device such as
Syngo.via RT Image Suite
offers the same options as
the proposed device: rigid
and deformable
registration.

Automatically
delineates
OARs and
healthy lymph
nodes
Deep learning
algorithm.
Automatic
segmentation
includes the
following
localizations:
* head and
neck (on CT
images)
* thorax/breast
(for
male/female
and on CT
images)
* abdomen (on
CT images and
MR images)
* pelvis
male(on CT
images and
MR images)
* pelvis female
(on CT
images)
* brain (on CT
images and
MR images)

Automatically
delineates
OARs and
healthy lymph
nodes (on any
CT images)
Deep learning
algorithm.
Automatic
segmentation
includes the
following
localizations:
* head and
neck
* thorax/breast
(for
male/female)
* abdomen
* pelvis (for
male only)
* brain.

Creation of
contours using
machine-learning
algorithms for
applications
including, but not
limited to,
quantitative
analysis, aiding
adaptive therapy,
transferring
contours to
radiation therapy
treatment planning
systems, and
archiving contours
for patient follow-up
and management.
Segmenting
anatomical
structures across a
variety of CT
anatomical
locations.
And segmenting
normal structures
of the prostate,
seminal vesicles,
and urethra within
T2-weighted MR
images.

The software
automatically
generates contours
using a deformable
registration
technique which
registers
pre-contoured
patients to target
patients.
Registrations are
either
between a serial
pair of intra-patient
volumes or
between a
pre-existing atlas of
contoured patients
and a patient
volume. This
process facilitates
contour creation or
re-contouring for
adaptive therapy.

Multimodality
contouring
Freehand 2D,
3D image-based
Smart Freehand
segmentation, s
Contour on any
arbitrary plane
including oblique
planes
deep learning
autocontouring
for organs at risk
(inclusive LNs)
One-click
adaptive
contouring
User
configurable
Organ
Templates
Multiple
structure set
support (1 per
image series)
Molecular
imaging data
such as PET,
threshold-based
and skin, gray
value-based
segmentation1
“CT-free”
contouring:
native PET or
MR contouring

The proposed device and
primary predicate are
capable of automatically
contouring the
organ-at-risk (OAR) and
healthy lymph nodes using
AI (deep learning)
algorithm.
There is a difference in
intended anatomies for CT
images as the proposed
device also includes pelvis
female.
For MR images, all
anatomies included in the
proposed device are also
included in the primary
predicate.


Parallel
contouring:
contouring
performed on
any image is
reflected on all
other images
Visualization of
previously drawn
structures on the
current image
series
Contour copy
and warping
between image
series2

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

Organ algebra
(union,
intersection,
exclusion)
Symmetric and
asymmetric
structure growth
or contraction
Smart 2D/3D
Nudge, brush
Pan, scale,
rotate contour
Geometrical and
smart
image-based
contour
interpolation
Multi-modality
Image
Manipulation
Multiplanar
reconstruction
(MPR) thin/thick,
minimum
intensity
projection (MIP),
volume

The proposed device has
the same tools as the
primary predicate.


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

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

AI Based
contouring, tools to
quickly create,
transform, and
modify contours.

Atlas based
contouring, tools to
quickly create,
transform, and
modify contours.

syngo.via RT
Image Suite
provides
dedicated tools,
which help the
medical
professional in
contouring
and evaluating
volumes of
interest.
Freehand and
semi-automatic
contouring of
regions-of-intere
st on any
orientation
including
oblique2

Both the proposed device
and the primary predicate
allow AI automatic
contouring and manual
contouring

Region/volum
e of
interest
measurement
s and
size
measurement
s

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

The proposed device
offers the same kind of
region/volume of interest
measurements and size
measurements as the
primary predicate


Information about your training dataset:

● Summary test statistics or other test results including acceptance criteria or

other information supporting the appropriateness of the characterised

ranges extracted from benchmarkdevicesandalternativetechnologiesintheliterature.Foran

auto segmentation model to be judged acceptable, every organ included in the model must

pass at least one acceptance criterion with success across the different testings it has been

submitted to. These criteria are as follows:

A. The Dice Similarity Coefficient(DSC)isequaltoorsuperiortotheacceptance

criteria set by the AAPM: DSC (mean)≥ 0.8.

B. The Dice Similarity Coefficient (DSC) is equal to or superior to inter-expert

variability: DSC (mean)≥ 0.54 or DSC (mean) ≥ mean (DSC inter-expert) + 5%

C. The clinicians’ s qualitative evaluation of the auto-segmentationisconsidered

acceptable for clinical use without modifications (A) or with minor

modifications / corrections (B) with a A+B % above or equal to 85%

A: the contour is acceptable for a clinical use without any modification

B: the contour would be acceptable for clinical use after minor

C: the contour requires major modifications (e.g.itwouldbefasterfor

the expert to manually delineate the structure)”

For the synthetic-CT generation tool, the acceptance criteria are as follows:

A. A median 2%/2mm gamma passing criteria of ≥95%

B. A median 3%/3mm gamma passing criteria of ≥99.0%

C. A mean dose deviation (pseudo-CT compared to standard CT) of ≤2% in

● Total number of individual patients images in the reported auto segmentation

tools and independence of test data and training data

Our training, validation andtestcohortsarebuiltfromreal-worldretrospectivedatawhichwere

initially used for treatmentofcancerpatients.Forthestructuresofagivenanatomyforagiven

modality (MR or CT),twonon-overlappingdatasetswereseparated:thetestpatients(number

selected based on thorough literature review and statistical power) and the train data. We

make sure that those sets are non-overlapping and further split the train cases into train and

validation sets and ensure enough train cases for the machine learning models to converge

and achieve good performances of the validation set.