Reader study with 6720 reads:
• fully crossed multi-reader,
multi-case design
• 240 cases
• 14 radiologists
Case-level ROC:
• radiologists’ AUC unaided =
0.866
• radiologists’ AUC aided =
0.886
• mean difference: +0.020,
95% C.I. [0.010, 0.030]

Reader study with 2700 reads:
• 150 cases were read by 9
trained physicians in two
separate reads – first without
ProstatID, and second with
ProstatID.
Case-level ROC for discriminating
Gleason Score ≥ 7 in those 130 out
of the 150 cases that had biopsy
results:
• radiologists’ AUC unaided =
0.629
• radiologists’ AUC unaided =
0.671
• mean difference: +0.042,
95% C.I. [0.005, 0.080]
Lesion-level wAFROC:
• radiologists’ AUC unaided =
0.387
• radiologists’ AUC aided =
0.430
• mean difference: +0.043,
95% C.I. [0.003, 0.083]

Reader study with 4080 reads:
• split-plot multi-reader, multi-
case design
(comprising 2 fully crossed
multi-reader, multi-case splits)
• 2× 170 = 340 cases from 2
consecutively acquired cohorts
• 2× 6 = 12 radiologists
• Two analysis scenarios:
a) all cases, all readers
b) only cases with biopsy, or a
negative MRI and ≥12
months negative follow-up
by PSA or MRI (exclusion
of 34 cases), and only case-
reader pairs in which
readers were not involved
in prostate MRI reading at
the institution providing the
case during the time of data
collection (exclusion of
459 case-reader pairs)
Case-level ROC for
discriminating Gleason Score ≥ 6:
• radiologists’ AUC (unaided) =
a) 0.676; b) 0.658
• radiologists’ AUC (aided) =
a) 0.701; b) 0.695
• mean difference:
a) +0.025,
95% C.I. [0.001, 0.049]
b) +0.037,
95% C.I. [0.011, 0.063]
Lesion-level wAFROC:
• radiologists’ AUC unaided =
a) 0.734; b) 0.772
• radiologists’ AUC aided =
a) 0.769; b) 0.834
• mean difference:
a) +0.035,
95% C.I. [0.002, 0.068]
b) +0.030,
95% C.I. [0.008, 0.052]

Equivalent –
The difference
between the
predicate and
subject
devices can be
justified by
comparing
with the
performance
of the
reference
device. Reader
studies of both
reference and
subject
devices used
similar
statistically
meaningful
case numbers,
qualified
readers,
acceptance
criteria and
yielded
comparable
study results.


Firstedition
2021-04
Corrected
version
2021-12

voluntary FDA recognized standards and guidelines:


The following guidance documents were used to support this submission:


show any deviations in specific sub-groups.


Similar;Both
productcodes
refertothe
sameCFR
892.2050-
Medicalimage
management
and
processing
system

LimbusContourisasoftwareonly
medicaldeviceintended foruse
bytrainedradiation oncologists,
dosimetristsand physiciststo
deriveoptimal contoursforinput
toradiation treatmentplanning.
Supportedimagemodalities are
ComputedTomography and
MagneticResonance.The Limbus
ContourSoftware assistsinthe
following
scenarios:
•Operatesinconjunctionwith
radiationtreatmentplanning
systemsorDICOMviewing
systemstoload,save,and display
medicalimagesand contoursfor
treatment
evaluationandtreatmentplanning.
•Creation,transformation,and
modificationofcontoursfor

LimbusContourisasoftwareonly
medicaldeviceintended foruse
bytrainedradiation oncologists,
dosimetristsand physiciststo
deriveoptimal contoursforinputto
radiation treatmentplanning.
Supportedimagemodalities are
ComputedTomography and
MagneticResonance.The Limbus
ContourSoftware assistsinthe
following
scenarios:
•Operatesinconjunctionwith
radiationtreatmentplanning
systemsorDICOMviewing
systemstoload,save,and display
medicalimagesand contoursfor
treatment
evaluationandtreatmentplanning.
•Creation,transformation,and
modificationofcontoursfor

automatic contouring test to ensure the contours were accurate.


applicationsincluding,butnot
limitedto:transferringcontoursto
radiotherapytreatmentplanning
systems, aidingadaptivetherapy
and archivingcontoursforpatient
follow-up.
•Localizationanddefinitionof
healthyanatomicalStructures.
LimbusContourisnot
intendedforusewithdigital
mammography.

IntendedUser Healthcareproviders Healthcareproviders Same

LimbusContourhasnodata
visualization.Dataprocessingis
automatedanddoesnotrequireuser
interaction.Acontrolinterfaceis
providedforsystemadministration
andconfigurationonly.
Visualizationsoftwaremustbeused
tofacilitatethereviewandeditofthe
generatedcontours.

CTStructures
● A_Aorta
● A_Aorta_l
● A_Celiac
● A_LAD
● A_Mesenteric_S
● A_Pulmonary
● Bag_Bowel
● Bag_Bowel_Extend
● Bag_Bowel_Full
● Bag_Bowel_S
● Bladder
● Body
● Bone_Hyoid

CTStructures
● A_Aorta
● A_Aorta_Base
● A_Aorta_I
● A_Celiac
● A_LAD
● A_Mesenteric_S
● A_Pulmonary
● Atrium_L
● Atrium_R
● Bowel_Bag
● Bowel_Bag_Extend
● Bowel_Bag_Full
● Bowel_Bag_Superior

Similar;The
subjectdevice
addsnew
structuresfor
existing
supported
image
modalities
(CT/MR)


● Bone_Ilium_L
● Bone_Ilium_R
● Bone_Ilium
● Bone_Mandible
● Bowel
● Bowel_Extend
● Bowel_Full
● Bowel_S
● BrachialPlex_L
● BrachialPlex_R
● BrachialPlexs
● Brain
● Brainstem
● Breast_L
● Breast_R
● Breasts
● Bronchus
● Canal_Anal
● CaudaEquina
● Cavity_Oral
● Chestwall_L
● Chestwall_R
● Chestwalls
● Clavicle_L
● Clavicle_R
● Cochlea_L
● Cochlea_R
● Colon_Sigmoid
● Cornea_L
● Cornea_R
● Duodenum
● Esophagus
● Eye_L
● Eye_R
● Eyes
● Femur_Head_L
● Femur_Head_R
● Femur_Heads
● Gallbladder
● Glnd_Lacrimal_L
● Glnd_Lacrimal_R
● Glnd_Submand_L
● Glnd_Submand_R
● Glnd_Thyroid
● GreatVes
● Heart
● Hippocampus_L
● Hippocampus_R
● Humerus_L
● Humerus_R
● Kidney_L
● Kidney_R
● Kidneys
● Larynx
● Lens_L
● Lens_R
● Lips
● Liver
● LN_Ax_Sclav_L

● Bowel
● Bowel_Extend
● Bowel_Full
● Bowel_Superior
● Bladder
● Body
● Body+Mask
● Bone_Hyoid
● Bone_Ilium_L
● Bone_Ilium_R
● Bone_Ilium
● Bone_Ischium_L
● Bone_Ischium_R
● Bone_Mandible
● Bone_Pelvic
● BoneMarrow_Pelvic
● BrachialPlex_L
● BrachialPlex_R
● BrachialPlexs
● Brain
● Brainstem
● Breast_Implant_L
● Breast_Implant_R
● Breast_L
● Breast_R
● Breasts
● Bronchus
● Canal_Anal
● Carina
● CaudaEquina
● Cavity_Oral
● Cerebellum
● Chestwall_L
● Chestwall_R
● Chestwall
● Clavicle_L
● Clavicle_R
● Cochlea_L
● Cochlea_R
● Colon_Sigmoid
● Cornea_L
● Cornea_R
● Duodenum
● Esophagus
● Eye_L
● Eye_R
● Eyes
● Femur_Head_L
● Femur_Head_R
● Femur_Heads
● Gallbladder
● Glnd_Lacrimal_L
● Glnd_Lacrimal_R
● Glnd_Submand_L
● Glnd_Submand_R
● Glnd_Thyroid
● GreatVes
● Heart
● Heart+A_Pulm


● LN_Ax_Sclav_R
● LN_Ax_L1_L
● LN_Ax_L1_R
● LN_Ax_L2_L
● LN_Ax_L2_R
● LN_Ax_L3_L
● LN_Ax_L3_R
● LN_Sclav_L
● LN_Sclav_R
● LN_IMN_L
● LN_IMN_R
● LN_Neck_L
● LN_Neck_R
● LN_Neck_234_L
● LN_Neck_234_R
● LN_Neck_2347AB_L
● LN_Neck_2347AB_R
● LN_Neck_IA
● LN_Neck_IA6
● LN_Neck_IB_L
● LN_Neck_IB_R
● LN_Neck_II_L
● LN_Neck_II_R
● LN_Neck_III_L
● LN_Neck_III_R
● LN_Neck_IV_L
● LN_Neck_IV_R
● LN_Neck_V_L
● LN_Neck_V_R
● LN_Neck_VI
● LN_Neck_VIIAB_L
● LN_Neck_VIIAB_R
● LN_Pelvis
● Lung_L
● Lung_R
● Lungs
● Musc_Constrict
● Musc_PecMinor_L
● Musc_PecMinor_R
● Musc_Sclmast_L
● Musc_Sclmast_R
● OpticChiasm
● OpticNrv_L
● OpticNrv_R
● Pancreas
● Parotid_L
● Parotid_R
● PelvisVessels
● PenileBulb
● Pituitary
● Prostate
● Prostate+SeminalVes
● PubicSymphys
● Rectum
● Retina_L
● Retina_R
● Ribs_L
● Ribs_R
● Ribs

● Hippocampus_L
● Hippocampus_R
● Humerus_L
● Humerus_R
● InternalAuditoryCanal_L
● InternalAuditoryCanal_R
● Kidney_L
● Kidney_R
● Kidneys
● Larynx
● Lens_L
● Lens_R
● Lips
● Liver
● Lung_L
● Lung_R
● Lungs
● Mesorectum
● Musc_Constrict
● Musc_PecMinor_L
● Musc_PecMinor_R
● Musc_Sclmast_L
● Musc_Sclmast_R
● Optics
● OpticChiasm
● OpticNrv_L
● OpticNrv_R
● Pancreas
● Parotid_L
● Parotid_R
● PelvisVessels
● PenileBulb
● Pericardium
● Pericardium+A_Pulm
● Pituitary
● Prostate
● Prostate+SeminalVes
● ProstateBed
● PubicSymphys
● Rectum
● Retina_L
● Retina_R
● Ribs_L
● Ribs_R
● Ribs
● Sacrum
● SeminalVes
● Skin
● SpinalCanal
● SpinalCord
● Spleen
● Sternum
● Stomach
● Trachea
● Uterus+Cervix
● V_Venacava_I
● V_Venacava_S
● Vagina
● VB_C1


● Sacrum
● SeminalVes
● Skin
● SpinalCanal
● SpinalCord
● Spleen
● Sternum
● Stomach
● Trachea
● Uterus_Cervix
● V_Venacava_I
● V_Venacava_S
● Vagina
● Ventricle_L
MRStructures
● Brainstem
● Cornea_L
● Cornea_R
● Eye_L
● Eye_R
● Hippocampus_L
● Hippocampus_R
● Optics
● PenileBulb
● Prostate
● Retina_L
● Retina_R
● SeminalVes

● VB_C2
● VB_C3
● VB_C4
● VB_C5
● VB_C6
● VB_C7
● VB_L1
● VB_L2
● VB_L3
● VB_L4
● VB_L5
● VB_T01
● VB_T02
● VB_T03
● VB_T04
● VB_T05
● VB_T06
● VB_T07
● VB_T08
● VB_T09
● VB_T10
● VB_T11
● VB_T12
● VBs
● Ventricle_L
● Ventricle_R
● Bladder_HDR
● Bowel_HDR
● Canal_Anal_HDR
● Colon_Sigmoid_HDR
● Rectum_HDR
● Urethra_HDR
● Bladder_CBCT
● Femur_Head_L_CBCT
● Femur_Head_R_CBCT
● LN_Pelvics_CBCT
● Prostate_CBCT
● Rectum_CBCT
● SeminalVes_CBCT
MRStructures
● Bladder
● Brainstem
● Cornea_L
● Cornea_R
● Eye_L
● Eye_R
● Femur_Head_L
● Femur_Head_R
● Hippocampus_L
● Hippocampus_R
● Optics
● PenileBulb
● PubicSymphys
● Prostate
● Rectum
● Retina_L
● Retina_R
● Sacrum


mostoftheanatomicalstructures
examined,theDiceScores(DSC)
betweenLimbusContourandexpert
contourswerenotsignificantly
differentfromtheinter-observer
expertvariabilitybaselines.
TheinvestigationbyWong,Huang,
Wellsetal.,2021evaluated
implementationofLimbusContour
prospectivelyattwoCanadian
institutions.LimbusContourwas
usedtogenerateOARandCTVsfor
allpatientsundergoingRTfora
centralnervoussystem(CNS),head
andneck(H&N),orprostatecancer.
Automaticcontoursweregenerated
onapproximately551eligiblecases.
203surveyswerecollectedon27
CNS,54H&N,and93prostateRT
plans,resultinginanoverallsurvey
compliancerateof32%.The
majorityofOARautomaticcontours
requiredminimaleditssubjectively
(meaneditingscore ≤ 2)and
objectively(meanDSCand95%HD
was ≥ 0.90and ≤ 2.0mm,
respectively).
Overall,thousandsoftotalstructures
wereanalyzedacrossall
investigationscoveringallsupported
anatomicalsitestoprovidea
substantialsampleofdeviceoutputs
todrawconclusionsthatanatomical
structurecontouroutputsoffergood
geometricalignmentwithexpert
contours,requireminimalediting,
andsignificantlyreducethetime
neededforcontouring.

oftheanatomicalstructures
examined,theDiceScores(DSC)
betweenLimbusContourandexpert
contourswerenotsignificantly
differentfromtheinter-observer
expertvariabilitybaselines.
TheinvestigationbyWong,Huang,
Wellsetal.,2021evaluated
implementationofLimbusContour
prospectivelyattwoCanadian
institutions.LimbusContourwas
usedtogenerateOARandCTVsfor
allpatientsundergoingRTfora
centralnervoussystem(CNS),head
andneck(H&N),orprostatecancer.
Automaticcontoursweregenerated
onapproximately551eligiblecases.
203surveyswerecollectedon27
CNS,54H&N,and93prostateRT
plans,resultinginanoverallsurvey
compliancerateof32%.Themajority
ofOARautomaticcontoursrequired
minimaleditssubjectively(mean
editingscore ≤ 2)andobjectively
(meanDSCand95%HDwas ≥ 0.90
and ≤ 2.0mm,respectively).
Overall,thousandsoftotalstructures
wereanalyzedacrossall
investigationscoveringallsupported
anatomicalsitestoprovidea
substantialsampleofdeviceoutputs
todrawconclusionsthatanatomical
structurecontouroutputsoffergood
geometricalignmentwithexpert
contours,requireminimalediting,
andsignificantlyreducethetime
neededforcontouring.

OperatingSystem •Windows10/
Windows Server2016andAbove
HardwareRequirements •2GHzor
fastermulticore processor •8GBof
RAM •ForGPUversions,a CUDA
capableNVIDIA GPUisrequired

OperatingSystem •Windows10/
Windows Server2016andAbove
HardwareRequirements •2GHzor
fastermulticore processor •16GB
ofRAM •ForGPUversions,a
CUDAcapableNVIDIA GPUis
required

Similar;
Limbus
Contourv1.8.0
requires16GB
ofRAM
insteadof8
GBtosupport
added
structuresfor
automatic
contouring


ExportedLimbusContourDICOM
RT-StructureSetfilesareimported
intotheTreatmentPlanningSystem
orDICOMViewerthrough
interactionswiththeFileSystem


Structure Modality Bodysite Numberoftrainingscans Numberofvalidationscans


Test DSC Threshold. Full testing results for all structures are provided in the table below:


510(k) Number
Device/Manufacturer
Predicate/Reference

DEN220040
Fibresolve / Imvaria, Inc
Predicate

ScreenDx is substantially equivalent to the following predicate:


Pulmonary symptoms suggestive of possible
ILD including IPF.

Clinicians qualified in the care of lung disease,
specifically in caring for patients with ILD

Target Population
Age > 22 years old.
Age > 22 years old.
Same

CT scans acquired in general assessment of
thoracic conditions

CT scans acquired in the work-up of patients
with suspected ILD and IPF

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

Algorithm
Machine learning pattern recognition
Machine learning pattern recognition
Same

Qualitative classification output of imaging
findings

Process a wide array of input images to flag
cases for possible follow-up by a specialist

Specifically ordered by a specialist to gather
additional discriminatory information


Native Hawaiian or other Pacific Islander


observed to be 91.4% (89.0 - 93.3%) and specificity was observed to be 95.2% (CI: 94.3 - 96.1%).


diseases. Results were analyzed within smoking subgroups.


Device Performance by CT Slice Thickness


the study were assessed to determine whether the device was able to identify positive cases.


Modification
Rationale
Testing Methods
Impact Assessment

Update model
architecture or
training data

With additional real-
world data and
ongoing assessments
of real-world
performance of the
model, re-training a
new model allows for
potential
improvements in
generalizability which
provides greater
clinical value.

Substantial
equivalence as
compared to the
prior version.
Statistical
assessments
following same
standards used in
original device
clearance.

Revised
generalizability or
accuracy metrics for
the system.
Benefit-Risk Analysis:
Benefit: Enhanced
performance;
generalizability.
Risk: Reduction in
clinical performance
or generalizability.
Risk Mitigation:
Evaluate device
model on Test
dataset metrics.
Execute unit and
integration tests for
the product code.

With additional real-
world data and
ongoing assessments
of real-world
performance of the
model, a change in
optimized threshold
targeting may
provide a better
balance of sensitivity
and specificity for the
appropriate
populations.

Revised relative
performance of
sensitivity,
specificity, PPV, and
NPV for real-world
use.
Benefit-Risk Analysis:
Benefit: Enhanced
performance;
generalizability.
Risk: Reduction in
clinical performance
or generalizability.
Risk Mitigation:
Evaluate device
model on Test
dataset metrics.
Execute unit and
integration tests for
the product code.


Subject Device
Primary Device
Reference Device

Myomics
Myomics Q (K211432)
cvi42 Auto (K213998)

Manufactured by Phantomics
Manufactured by Phantomics
Manufactured by Circle

Myomics is intended to be used for viewing, post-
processing, qualitative and quantitative evaluation
of cardiovascular magnetic resonance (MR)
images in a Digital Imaging and Communications
in Medicine (DICOM) standard format.
It enables a set of tools to assist physicians in
qualitative assessment of cardiac images and
quantitative measurements of the heart and
adjacent vessels; and to view the presence or
absence of physician-identified lesion in blood
vessels.
The target population for manual workflows of
Myomics is not restricted; however, semi-
automated machine learning algorithms of
Myomics are intended for an adult population.
The software comprises various analysis modules,
including AI-powered algorithms, for a
comprehensive evaluation of MR images.
Myomics is used for cardiac images acquired
from a 3.0 T MR scanner.

Myomics Q is intended to be used for viewing,
post-processing and analysis of cardiac magnetic
resonance (MR) images in a Digital Imaging and
Communications in Medicine (DICOM) Standard
format. It enables:
- Importing cardiac MR images in DICOM format.
- Supporting clinical diagnostics by analysis of
cardiac MR images using display functionality such
as panning, windowing, zooming through
series/slices of the images.
- Supporting clinical diagnostics analysis of the
heart in cardiac MR images and signal intensity.
- Software package is designed to support the
physician-to-physician compliance assessment,
document and follow up heart disease by cardiac
MRI.
It shall be used by qualified medical professionals,
experienced in examining and evaluating
cardiovascular MR images, for the purpose of
obtaining diagnostic information as part of a
comprehensive diagnostic decision-making process.
This device is a software application that can be
used as a stand-alone product or in a network
environment.

cvi42 Auto is intended to be used for
viewing, post-processing, qualitative and
quantitative evaluation of cardiovascular
magnetic resonance (MR) and computed
tomography (CT) images in a Digital Imaging
and Communications in Medicine (DICOM)
Standard format.
It enables a set of tools to assist physicians in
qualitative assessment of cardiac images and
quantitative measurements of the heart and
adjacent vessels; perform calcium scoring;
and to confirm the presence or absence of
physician-identified lesion in blood vessels.
The target population for cvi42 Auto’s
manual workflows is not restricted; however,
cvi42 Auto’s semi-automated machine
learning algorithms are intended for an adult
population.
cvi42 Auto shall be used only for cardiac
images acquired from an MR or CT scanner.
It shall be used by qualified medical
professionals, experienced in examining and
evaluating cardiovascular MR or CT images,

Table 1. Comparison to the predicate devices


Myomics shall be used only for cardiac images
acquired from an MR scanner. It shall be used by
qualified medical professionals, experienced in
examining and evaluating cardiovascular MR
images, for the purpose of obtaining diagnostic
information as part of a comprehensive diagnostic
decision-making process.

The target population for the device is not
restricted, however the image acquisition by a
cardiac MR scanner may limit the use of the device
for certain sectors or the public.


Device Name
Myomics
Myomics Q
cvi42 Auto Imaging Software Application

Manufacturer
Phantomics Inc.
Phantomics Inc.
Circle Cardiovascular Imaging Inc.

Picture Archiving and Communication
System

Classification
Class II
Class II
Class II

Table 2. General comparison to the predicate devices


Imaging Type
Cardiovascular
Cardiovascular
Cardiovascular

Yes (Endocardium and epicardium contour
segmentation)

Store images
Yes (File Format: dcm, json, nii, csv)
Yes (File Format: dcm, json, nii, csv)
Yes (File Format: PDF, XML)

Machine Learning
Yes (Semi-automatic segmentation)
No
Yes (Semi-automatic segmentation)

Quantitative
Assessment of
Cardiac Function

Yes (Manual segmentation, and semi-
automatic segmentation using Machine
Learning technique of four heart chambers in
short-axis views)

Yes (Manual segmentation, and semi-
automatic segmentation using Machine
Learning technique of four heart chambers in
long and short-axis views)

Operating System
Microsoft Windows
Microsoft Windows
Mac OS and Microsoft Windows

Table 3. Feature comparison table of Myomics with the predicate devices


AI Module No.
Data Group (AI Model Name)
Training
Validation
Test

AIM-01
Native T1 Map Myocardium Segmentation
594 cases
42 cases
92 cases

AIM-02
Post T1 Map Myocardium Segmentation
498 cases
41 cases
91 cases

AIM-03
T2 Map Myocardium Segmentation
586 cases
59 cases
109 cases

AIM-04
CINE Myocardium Segmentation
640 cases
40 cases
90 cases

AIM-05
LGE PSIR Myocardium Segmentation
491 cases
27 cases
77 cases

AIM-06
CINE RV Myocardium Segmentation
437 cases
142 cases
192 cases

AIM-07
LGE Magnitude Myocardium Segmentation
477 cases
27 cases
77 cases

Table 4. AI Module Description in Myomics


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

8F., No.360, Sec. 1, Jingmao Rd.,
Beitun Dist.,
Taichung City 406040,
Taiwan

886-04-23213838 #168
thothwang@gmail.com
tihao.wang@everfortune.ai


EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT
SYSTEM (MLS-CT-100)

Radiological computer-assisted triage and notification software


prioritizing the clinical assessment of
non-contrast head CT cases with
features suggestive of midline shift
(MLS) in individuals aged 18 years
and above. EFAI MLSCT analyzes
cases using deep learning algorithms
to identify suspected MLS findings. It
makes case-level output available to a
PACS/workstation for worklist
prioritization or triage.
EFAI MLSCT is not intended to direct
attention to specific portions of an
image or to anomalies other than
MLS. Its results are not intended to be
used on a stand-alone basis for clinical
decision-making nor is it intended to
rule out MLS or otherwise preclude
clinical assessment of CT studies.

The device is intended to assist
hospital networks and trained medical
specialists in workflow triage by
flagging the following suspected
positive findings of pathologies in
head CT images: intracranial
hemorrhage, mass effect, midline
shift and cranial fracture.
qER uses an artificial intelligence
algorithm to analyze images on a
standalone cloud-based application in
parallel to the ongoing standard of care
image interpretation. The user is
presented with notifications for cases
with suspected findings.
Notifications include non-diagnostic
preview images that are meant for
informational purposes only. The
device does not alter the original
medical image and is not intended to
be used as a diagnostic device.
The results of the device are intended
to be used in conjunction with other
patient information and based on
professional judgment, to assist with
triage/prioritization of medical images.
Notified clinicians are responsible for
viewing full images per the standard of
care.

No; device does not mark, highlight,
or direct users’ attention to a
specific location in the original
image.

Artificial intelligence algorithm with
database of images.


Ultrasonic Pulsed Doppler Imaging System (Primary)


Diagnostic Ultrasound Transducer 892.1570 90-ITX


P60 Series Digital Color
Doppler Ultrasound System

S90 Exp Series Digital Color
Doppler Ultrasound System

P20 Elite Series Digital Color
Doppler Ultrasound System


The data set were acquired with P60/S60 series Digital Color Doppler
Ultrasound System and probes in order to secure diversity of the data set:
Mix of data from retrospective data collection and prospective data collection
in clinical practice.

The ground truths drawn by five OB/GYN experts with more than 3 years’
experience were cross-checked by the two experts with more than 20 years’
experience and were re-checked by other four experts with more than 30
years’ experience.
Any images that do not meet the inclusion/exclusion criteria were excluded
from the set of images.

The test set used to test the standalone performance of S-FetusAI are
independent of the training data set and tuning data set, which have been
collected at three hospitals in China and meet the characteristics of
independence, representativeness, uniformity of distribution, etc., and strive
to include all possible scenarios in the clinic as far as possible.
S-Fetus-Recognition:
The number of test set is more than 200 images for each standard section;
total 3717 images for 14 standard sections.
S-Fetus-Measure:


The number of test set is more than 250 for each standard section; total 2929
image for 9 standard sections.

The accuracy of the S-Fetus-Recognition is not less than 85%.
The accuracy of the S-fetus-Measure is not less than 80%.

The accuracy of the S-Fetus-Recognition is 90.01%.
The accuracy of the S-fetus-Measure is 85%.


P60 Exp Digital Color Doppler Ultrasound System with S-FetusAI
& VC6-2 Probe

Trial group: Automatic recognition and measurement of the S-FetusAI
function of the Research Device;
Contrast group: Manual recognition and measurement of the investigators
(physicians with more than 3 years of specialized training in obstetric
ultrasound examination; total 7 investigators).
The section recognition results and precision of Sp of Trial group and
Contrast group were evaluated by the 2 Readers from the independent
evaluation group, which consists of three qualified professionals (senior
experts who had been involved in prenatal ultrasound examination).

Clinical
Performance
Evaluation &
Acceptance Criteria

1.Identification Precision of the 14 Standard Sections (Primary Evaluation
Indicator)
Non-inferiority design is used.
If the lower limit of the 95% CI for the difference of Identification Precision
is greater than -10%, then the Identification Precision of the Standard Section
of S-FetusAI function is considered non-inferior to manual recognition.
2. Measurement Error of Growth Parameters (Secondary Evaluation
Indicator)
1)The relative errors of measurement for the 11 Growth Parameters;
2)The precision of Sp (spinal cord cone end positioning).

According to the clinical trial results, we can deduce that the recognition
precision of the standard sections of S-FetusAI was not inferior to that of
the manual scan, S-FetusAI has a good consistency with the manual
measurement and the Sp (spinal cone end positioning) precision was
basically equal between the trial group and control group.


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

IEC60601-1-2: 2020-09(4.1 Edition) , Medical electrical equipment - Part
1-2: General requirements for basic safety and essential performance -
EMC

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

IEC TR 60601-4-2 Edition 1.0 2016-05, Medical electrical equipment -
Part 4-2: Guidance and interpretation - Electromagnetic immunity:
performance of medical electrical equipment and medical electrical
systems

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

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

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

ultrasound system and its applications comply with the following FDA-recognized standards.


Label Fluid Fetus Umbilical-cord Placenta Uterus

Acceptance rate for each label - qualitative results:


Label Fluid Head Body Limbs Umbilical-cord Placenta Uterus


The mean DSC for the accepted and rejected cases - quantitative results:


Label Fluid Head Body Umbilical-cord Limbs Placenta Uterus


Device Trade Name
DeepFoqus (DeepFoqus-Accelerate)

Medical image management and processing system

Automated Radiological Image Processing Software


Predicate Trade Name (Primary Predicate is listed first)

3.0 Legally Marketed Predicate Devices 21 CFR 807.92(a)(3)


Characteristics
Subject Device:
Predicate Device:

Characteristics
Hi-Imaging 4TAVR
3mensio Workstation (K153736)

21 CFR§892.2050 “Medical image management and
processing systems”

QIH (Automated Radiological Image Processing
Software)

LLZ (System, Image Processing,
Radiological)

Hi-D Imaging 4TAVR software is intended to
visualize, analyze, and report anatomical structures of
the full heart of the patient. Full heart comprises the
components of the heart and vessels, including aorta,
left ventricle, left atrium, myocardium, right ventricle,
and pulmonary artery. The intended use is to support
trained healthcare professionals (cardiologists,
radiologists, and clinical specialists) before the cardiac
operations, such as transcatheter aortic valve
replacement (TAVR), in reading and interpreting
medical DICOM images of the heart and vessels, and
by measuring clinically relevant pre-procedural
planning parameters (diameters, lengths, perimeters,
areas). The software itself is not intended to be a
decision-making tool by itself.

3mensio Workstation is a software solution
that is intended to provide Cardiologists,
Radiologists and Clinical Specialists
additional information to aid them in reading
and interpreting DICOM compliant medical
images of structures of the heart and vessels.
3mensio Structural Heart enables the user to:
(cid:120) Visualize and measure (diameters,
lengths, areas, volumes, angles)
structures of the heart and vessels
(cid:120) Quantify calcium (volume, density)
3mensio Vascular enables the user to:
(cid:120) Visualize and assess stenosis,
aneurisms and vascular structures
(cid:120) Measure the dimensions of vessels
(diameters, lengths, areas, volumes,
angles)

Hi-D Imaging 4TAVR is a software as a medical
device (SaMD) intended to support trained healthcare
professionals (cardiologists, radiologists, clinical
specialists) with the assessment of the cardiovascular
anatomy and visualization and measurement of
structures of the heart and vessels, including the aortic
valve, and with pre-operational planning and sizing for
transcatheter aortic valve replacement (TAVR)
procedures by providing:
- Automatic segmentation of the full heart and vessels
- 3D and 2D rendering of DICOM images
- Automatic centerline detection
- Automatic detection of the LVOT (left ventricular
outflow tract), aortic, sinus, sinotubular junction
(STJ), ascending aorta and descending aorta planes
- Automatic multiplanar reconstruction (MPR) based
on the centerline
- Measurement and visualization tools
- Manual measurement option for the user
- Automatic S-curve extraction
- Automatic report generation
Hi-D Imaging 4TAVR software is indicated for
patients with tricuspid aortic stenosis who are eligible
for TAVR operations and without any prior aortic
valve implants.

3mensio Workstation enables visualization
and measurement of structures of the heart
and vessels for:
• Pre-operational planning and sizing for
cardiovascular interventions and surgery
• Postoperative evaluation
• Support of clinical diagnosis by quantifying
dimensions in coronary arteries
• Support of clinical diagnosis by quantifying
calcifications (calcium scoring) in the
coronary arteries
To facilitate the above, the 3mensio
Workstation provides general functionality
such as:
• Segmentation of cardiovascular structures
• Automatic and manual center lumen line
detection
• Visualization and image reconstruction
techniques: 2D review, Volume Rendering,
MPR, Curved MPR, Stretched CMPR,
Slabbing, MIP, AIP, MinIP
• Measurement and annotation tools
• Reporting tools

Table 1: Summary of Technological Characteristics


CT data in DICOM format (vendor independent)

(cid:120) Importing (Single and batch imports)
(cid:120) Encryption
(cid:120) History of processed images

(cid:120) Exporting
(cid:120) Deleting
(cid:120) Anonymizing (no automatic deletion of
original patient data)
(cid:120) Search

(cid:120) Automatic Centerline extraction for the entire aortic
segment.
(cid:120) Automatic anatomical profiling of MPR planes
along the aortic centerline.

(cid:120) Realign orthogonal MPRs
(cid:120) Segmentation toolset:
- Automatic segmentation of the
ascending aorta
- Automatic centerline (center lumen
line)
- Manual centerline
- Centerline editing
(cid:120) Undo/redo operations
(cid:120) Volume sculpting

(cid:120) Automatic segmentation and anatomical
measurements of heart components
(cid:120) Automatic anatomical measurements for pre-
operational TAVR planning.
(cid:120) Automatic detection of TAVR anatomical
landmarks.
(cid:120) TAVR sizing (IFU based TAVR sizing chart).
(cid:120) C-Arm angulation calculation (S-curve).

X-Ray Image Assessments:
(cid:120) Linear (length and diameter), angular and
ROI measurements
(cid:120) Volume measurements
(cid:120) C-Arm angulation calculation
(cid:120) Text and arrow annotations
(cid:120) Calcium scoring for assessment of
calcium in the aortic root
(cid:120) Calcium scoring for assessment of
calcium in the coronary arteries
(cid:120) Segmentation and analysis of coronary
artery tree centerline
Intravascular ultrasound (IVUS) / optical
coherence tomography (OCT) Image
Assessments:
(cid:120) Orthogonal, oblique, double oblique,
curved, cross-curved, stretched MPR
rendering
(cid:120) MIP, AveIP, MinIP and color volume
slabs
(cid:120) MIP volume rendering
(cid:120) Color volume rendering
(cid:120) Grayscale volume rendering
(cid:120) 2D slice review and stack comparison
(cid:120) 4D cine
(cid:120) Interactive VOI clipping
(cid:120) Multi-tissue color and opacity control
(cid:120) Active presets/User-defined presets


Characteristics
Subject Device:
Predicate Device:

Hi-Imaging 4TAVR
3mensio Workstation (K153736)

(cid:120) 2D, 3D visuals with centerline
(cid:120) Downloadable 3D meshes of the heart
compartments
(cid:120) Planes of interest visualized in 3D
(cid:120) Summary
(cid:120) Report generation, downloadable in PDF format
(cid:120) IFU based TAVR sizing chart

(cid:120) Printout
(cid:120) Session state
(cid:120) PDF format
(cid:120) DICOM PDF report


Feature
Affiniti Series Diagnostic
Comparison

The Auto ElastQ software feature
is intended to assist the user in
making liver stiffness
measurements using 2D-SWE
through recommended particular
frames and ROI positions to the
user.
The system software will select up
to three frames that are assessed
to be the most stable and present
these to the user. The circle ROI
will be launched in the locations
that are identified to be
homogenous and temporally
stable.

The ElastQ feature (“manual
workflow”) is a tool for measuring
the stiffness of the target organ
(liver) which requires the user to
review a loop of elastography
images displayed on the system
and uses the trackball to cine to
the frame(s) that are visually
perceived to be the most stable.
The circle ROI is then launched in
the center of the shear wave
imaging box, and the user
manually positions the ROI within
the box in a location that appears
to be homogenous and
representative of the tissue in the
entire box.

Subject of this
submission
The difference between
the predicate and the
subject device is:
The proposed Auto
ElastQ feature
recommends particular
frames and ROI locations
that are assessed to be
most stable homogenous
to the user.
The predicate device
requires users to review
a loop of elastography
images and then use the
trackball to cine to the
frames that are visually
perceived to be the most
stable. Then the user
must manually positions
the ROI in a location that
appears to be most
stable and homogenous.

substantially equivalent to the predicate devices (K240850).


The Auto ElastQ workflow
analyzes the frames present in
the cineloop buffer. The system
software will automatically cine to
three frames that are assessed to
be the most stable and present
these to the user. The
measurement ROI will
automatically launch and be
placed on locations identified to
be homogeneous and temporally
stable.
Even though the Auto ElastQ
feature will suggest a particular
ROI placement, the user must
inspect the results and ensure
that it meets intended clinical use.
Otherwise, the user can make
adjustments to what the feature
proposes, by either selecting a
different frame or a different ROI
position.

In the current released software
(“manual workflow”), the user
reviews a loop of elastography
images displayed on the system
display and then uses the
trackball to cine to the frame(s)
that are visually perceived to be
the most stable. The circle ROI is
then launched in the center of the
shear wave imaging box, and the
user manually positions the ROI
within the box in a location that
appears to be homogeneous and
representative of the tissue in the
entire box.

Subject of this
submission
The proposed Auto
ElastQ will provide a
recommendation to the
user where the predicate
device requires the user
to navigate the system
manually.

Identical, no new
transducers or modes
are being introduced in
this submission.

The Auto ElastQ feature
calculates stiffness
measurements based on selected
frames and ROI positions
recommended to the user or
based on user adjustments to
what the feature recommended.

The ElastQ feature calculates
stiffness measurements based on
the manually selected frames and
ROI positions.

No new measurements
are being introduced in
this submission. The
subject of this
submission is to provide
workflow enhancements.

A retrospective data analysis
study was conducted to evaluate
the performance of the Auto
ElastQ software compared to
manual measurements performed
by expert readers.
The results demonstrated high
agreement of Auto ElastQ
algorithm-generated
measurements, based on
statistical analysis, with manual
elastography measurements.

N/A – the equivalent functionality
is performed manually by the
users to select the most
appropriate frames and ROI
positions.


Special 510(k) Premarket Notification
Page 1 of 3


Special 510(k) Premarket Notification
Page 2 of 3


Special 510(k) Premarket Notification
Page 3 of 3


Correspondent Contact Mr. Michael Pogose


Legally Marketed Predicate Devices 21 CFR 807.92(a)(3)


criteria, which is summarized below in Table 1.


Difference between devices
4.333 > 0
0.611 > 0


Table 3. Summary performance metrics from full sample of 308 patients, with 95% confidence intervals where appropriate.


Metrics White Black/African Hispanic/ Asian/Asian Unknown/

where appropriate. *CI could not be calculated due to lack of evidence (low N).


appropriate. MCR = Mayo Clinic Rochester, BMC = Boston Medical Center.


Metrics
SIEMENS
GE Medical
Philips
Canon/ Toshiba


Brainomix 360 e-CTA
Brainomix 360 e-CTA
iSchemaView Rapid

Brainomix 360 e-CTA is an
image processing software
package to be used by trained
professionals, including, but
not limited to physicians and
medical technicians. The
software runs on standard “off
the-shelf” hardware (physical
or virtualized) and can be used
to perform image viewing,
processing, and analysis of
images. Data and images are
acquired through DICOM
compliant imaging devices.
Brainomix 360 e-CTA provides
viewing and analysis
capabilities for imaging
datasets acquired with CTA (CT
Angiography).

Rapid is an image processing
software package to be used by
trained professionals, including
but not limited to physicians
(medical analysis and decision
making) and medical
technicians (administrative case
processing). 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.
Rapid is indicated for use in
Adults only. Rapid provides both
viewing and analysis capabilities
for functional and dynamic
imaging datasets acquired with
CT, CT Perfusion (CTP), CT
Angiography (CTA), C-arm CT

A table comparing the key features of the proposed and predicate devices is provided below.


Brainomix 360 e-CTA is not
intended for mobile diagnostic
use.
Brainomix 360 e-CTA vessel
density asymmetry ratio
applies only to the MCA region.

Perfusion and MRI including a
Diffusion Weighted MRI (DWI)
Module and a Dynamic Analysis
Module (dynamic contrast-
enhanced imaging data for MRI,
CT, and C-arm CT). Rapid C-arm
CT Perfusion can be used to
qualitatively assess cerebral
hemodynamics in the
angiography suite. The CT
analysis includes NCCT maps
showing areas of hypodense
and hyperdense tissue. 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. Rapid CT Perfusion and
Rapid MR Perfusion can be used
by physicians to aid in the
selection of acute stroke
patients (with known occlusion
of the intracranial internal
carotid artery or proximal
middle cerebral artery).
Instructions for the use of
contrast agents for this
indication can be found in
Appendix A of the User’s
Manual. Additional information
for safe and effective drug use is
available in the product-specific
iodinated CT and gadolinium-
based MR contrast drug
labeling. In addition to the
Rapid imaging criteria, patients
must meet the clinical
requirements for
thrombectomy, as assessed by
the physician, and have none of


the following contraindications
or exclusions:
• Bolus Quality: absent or
inadequate bolus.
• Patient Motion: excessive
motion leading to artifacts
that make the scan
technically inadequate.
• Presence of hemorrhage.
• C-Arm CTP is not to be
used in the Rapid
Thrombectomy indication
for patient selection
criteria, other modalities
should be consulted.
Cautions:
C-Arm CTP provides qualitative
data only, review other
modalities prior to diagnosis.
CBV and CBT are not absolute
and CBT, CBV, MTT and Tmax
are supported for qualitative
interpretation of the perfusion
maps only.

None – software only
application. The software
application does not deliver or
depend on energy delivered to
or from patients

Primary Users
Radiologist/Clinician
Same
Same

Design:
Supported
Modalities for
image processing
and visualization

CT [NCCT, CT, CTA, C-arm
CT(CBCT)] or MR (MR, MRA)

Maximum intensity projections
(MIPs) of the vascular system
in axial, coronal, left and right
hemisphere sagittal, and
posterior views

Maximum intensity projections
(MIPs) of the vascular system
in axial, coronal and left and
right hemisphere sagittal views

Maximum intensity projections
(MIPs) of the vascular system in
axial, coronal, left and right
hemisphere sagittal.

Vessel Density with visual
representation (3D-colored
overlay)

Vessel Density with visual
representation (2D-colored
overlay)


View, process and analyze
medical images. Performs
standard PACS functions with
respect to querying and listing

Standard off-the-shelf server or
virtual server

Acquires medical image data
from DICOM compliant imaging
devices and modalities

Arterial input function
(AIF)Venous output function
(VOF) – CTA

Brainomix 360 e-CTA is a
software package that provides
for the study of changes of
tissue in digital images
captured by CTA. Brainomix
360 e-CTA provides viewing
and quantification for CTA
images.

RAPID is a software package
that provides for the
visualization and study of
changes of tissue in digital
images captured by CT and MRI.
RAPID provides viewing and
quantification.

Materials
N/A – Software only device
Same
Same

Biocompatibility
N/A – Software only device
Same
Same

Sterility
N/A – Software only device
Same
Same

Electrical Safety
N/A – Software only device
Same
Same

Mechanical Safety
N/A – Software only device
Same
Same

Chemical Safety
N/A – Software only device
Same
Same

Thermal Safety
N/A – Software only device
Same
Same

Radiation Safety
N/A – Software only device
Same
Same


Physical
Software Package
Software Package

Characteristics
Operates on off-the-shelf hardware
Operates on off-the-shelf hardware

Storage
Storage not supported
Storage not supported

Decision support device used to assist in

in the assessment and
the assessment and characterization of

breast lesions and thyroid nodules using US

thyroid nodules using US image data.
image data.

Koios Decision Support (DS) is an artificial

intelligence (AI)/machine learning (ML)-

learning (ML)-based computer-aided
based computer-aided diagnosis (CADx)

diagnosis (CADx) software device
software device intended for use as an

intended for use as an adjunct to
adjunct to diagnostic ultrasound

diagnostic ultrasound examinations of
examinations of lesions or nodules

suspicious for breast or thyroid cancer.

confirm regions of interest (ROIs) within an

confirm regions of interest (ROIs)
image representing a single lesion or

nodule to be analyzed. The software then

automatically characterizes the selected

software then automatically
image data to generate an AI/ML-derived

characterizes the selected image data
cancer risk assessment and selects

to generate an AI/ML-derived cancer
applicable lexicon-based descriptors

risk assessment and selects applicable
designed to improve overall diagnostic

lexicon-based descriptors designed to
accuracy as well as reduce interpreting

improve overall diagnostic accuracy
physician variability.

Koios DS software may also be used as an

Koios DS software may also be used
images, including ultrasound and

as an image viewer of multi-modality
mammography. The software includes

tools that allow users to adjust, measure

and mammography. The software
and document images, and output into a


structured report.
Koios DS software is designed to assist
trained interpreting physicians in analyzing
the breast ultrasound images of adult (>=
22 years) female patients with soft tissue
breast lesions and/or thyroid ultrasounds
of all adult (>= 22 years) patients with
thyroid nodules suspicious for cancer.
When utilized by an interpreting physician
who has completed the prescribed
training, this device provides information
that may be useful in recommending
appropriate clinical management.

Target Population
(subset of above
for comparison
purposes)

Limitations for
Use
(subset of above
for comparison
purposes)

•Patient management decisions
•Patient management decisions should

not be made solely on the results of the

results of the Koios DS analysis.
Koios DS analysis.

•Koios DS software is not to be used for

the evaluation of normal tissue, on sites of

on sites of post-surgical excision, or
post-surgical excision, or images with

doppler, elastography, or other overlays

other overlays present in them.
present in them.

•Koios DS software is not intended for use


on portable handheld devices (e.g.
smartphones or tablets) or as a primary
diagnostic viewer of mammography
images.
•The software does not predict the
presence of the thyroid nodule margin
descriptor, extra-thyroidal extension. In
the event that this condition is present, the
user may select this category manually
from the margin descriptor list.

Modality Used for
Breast Ultrasound Data
Breast Ultrasound Data

Analysis
Thyroid Ultrasound Data
Thyroid Ultrasound Data

Input
Input
Medical images provided in a DICOM
Medical images provided in a DICOM

Breast
The software requires a user to select up to
two ROIs, from up to two orthogonal
views, that represent a single lesion to be
selected and processed.
Thyroid
Two ROIs that represent a single lesion to
be selected and processed are required for
analysis.
The first ROI is drawn on the transverse
view of the nodule. The second is drawn on
the longitudinal view.

up to two ROIs, from up to two
two ROIs, from up to two orthogonal

single lesion to be selected and
selected and processed.

lesion to be selected and processed
analysis.

transverse view of the nodule. The
the longitudinal view.

Koios defined categorical and continuous
outputs (confidence level indicator) that
align to BI-RADS, U1-U5, and auto-
classified shape and orientation.

Koios DS software automatically classifies

classifies thyroid nodules suspicious
thyroid nodules suspicious for cancer


based on image data generating an output
aligned to either the TI-RADS or ATA
classification guidelines. The system
automatically generates user-modifiable
nodule descriptors (Composition,
Echogenicity, Shape, Margin, Echogenic
Foci) and a direct, image-derived cancer
risk assessment that is translated into an
optional lexicon-specific modifier.


Breast Smart Click
Breast Smart Calipers
Thyroid Smart Calipers

Breast Smart Click
Breast Smart Calipers
Thyroid Smart Click
Thyroid Smart Calipers
Image Registration and
Matching

Technological Characteristics Comparison Table


6.Assessment of Categorical
Agreement – Shape
(prior results continue to apply)

7.Assessment of Categorical
Agreement – Orientation
(prior results continue to apply)


9.Data Set Drift Analysis -
Malignancy Risk Classifier AUC


Average Time for Study Preprocessing: 2.39 +/- 0.48
seconds
Average Time for Image Matching: 0.22 +/- 0.12
seconds
Note: The average study size which was processed in
this experiment was 53 +/- 11 images. The measured
durations scale roughly linearly with study size.

AUC = 0.946
Sensitivity = 0.975
Specificity = 0.637

4: End-to-End Thyroid Engine Performance

AUC = 0.801
Sensitivity = 0.670
Specificity = 0.603

Successful Match:
Count: 2018
Fraction: 0.995
No Match:
Count: 10
Fraction: 0.005
Incorrect Match:


6: Breast Image Matching DICE Coefficient

Successful Match:
Count: 1288
Fraction: 1.000
No Match:
Count: 0
Fraction: 0.000
Incorrect Match:
Count: 0
Fraction: 0.000
Incorrect Image:
Count: 0
Fraction: 0.000

8: Thyroid Image Matching DICE Coefficient


Years of
Experience –
Mammography
and/or Breast
Ultrasound

Academic
Institution
Affiliation
(Yes/No)


R1
Domestic Endocrinologist (End)
< 10 years

R2
Domestic Radiologist (Rad)
≥ 20 years


Change in average Sensitivity and
Specificity of Follow-up with Koios DS
(US readers, US data)

Change in average Sensitivity and
Specificity of Follow-up with Koios DS
(EU readers, EU data)

Change in average AUC with Koios DS
(EU Readers, EU Data)

Inter-Reader Variability measuring the
association of TI-RADS points assigned
with and without decision support
Difference (Relative Change %)

40.7% (all readers, all data)
37.4% (US readers, US data)
49.7% (EU Readers, EU Data)

-23.6% (all readers, all data)
-22.7% (US readers, US data)
-32.4% (EU Readers, EU Data)

Change in average AUC with Koios DS
descriptor classifiers only (without AI
Adapter) (parametric)

Change in average AUC with Koios DS
descriptor classifiers only (without AI
Adapter) (non-parametric)

Change in average sensitivity and
specificity of FNA with Koios DS
descriptor classifiers only (without AI
Adapter)


Change in average sensitivity and
specificity of Follow-up with Koios DS
descriptor classifiers only (without AI
Adapter) – excluding cases
recommended for FNA

Change in average sensitivity and
specificity of Follow-up with Koios DS
descriptor classifiers only (without AI
Adapter) – including cases
recommended for FNA


All Readers, All Data
US Readers, US Data
EU Readers, EU Data

Change in average Sensitivity/Specificity of FNA

TI-RADS categorization
w/AI Adapter + size
criteria

Change in average Sensitivity/Specificity of Follow-up

Summary of System Clinical Performance Using TI-RADS RSS


Distance over a non-straight trajectory in mm

Area in mm², perimeter, area derived diameter, perimeter
derived diameter, min diameter and max diameter all in mm

The HU (Hounsfield unit) of the underlying pixel


(Subject device)
(Primary Predicate)
(Secondary Predicate)

TruPlan Computed
Tomography (CT)
Imaging Software

Medical image
management and
processing system

Picture archiving and
communications system


TribusConnect is a cloud-
based DICOM viewer
designed for medical
professionals to securely
access, review,
manipulate, measure and
visualize DICOM images.
The software is intended
to be used by trained
healthcare professionals,
including but not limited
to radiologists,
physicians, nurses, and
technicians.

The RemotEye Viewer
software product is
intended to be used as a
functional, web based
medical image viewer to
download, review,
interpret, manipulate,
visualize and print
medical multi-modality
image data in DICOM
format also stored in
remote locations with
respect to the viewing
site. When interpreted by
a trained physician, the
medical images
displayed by RemotEye
Viewer can be used as an
element for diagnosis.
Typical users of
RemotEye Viewer are
trained professionals,
including but not limited
to radiologists,
physicians, nurses and
technicians.

TruPlan enables
visualization and
measurement of
structures of the heart
and vessels for:
- Pre-procedural
planning and sizing for
the left atrial appendage
closure (LAAC)
procedure
- Post-procedural
evaluation for the LAAC
procedure


TribusConnect is a
medical image
management and
processing device which
facilitates remote viewing
on different locations of
CT images stored in
DICOM format, allowing
healthcare professionals
to access, review,
manipulate, measure and
visualize
simulated patient data
from different sources.
TribusConnect is suitable
for, but not limited to, pre-
procedural planning of
cardiac interventions and
post procedural analysis
of cardiac interventions.
To facilitate the above,
TribusConnect uses CT
DICOM data to output
volume renderings, ML-
based heart
segmentation, a
simulated fluoroscopy
view, endo views, MIP and
MPR views.
Measurements can only
be performed on MPR
views.
TribusConnect is not
intended for diagnostic
use on mobile displays.
TribusConnect is not
intended to serve as the


primary archive for
medical imaging data.
TribusConnect is an
adjunct tool and is not
intended to replace a
physician’s own review on
the images.
The intended patient
population is comprised
of adult patients (22 years
of age and older)

No limitations in 510(k) or
IFU
Similar, The fact that a
portion of the population
is not within the scope of
TribusConnect does not
introduce new risks.

TruPlan’s intended
patient population is
comprised of adult
patients.
Same

Multi modality DICOM
compliant (Including CT)
Similar, more supported
modalities


• Study/series previewing
• Exporting
• Deleting
• Anonymizing
• Search
Same

Advanced distance, area,
angle and density
measurement tools
(including SUV for PET
images), plus several
graphical annotation
tools.
Measurement of CTR
(Cardio-Thoracic Ratio)
also supported.
Same

• Distance (length,
diameter,
perimeter)
• Area
• Angle
• Signal intensity
• Coordinates
Same

• 2D
• 3D
• 4D
• MPR
• Annotations
• Segmented 3D volume

• 2D
• 3D
• MPR / MIP / MinIP /AvgIP
• Annotations
Similar. No information
could be found whether
4D is supported, since 4D
as implemented in
TribusConnect and
TruPlan is a sequence of
3D images played in a
movie, this will not
introduce a new risk

• 2D
• 3D
• 4D
• MPR
• MIP
• Annotations
• Segmented 3D volume
Same

ML based segmentation
of all heart chambers,
aorta, pulmonary trunk
and vena cava

ML based segmentation
left atrium, left ventricle,
left atrial appendage
and the aorta. (Left


(Subject device)
(Primary Predicate)
(Secondary Predicate)

heart)
Manual sculpting
Similar, The same
underlying technique is
used the algorithm in
TribusConnect is trained
to identify more
structures, this does not
introduce new risks.
Absence of the sculpting
feature does not
introduce new risks.

Semi-Automated
Localization of LAA
appendage

Localization of LAA
appendage
Similar, since
TribusConnect does not
automatically suggest
measurements like
TruPlan, no new risks are
introduced.

DICOM presentation
states and Key note
images
Similar, the saved
sessions in
TribusConnect also
contain information
about the orientation of
the MPR planes and
volumes, this does not
introduce new risks

Analysis is saved in a
saved session.
Same

Cloud-based peer-to-
peer image sharing
functionalities.

Sessions are saved on a
central location and
available for other users.


Similar, work can be
shared via a saved
session, but no “live”
image sharing, this does
not introduce new risks

File system based upload
of DICOM files.

DICOM protocol and
import from disk
Same

Supports DICOM
connectivity and file-
system based upload of
image data.
Same

Laptop, on-premise
server
Similar, an on-premise
server is not necessarily
accessible via the
internet, the risks
introduced by the cloud
component are handled
in the cyber security risk
assessment and
deemed acceptable

Cloud based zero
footprint viewer, users
can access the viewer
through a standard web
browser on their
desktops, laptops, or
mobile devices.

RemotEye Viewer is
compatible with
Windows, Mac OS X and
Linux clients.
Similar, TribusConnect is
supported on more
devices, the possible
risks introduced by this
are evaluated as part of
the risk management
activities and deemed
acceptable

Windows/Apple based
desktop/laptop
application.
Similar, TribusConnect
is supported on more
devices and cloud
based, the possible risks
introduced by this are
evaluated as part of the
risk management
activities and deemed
acceptable


MHD (valsava manoeuver detection) and Anal sphincter:


independent reviewers, with
at least two being US
Certified sonographers, with
extensive clinical experience.

To ensure separation of
train and test data,
following steps were
undertaken:
Post model development,
we acquired consecutive
data from previously
unseen sites to test the
robustness of the algorithm
on new data distributions.
We also collected
consecutive data
(separately) from sites that
had contributed to the
train pool as well.

To ensure separation of train
and test data, following steps
were undertaken:
Post model development, we
acquired data from
previously unseen (in train
pool) systems as well as
new/unseen sites to test the
robustness of the algorithm
on new data distributions.

510(k) Premarket Notification Submission


AI Testing Summary for updated feature SonoAVC Follicle 2.0:


For various confounders the individual accuracy
numbers are as follows:
Probe wise distribution:
RIC5-9 – 94.6%
RIC10 – 93,6%
RIC6-12 – 90.6%
Geographical distribution:
Germany – 92.2%
India – 95.1%
Spain – 94.7%
U.K. – 95.5%

The test cohort consists of multiple sets – Set 1 is
acquired together with the train data set and
remaining sets (2-5) are acquired post model
development.
For set 1 – the size distribution of follicles (measured
as the largest 3D diameter) is detailed below:
3-5 mm: 31%
5-10 mm: 40%
10-15 mm: 21%
>15 mm: 8%
The above is in line with the clinical prevalence of the
follicle sizes.
The above, together with data from diverse probes and
different sites ensure sufficient diversity is captured in
terms of acquisition settings, image quality as well as
patient demographics.
The performance matrix for the various size ranges
for follicles is as follows.
Size Range Dice
(mm) Coefficient
3-5 0.937619
5-10 0.946289
10-15 0.962315
>15 0.93206


Data is provided by external clinical partners who de-
identified the data before sharing it with us. Original
data is collected in the form of 3D volumes in *.vol1 or
*.4dv2 data formats. This preserves the flexibility to re-
process data to our needs retrospectively during scan
conversion3.
Train Data Distribution:
Total Volumes: 249
- Distribution by Systems: Voluson E8 (20),
Voluson E10 (131), Voluson P8 (29), Voluson
S10 (48), Voluson S8 (21)
- Distribution by Probes: RIC5-9A-RS (98), RIC5-
9-D (149), RIC6-12-D (2)
- Distribution by Countries: India (62), Germany
(29), United Kingdom (29), Spain (103)
Test Data Distribution
Total Datasets: 138
Total follicle count across all volumes: 2708
- Distribution by Systems: Voluson E22 (63), E10
(32), Voluson E8 (20), Voluson P8 (8), Voluson
S10 (12), Voluson S8 (3)
- Distribution by Probes: RIC5-9 (84), RIC10 (37),
RIC6-12 (17)
- Distribution by Countries: Germany (54), India
(11), Spain (43), United Kingdom (7); USA (23)

To ensure correct and reliable “truthing” process of the
training data we followed a two-step approach.
In the 1st step, a detailed curation protocol (developed
by clinical experts) was shared with the curators, and
they were trained and instructed to follow the same.
As an automated quality control step, we confirm the
availability of all the required masks/markings in each
of the curated views. Missing masks or inconsistent
labels are reported back to the curators and the
datasets not used for training/development until all
masks are available and consistently labelled.
In addition, during and after the data curation process,
a arbitrator reviews all the datasets and curations from
each curator’s completed data pool for their clinical
accuracy. In case that any inconsistencies are detected,
an optimal curation strategy is discussed and
communicated back to the entire curation team.
,


For Evaluation pool:
The evaluation was based on interpretation of the AI
output by reviewing clinicians. The evaluation was
conducted by three independent reviewers, with at
least two being US Certified sonographers, with
extensive clinical experience.

To ensure separation of train and test data, following
steps were undertaken:
1. Post acquiring the data at the start of the
model development – the entire data pool
was split into train / validation and test
datasets ensuring that there is uniqueness in
the patient representation in each set – i.e. a
single patient is present only in one of the
three groups. Also, it was attempted that
there is a representation of as many
geographical sites as possible in both the test
and train pool. Beyond these two constraints,
the data was split randomly.
2. Post model development, we acquired
consecutive data from previously unseen (in
train pool) systems as well as new/unseen
probe to test the robustness of the algorithm
on new data distributions.


In order to demonstrate the generalization
performance of the algorithm, the quantitative
evaluation is performed for two subgroups: a data set
containing of a variety of ultrasound systems and data
formats against a data set containing the target
platform. For both subgroups the acceptance criteria
a re met.

- Systems: GEHC Voluson V730, E6, E8, E10,
Siemens S2000 and Hitachi Aloka
- Formats: Still images were obtained in DICOM
& JPEG format, cine loops in RAW data
format.
- Countries: UK, Austria, India and USA
- Total number of images: 2.2M
- Total number of cine loops: 3595

To ensure the quality of the curated data for
verification, the following strategy is employed:
employed
1. The images were curated (sorted and graded)
by a single sonographer
2. The images were sorted and graded by
ScanNav AutoCapture Second Trimester.
This process resulted in some images being
reclassified during sorting.
3. Where they differed from the ground truth,
the sorted images from step 2 were reviewed
by a 5-sonographer review panel, in order to
determine the sorting accuracy of the system.
The sorting process resulted in some images
being reclassified based upon the majority
view of the panel.
4. Where they differed from the ground truth,
the graded images from step 1 were reviewed
by a 5-sonographer review panel, in order to
determine the grading accuracy of the system


All training data is independent from the test data at a
patient level.


• Data used for both training and validation has
been collected across multiple geographical
sites using different systems to represent the
variations in target population.
• The verification for the SonoLyst 1st Trim IR&X
feature is based on computing confusion
matrices for the sorting (SonoLyst IR) and
grading (SonoLyst X) features
• The verification of the SonoLystLive 1st Trim
Trimester features is based on the average
agreement between a sonographer panel and
the output of the algorithm regarding Traffic
light quality (green/amber/none)
• The verification of the SonoBiometry CRL
feature is based on the acceptability rate for
the placement of CRL callipers
• The average success rate of SonoLyst 1st
Trimester IR, X and SonoBiometry CRL and
overall traffic light accuracy is 80% or higher
•

For SonoLyst 1st Trimester the following confounder is
used: the algorithmic performance is tested on two
sub data sets: 1) data acquired with transabdominal
probes 2) data acquired with transvaginal probes. By
choosing transabdominal vs transvaginal probes as
confounder for the data analysis, the robustness of the
algorithm against the influence of the abdominal wall,
the transducer geometry and frequency is evaluated.
For both subgroups the acceptance criteria are met.
This demonstrates the generalization performance of
t he algorithm.


- Systems: GE Voluson V730, P8, S6/S8, E6, E8,
E10, Expert 22, Philips Epiq 7G
- Formats: Still images were obtained in DICOM
& JPEG format, cine loops in RAW data
format.
- Countries: UK, Austria, India and USA
- For training 122,711 labelled source images
from 35,861 patients
- For testing the following number of images
were used:
SonoLyst 1st Trim IR: 5271
SonoLyst 1st Trim X: 2400
SonoLyst 1st Trim Live: 6000
SonoBiometry CRL: 110

To ensure the quality of the curated data for
verification, the following strategy is employed:
employed
1. The images were curated (sorted and graded)
by a single sonographer
2. The images were sorted and graded by
ScanNav AutoCapture First Trimester.
This process resulted in some images
being reclassified during sorting.
3. Where they differed from the ground truth,
the sorted images from step 2 were
reviewed by a 5-sonographer review
panel, in order to determine the sorting
accuracy of the system.
The sorting process resulted in some images
being reclassified based upon the majority
view of the panel.
4. Where they differed from the ground truth,
the graded images from step 1 were
reviewed by a 5-sonographer review
panel, in order to determine the grading
accuracy of the system.

All training data is independent from the test data at a
patient level.
A statistically significant subset of the test data is
independent from the training data at a site level, with
no test data collected at the site being used in training.


Ankle
Frontal, Lateral, Oblique
Fracture
Adults, Infants, Children, Adolescents

Foot
Frontal, Lateral, Oblique
Fracture
Adults, Infants, Children, Adolescents

Knee
Frontal, Lateral, Oblique
Fracture
Adults, Infants, Children, Adolescents

Wrist
Frontal, Lateral, Oblique
Fracture
Adults, Children, Adolescents

Hand/Finger
Frontal, Lateral, Oblique
Fracture
Adults, Infants, Children, Adolescents

Elbow
Frontal, Lateral, Oblique
Fracture
Adults, Children, Adolescents

Forearm
Frontal, Lateral
Fracture
Adults, Infants, Children, Adolescents

Adults, Neonates, Infants, Children,
Adolescents

the review of commonly acquired radiographs.


Shoulder
Frontal, Lateral
Fracture
Adults, Infants, Children, Adolescents

Adults, Children, Adolescents,
Neonates, Infants

Pelvis
Frontal
Fracture
Adults, Infants, Children, Adolescents

Hip
Frontal, Lateral
Fracture
Adults, Infants, Children, Adolescents


Device Name
TechCare Trauma
BoneView 1.1-US

Radiological computer assisted
detection/diagnosis software for
fracture Class II


TechCare Trauma is intended to
analyze 2D X ray radiographs using
machine learning techniques to aid in
the detection, localization, and
characterization of fractures and/or
elbow joint effusion during the
review of commonly acquired
radiographs.

BoneView is intended to aid in the
detection,
localization, and characterization of
fractures on
acquired medical images

Fracture, for Adults (greater than 21
years of age) and
Infants/Children/Adolescents
(between 28 days of age
and 21 years of age):
Ankle
Foot
Knee
Leg
Hand/Finger
Forearm
Arm
Shoulder
Clavicle
Pelvis
Hip
Fracture, for Adults (greater than 21
years of age) and
Children/Adolescents (between 2
years of age
and 21 years of age):
Wrist
Fracture, for Adults only (greater
than 21 years of age):
Femur
Thorax (includes ribs)
Fracture, for neonates (from 0 to 28
days of age):
Arm (includes Humerus)
Clavicle
Elbow Joint Effusion, for Adults
(greater than 21 years of age) and
Children/Adolescents (between 2
years of age
and 21 years of age):
Elbow (lateral)
Forearm (lateral)

Fracture :
Adults (greater than 21 years of age)
and
Children/Adolescents (between 2
years of age
and 21 years of age):
Ankle
Foot
Knee
Tibia/Fibula
Wrist
Hand
Elbow
Forearm
Humerus
Shoulder
Clavicle
Adults (greater than 21 years of age)
only:
Pelvis
Hip
Femur
Ribs
Thoracic Spine
Lumbosacral Spine


Similar with the
addition of Elbow
Joint Effusions for
TechCare Trauma

The intended users of TechCare
Trauma are clinicians with the
authority to diagnose fractures
and/or elbow joint effusions in
various settings including primary
care (e. g., family practice, internal
medicine), emergency medicine,
urgent care, and specialty care (e. g.
orthopedics), as well as radiologists
who review radiographs across
settings.

The intended users of BoneView are
clinicians with the authority to
diagnose fractures in various settings
including primary care (e. g., family
practice, internal medicine),
emergency medicine, urgent care, and
specialty care (e. g. orthopedics), as
well as radiologists who review
radiographs across settings.

For Fracture : Neonates and infants
(from birth to less than 2 years),
children and adolescents (aged 2 to
less than 22 years) and adults (aged
22 years and over).
For EJE: Children and adolescents
(aged 2 to less than 22 years) and
adults (aged 22 years and over).

Adults (greater than 21 years of age)
and Children/Adolescents (between 2
years of age and 21 years of age)

Similar with the
addition of
neonates and
infants (from
birth to less than
2 years) for
TechCare Trauma

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

PACS system
Image annotations made on copy of
original
image or image annotations toggled
on/off

Deployment on-premise or on cloud
and
connection to several computing
platforms and
X-ray imaging platforms such as X-ray
radiographic systems, or PACS


mage-level ROC-A
UC Summary for TechCa
re Trauma

Popul
ation (Nb of images)
Imag
e-leve
l R
OC-
AU
C [95%
CI]

mage-level ROC-A
UC Summary for fractur
e detectio
n

Category (Nb
of images Adu
lt ROC-AUC
[95% CI]
Ped
iatr
ic
ROC-AU
C [95%

Image-level ROC-AUC Summary for TechCare Trauma


Population (Nb of images)
Image-level ROC-AUC [95% CI]


Se (High Sp)
Sp (High Sp)
Se (High Se)
Sp (High Se)

Image-level Sensitivity and Specificity for Fracture detection - Adult population (N = 4109)


Image-level Sensitivity and Specificity for EJE detection - Adult population (N = 280)


Image-level Sensitivity and Specificity for Fracture detection – Pediatric population (N = 2872)


Image-level Sensitivity and Specificity for EJE detection – Pediatric population (N = 483)


informational purposes only) and
both exact and four-category
quantitative estimates of the
patient’s coronary artery calcium
burden in Agatston units.
The output of the subject device
is made available to the
physician on-demand as part of
his or her standard workflow.
The device generated calcium
score or score group can be
viewed in the patient report at
the discretion of the physician,
and the physician also has the
option of viewing the device-
generated calcium segmentation
in a diagnostic image viewer.
The subject device output in no
way replaces the original patient
report or the original chest CT
scan; both are still available to be
viewed and used at the discretion
of the physician.
The device is intended to provide
information to the physician to
provide assistance during review
of the patient’s case. Results of
the subject device are not
intended to be used on a stand-
alone basis and are solely
intended to aid and provide
information to the physician. In
all cases, further action taken on
a patient should only come at the
recommendation of the physician
after further reviewing the
patient’s results.

coronary arteries: the right
coronary artery (RCA), the left
main artery (LM), the left anterior
descending artery (LAD), and the
left circumflex artery (LCX). The
software calculates the Agatston
score as the sum for each artery
while also providing the total
across all coronary arteries. The
total Agatston score is calibrated
to 3.0mm slice thickness and is
used to derive a CAC category and
the arterial age. The provided
segmentations are for illustrative
purposes only and are not intended
for diagnostic use.
ClearRead CT CAC provides
adjunctive information and is not
intended to be used without
clinical expert review.

Routine, non-contrast, non-gated
chest CT Series

Non-contrast, non-gated, standard,
or low-dose chest CT scans


raditional 510(k
) Premarket Notification
Riverain
Technologies

Clinical
Coronary Artery Calcification
Coronary Artery Cal
cification

Intended
Medical Facility
Medical Facility

Measurement
Agatston Units
Agatston Units

Default
130 HU (Hounsfield Units)
130 HU (Hounsfield
Units)

Coronary
CAC detection category (based
CAC detection categ
ory and

Artery
on Agatston score), exact
arterial age based on
the measured

Calcification
Agatston score.
Agatston score.

Traditional 510(k) Premarket Notification Riverain Technologies


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

Yes, Coronary Calcium
Detection Category and exact
Agatston score 4 categories (for
detection category):
• 0
• 1-99
• 100-399
• 400 +

Yes, Coronary Calcium Detection
Category and measured Agatston
score 4 categories (for detection
category):
• 0
• 1-99
• 100-399
• 400 +


Predicate Device
Reference Device AI-
Subject Device

BriefCase-
Rad Companion
Aidoc BriefCase-

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

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

BriefCase-
Quantification is a
radiological image
management and
processing system
software indicated for
use in the analysis of
contrast-enhanced CT
exams that include the
aorta in adults or
transitional
adolescents aged 18
and older.
BriefCase-
Quantification of Aortic
Measurement (M-
Aorta) is intended to
assist hospital
networks
and appropriately
trained
medical specialists by
providing the user with
aortic diameter
measurements across
the aorta. BriefCase-
Quantification is
indicated to evaluate
normal and
aneurysmal aortas and
is not intended to
evaluate post-
operative aortas.


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

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

The device provides
the following
assessmnets:
● Aortic
measurements
at 10
anatomical
landmarks;
● Maximum
aortic diameter
of the
abdominal
aorta,
descending
aorta and
ascending
aorta.
The BriefCase-
Quantification results
are not intended to be
used on a stand-alone
basis for clinical
decision-making or
otherwise preclude
clinical assessment of
cases.
These measurements
are unofficial, are not
final, and are subject to
change after review by
a radiologist. For final
clinically approved
measurements, please
refer to the official
radiology report.
Clinicians are
responsible for viewing


Appropriately trained
medical specialists

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

Hospital networks and
appropriately trained
medical specialists

CT exams with
contrast that include
the abdominal aorta

contrast-enhanced CT
exams that include the
aorta

Artificial intelligence
algorithm with
database of images.

-BriefCase-
Quantification,
is hosted on a cloud
server, analyzes
applicable CT images
that are acquired on
CT scanner that are
forwarded to
BriefCase-
Quantification.

- The system remains
hosted in the teamplay
digital health platform
and remains driven by
the AI-Rad Companion
Engine.
- The edge
deployment the
processing of clinical

- BriefCase-
Quantification is
hosted on a cloud
server, analyzes
applicable CT images
that are forwarded to
BriefCase-
Quantification.
- The results of the
analysis are exported


Predicate Device
Reference Device AI-
Subject Device

BriefCase-
Rad Companion
Aidoc BriefCase-

- The results of the
analysis are exported
in DICOM format, and
are sent to a PACS
destination for review
by medical specialists,
to assist in the
measurement of the
abdominal aorta.

data and the
generation of
results are performed
within the customer
environment.
- This system remains
fully connected to the
cloud for monitoring
and maintenance of
the system from a
remote setup.


Table 3. Frequency Distribution of Gender


Table 4. Frequency Distribution of Manufacturer


Subject Device
Primary predicate Device
Reference Device

Device name
Neurophet AQUA V3.1
Neurophet AQUA v2.1
NeuroQuant® v2.2

Manufacturer
NEUROPHET, Inc.
NEUROPHET, Inc.
CorTechs Labs, Inc

Neurophet AQUA 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.

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.

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

•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

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

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

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

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

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


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

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

▪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
▪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
-Image artifact check
-Scan protocol
verification
•Results must be
reviewed by a trained
physician

•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


Parameter
CaRi-Plaque
Autoplaque 3.0 (K212758)

Review of coronary vessels in 2D
MPR, curved MPR, and
straightened view.

2D measurement tools of vessel
diameter and contour

3D Imaging
Review of structures in 3D
Same

N/A; this visualization method is
not necessary analyzing the
presence and extent of coronary
plaque and luminal stenosis

MPR with oblique slicing.
Variable slab thickness is not
necessary analyzing the
presence and extent of coronary
plaque and luminal stenosis

MPR with oblique slicing and
variable slab thickness

Diameter stenosis: Maximum diameter
stenosis, with respect to proximal and
distal references

LD-NCP volume: low-density
noncalcified plaque volume

No. This is calculated but not
reported as a device output.

NCP burden: noncalcified plaque
volume / analyzed vessel volume

LD-NCP burden: low-density
noncalcified plaque volume / analyzed
vessel volume

CP burden: calcified plaque
volume/analyzed vessel volume

Total plaque burden: total plaque
volume/analyzed vessel volume

Plaque composition NCP: noncalcified
plaque composition (NCP volume /
total plaque volume)

Plaque composition CP: calcified
plaque composition (CP volume / total
plaque volume)

Plaque composition LDNCP: low-
density noncalcified plaque

Table 1. Technology Comparison to Predicate


Parameter
CaRi-Plaque
Autoplaque 3.0 (K212758)

composition (LD-NCP volume / NCP
volume)

Yes, refers to proximal and distal
references

Remodeling index: ratio of maximum
vessel area / proximal and distal
references

Area stenosis: maximum area stenosis,
with respect to proximal and distal
references

Contrast density difference: maximum
difference in contrast density over
lesion with respect to proximal

No; not used as a common
reference standard

MLD: minimal luminal dimeter over
lesion

Vessel profile: area, maximum
diameter, minimum diameter
measured from selected vessel cross
section

Lumen profile: area, maximum
diameter, minimum diameter
measured from selected lumen cross
section

Yes- semi-automatic (threshold-
based segmentation with full
option to edit); output
demonstrated to meet pre-
determined acceptance criteria
in clinical performance study

Yes- semi-automatic (deep
learning based with full option to
edit)


Via Delle Nazioni, 9
37012 Bussolengo (VR) - Italy


Primary Predicate
510(k) Number
Manufacturer

OrthoNextTM Platform System
K202519
Orthofix S.r.l.


Reference Device
510(k) Number
Manufacturer

Orthofix TrueLok Hexapod System (TL-
HEX) V2.0


The subject OrthoNext™ Platform System is a web-based modular software system,
indicated for assisting healthcare professionals in planning of orthopedic surgery and
treatment both preoperatively and postoperatively, including deformity analysis and
correction with several Orthofix products.
The subject software system is intended for use by Healthcare Professionals (HCP),
with full awareness of the appropriate orthopedic procedures, in the operating theatre
only.
The subject software functions are intended to inform the HCP on orthopedic
procedure treatment planning when the Orthofix external or internal fixation systems
are used. These functions are evidence-based tools that support HCP when
considering treatment digital planning options for a patient. The software functions
do not treat a patient or determine a patient’s treatment.
The software enables the HCP to import radiological images, display 2D views (frontal
and lateral) of the radiological images, overlay the positioning of the template and
simulate the treatment plan option, and to generate parameters and/or
measurements to be verified or adjusted by the HCP based on their clinical judgment.

The OrthoNext™ Platform System is indicated for assisting healthcare professionals in
preoperative planning of orthopedic surgery and post-operative planning of
orthopedic treatment. The device allows for overlaying of Orthofix Product templates
on radiological images, and includes tools for performing measurements on the image
and for positioning the template. Clinical judgments and experience are required to
properly use the software.

Comparison of
Technological
Characteristics with the
Predicate Device

The following table provides a comparison of technological characteristics of the
subject and the primary predicate device. Any differences have been demonstrated to
not raise different questions of safety and effectiveness by virtue of objective
evidence.


Subject Device
OrthoNext™ Platform System

The OrthoNext™ Platform system is
indicated for assisting healthcare
professionals in preoperative planning
of orthopedic surgery and post-
operative planning of orthopedic
treatment. The device allows for
overlaying of Orthofix Product
templates on radiological images, and
includes tools for performing
measurements on the image and for
positioning the template. Clinical
judgments and experience are
required to properly use the software.

The OrthoNext™ Platform system is
indicated for assisting healthcare
professionals in preoperative
planning of orthopedic surgery. The
device allows for overlaying of
Orthofix Product templates on
radiological images, and includes
tools for performing measurements
on the image and for positioning the
template. Clinical judgments and
experience are required to properly
use the software.
The OrthoNext™ Platform system is
not to be used for mammography.

Assessment: The subject indications for use include “post-operative planning of orthopedic treatment”, a
feature already included in the scope of the reference device, without substantially changing the intended
use of the device, which remains intended for assisting healthcare professionals in orthopedic procedure
(surgery and treatment) planning. The difference in the type of treatment planning does not constitute a
new intended use. The statement “The OrthoNext™ Platform system is not to be used for mammography”
included in the original Indications for Use of the primary predicate device was not incorporated in the
Indications for Use of the subject device because it was specifically addressed in a dedicated section of the
Instructions for Use; as the primary predicate device, the subject device is not to be used for
mammography.
Equivalent - no significant new questions have been raised.

OrthoNext™ Platform System does
not directly interact with patients.
Software planning functionalities are
applicable to patients that need pre-
operative orthopedic surgery planning
or post-operative orthopedic
treatment planning.

OrthoNext™ Platform System does
not directly interact with patients.
Software planning functionalities are
applicable to patients that need pre-
operative orthopedic surgery
planning.

Assessment: in comparison to the predicate device, the subject patient population explicitly includes
patients needing postoperative orthopedic treatment planning, as in the reference device. The population
that undergoes postoperative treatment is the same (or a subgroup) of the population needing
preoperative planning, thus it can be considered already included in the patient population of the primary
predicate device.
Equivalent - no significant new questions have been raised.

Principle of operation:
• Importation medical images format
(x-ray images)
• Processing tools
• Measurements and parameters
analysis tools
• Surgical planning tools
• Enable software modules
(preoperative and postoperative) for
overlaying template for simulation.


Assessment: The subject device has the same principle of operation of the primary predicate. Equivalent -
no significant new questions have been raised.

Web-based: login is possible through
access to a website by username and
password licensed by the
manufacturer.
The device is not incorporated into IT-
Networks.

Web-based: login is possible through
access to a website by username and
password licensed by the
manufacturer.
The device is not incorporated into
IT-Networks.

Assessment: The configuration and mode of access of the subject device is the same as the predicate
device.
Equivalent - no significant new questions have been raised.

5.
Supported devices
PC/Mac/Tablet
PC/Mac

Assessment: the main supported platforms are PC/Mac, as the primary predicate device. The subject
device is also validated for use on a tablet: since the software is web-based, access is allowed via a login to
the same web portal, independently from the platform used.
Equivalent - no significant new questions have been raised.

Windows (Microsoft) - Minimum
Windows 10; macOS (Apple) -
Minimum macOS Big Sur (version 11);
iPad (Apple) - Minimum 15.8

Assessment: the supported operating systems are the same (Windows, macOS) in comparison to the
primary predicate. The subject device is also validated for use on a tablet, thus also a dedicated operating
system is included.
Equivalent - no significant new questions have been raised.

• Google Chrome v120 or higher
(Windows)
• Microsoft Edge v121 or higher
(Windows)
• Firefox v122 or higher (Windows)
• Safari 17.2 or higher (macOS)
• Mobile Safari 15.8 or higher (iPadOS)
- Web Graphics Library (WebGL)
enabled
NOTE: Web Graphics Library (WebGL)
availability and minimum screen
resolution are checked at login time in
order to provide access to the SW.

• Google Chrome Browser Version 83
or higher (Windows)
• Microsoft Edge Version 44 or
higher (Windows)
• Mozilla Firefox Version 77 or higher
(Windows)
• Safari Version 13 or higher (macOS)
- Web Graphics Library (WebGL)
enabled
NOTE: Web Graphics Library
(WebGL) availability and minimum
screen resolution are checked at
login time in order to provide access
to the SW.

Assessment: The supported browsers are the same in comparison with the primary predicate, with updated
versions. The subject device is also validated for use on a tablet, thus also a dedicated browser is included.
Equivalent - no significant new questions have been raised.


Computer System Requirements
Display Settings: Screen Resolution of
1280 x 768 pixels or higher.
Internet Connection:
Minimum required internet
connectivity speed of 512 kbps
Recommended internet connectivity
speed of 3 mbps or higher

Assessment: The subject device system requirements are identical to the primary predicate device.
Equivalent - no significant new questions have been raised.

Can receive digital images in .png or
.jpg format

Assessment: Subject device image input management is identical to the primary predicate device.
Equivalent - no significant new questions have been raised.


N° of Samples
Mean value [0-255]
Median Vaue [0-255]

intensities produced the following results (0 = black; 255 = white).


Candidate Model – Threshold Value = 0.96


Intersection
Center MAE
Center MAPE
Radius MAE
Radius MAPE


Software usability activities have been conducted at the same time, asking surgeons
to perform some specified tasks, answer to some questions in written form, navigate
through the software and inviting them to describe ease of use or concerns regarding
tasks they are invited to perform. Feedback has been collected in written form
together with the answers rating and was analyzed in the Summative Usability Report.
Given the overall analysis, Orthofix concluded that the design verification and
validation results are acceptable, and that the usability test gave objective evidence
that the device usability requirements are met as far as safety of the user interface of
the subject OrthoNext™ Platform System is concerned.

Based upon: intended use, conditions of use, patient population, basic software
design, operating principle, and non-clinical performance data, the subject
OrthoNext™ Platform System has been shown to be substantially equivalent to the
legally marketed primary predicate device (K202519).


Digital Imaging and Communications in
Medicine (DICOM) Set

62304 Edition 1.1 2015-06
CONSOLIDATED VERSION

Medical Device Software –Software Life
Cycle Processes

Medical devices – Application of risk
management to medical devices

62366-1 Edition 1.1 2020-06
CONSOLIDATED VERSION

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

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

20417 First edition 2021-04
Corrected version 2021-12

Medical devices – Information to be
supplied by the manufacturer

FDA Recognized Consensus standards listed below:


Radiological Computer Aided
Triage And Notification Software

Regulation Number
21 CFR 892.2080
21 CFR 892.2080
Same

Classification Panel
Radiology
Radiology
Same

uAI Easy Triage ICH is a
radiological computer-assisted
triage and notification software
device indicated for analysis of
non-enhanced head CT images.
The device is intended to assist
hospital networks and trained
radiologists in workflow triage by

Cina is a radiological computer
aided triage and notification
software indicated for use in the
analysis of (1) non-enhanced head
CT images and (2) CT
angiography of the head.
The device is intended to assist

Compared to predicate device, the
proposed device provides one
applications for ICH while the
predicate device CINA (K221716)
provides two applications for ICH
and LVO.
uAI Easy Triage ICH and the
predicate device CINA(K221716)
have the same intended use


flagging and prioritizing studies
with suspected positive findings of
Intracranial Hemorrhage (ICH).

hospital networks and trained
radiologists in workflow triage by
flagging and communicating
suspected positive findings of (1)
head CT images for Intracranial
Hemorrhage (ICH) and (2) head
CT angiography for large vessel
occlusion (LVO) of the anterior
circulation (distal ICA, MCA-M1
or proximal MCA-M2).
Cina uses an artificial intelligence
algorithm to analyze images and
highlight cases with detected (1)
ICH or (2) LVO on a standalone
Web application in parallel to the
ongoing standard of care image
interpretation. The user is
presented with notifications for
cases with suspected ICH or LVO

and\indications for use in terms of
finding suspected intracranial
hemorrhage in non-enhanced head
CT, flagging suspected cases, and
indicating the case to the attention of
the clinician.


findings.
Notifications include compressed
preview images that are meant for
informational purposes only, and
are not intended for diagnostic use
beyond notification. The device
does not alter the original medical
image, and it is not intended to be
used as a diagnostic device.
The results of Cina are intended to
be used in conjunction with other
patient information and based on
professional judgement to assist
with triage/prioritization of
medical images. Notified
clinicians are ultimately
responsible for reviewing full
images per the standard of care.


User population
Radiologist
Radiologist
Same

Anatomical region of interest
Head
Head
Same

Non-contrast CT scan of the head
or neck and CT angiogram images
of the brain

Compared to predicate device, the
proposed device supports non-
contrast head CT data for ICH
application while the predicate device
support non-contrast head CT data for
ICH application or neck and brain CT
angiogram data for LVO application.
The difference between the proposed
device and the predicate device will
not impact the safety and
effectiveness of the subject device.

DICOM information about the
patient, study and current image

No; device does not mark,
highlight, or direct users’ attention
to a specific location in the


Artificial intelligence algorithm
with database of images

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


- ICH image processing
applications
- uAI Easy Triage Platform,
including Alert Icon, Patient
Management, and Image Viewer.

- LVO and ICH image processing
applications
- Cina Platform (worklist and
Image Viewer)

Compared to predicate device, the
proposed device have same software
structure and add an alert icon
module to pop-up the suspected
positive findings of ICH. The
difference between the proposed
device and the predicate device will
not impact the safety and
effectiveness of the subject device.


ICH Subtypes
Intraparenchymal Hemorrhage (IPH)
30%

Table 2 Clinical testing data subgroup information


Device provides
estimates of bone
mineral density,
T- scores and Z-
scores

Similar, subject
device provides a
subset of predicate
device outputs i.e.
only T- score group.

User
Healthcare provider
Healthcare Provider
Same


Proposed Device:
Predicate Device:
Summary

Retrospective
measurements
from CT scans

CT scan images can be
selected and inputted to
the software.

Software
automatically
measures and
averages Hounsfield
units in the regions of
interest of spinal
bones.

Software automatically
measures and averages
Hounsfield units in the
trabecular region of the
spinal bones.

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

User Healthcare provider Healthcare Provider Same


Digital Imaging and Communications in Medicine
(DICOM) Set

62304
Edition 1.1
2015-06
CONSOLIDA
TED
VERSION

Medical Device Software –Software Life Cycle
Processes

Medical devices – Application of risk management to
medical devices


62366-1
Edition 1.1
2020-06
CONSOLIDA
TED
VERSION

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

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

20417 First
edition
2021-04
Corrected
version
2021-12

Medical devices – Information to be supplied by the
manufacturer


Comparison of
Technological
Characteristics

Trade Name:
EzraFlash
EzraFlash
SubtleMR

Ezra Flash is an image pro-
cessing software used for im-
age enhancement of MR im-
ages. It can be used to
reduce image noise in im-
ages acquired as part of non-
contrast MRI exams on 1.5-
Tesla and 3-Tesla Siemens
and GE scanners for patients
> 18 years of age:
• Sagittal T1, Axial
T2 and Axial Flair
sequences within the
head region
• Axial T2, Coronal T2
within the Abdomen
region
• Sagittal T2, Axial T2,
Coronal T2 within the
Pelvis region

Ezra Flash is an image pro-
cessing software used for im-
age enhancement of MR im-
ages. It can be used to
reduce image noise in im-
ages acquired as part of non-
contrast MRI exams on 3-
Tesla Siemens and GE scan-
ners for Sagittal T1, Axial
T2 and Axial Flair sequences
withintheheadregionforpa-
tients > 18 years of age.

SubtleMR is an image pro-
cessing software that can be
used for image enhancement
in MRI images. It can
be used to reduce image
noise for head, spine, neck,
abdomen, pelvis, prostate,
breast and musculoskeletal
MRI, or increase image
sharpness for head MRI.

Software package that op-
erates on off-the-shelf hard-
ware.