Primary Predicate
Predicate
Predicate
Predicate
Predicate
Predicate

Manufactured by Circle
Manufactured by Circle
Manufactured by Circle
Manufactured by Circle
Manufactured by Circle
Manufactured by Circle

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. cmr42 is a software
application that can be used as a
stand-alone product or in a
networked environment.
The target population for the cmr42
is not restricted, however the image
acquisition by a cardiac magnetic
resonance scanner may limit the
use of the device for certain sectors
of the general public.
cmr42 shall not be used to view or
analyze images of any part of the
body except the cardiac magnetic
resonance images acquired from a
cardiovascular magnetic resonance
scanner.

information as part of a
comprehensive diagnostic decision-
making process. cvi42 is a software
application that can be used as a
stand-alone product or in a networked
environment.
The target population for the cvi42 is
not restricted.

It shall be used by qualified medical
quantitative measurements of calcified
comprehensive diagnostic decision-
diagnostic information as part of a

professionals, experienced in
plaques in the coronary arteries
making process. cvi42 is a software
comprehensive diagnostic decision-

examining and evaluating
(calcium scoring), specifically
application that can be used as a
making process.

cardiovascular MR images, for the
Agatston and volume and mass
stand-alone product or in a networked

purpose of obtaining diagnostic
calcium scores
environment.

comprehensive diagnostic decision-
It shall be used by qualified medical
The target population for the cvi42 is

making process. cmr42 is a software
professionals, experienced in
not restricted.

application that can be used as a
examining and evaluating

stand-alone product or in a
cardiovascular CT images, for the

networked environment.
purpose of obtaining diagnostic

The target population for the cmr42
comprehensive diagnostic decision-

is not restricted, however the image
making process. ct42 is a software

acquisition by a cardiac magnetic
application that can be used as a

resonance scanner may limit the
stand-alone product or in a networked

use of the device for certain sectors
environment.

The target population for the ct42 is not

cmr42 shall not be used to view or
restricted, however the image

analyze images of any part of the
acquisition by a cardiac CT scanner

body except the cardiac magnetic
may limit the use of the device for

resonance images acquired from a
certain sectors of the general public.

scanner.
ct42 shall not be used to view or


Primary Predicate
cmr42 (K082628)
Manufactured by Circle

Predicate
ct42 (K111373)
Manufactured by Circle

Predicate
cvi42 (K141480)
Manufactured by Circle

Predicate
cvi42 Auto (K213998)
Manufactured by Circle

Predicate
Strain (K232661)
Manufactured by Circle

Predicate
CT Function (K241038)
Manufactured by Circle

Device Classification
QIH, LLZ
LLZ
LLZ
LLZ
QIH, LLZ
LLZ
QIH, LLZ

Picture Archiving and
Communications System

Medical image management
and processing system

and processing system
Communications System

Imaging Modalities
MR and CT
MR
CT
MR and CT
MR and CT
MR
CT

DICOM Compliant
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Import and display MR/CT
Yes
Yes
Yes (MR only)
Yes (CT only)
Yes
Yes
Yes (MR only)
Yes (CT only)

Post process CMR/CCT images
Yes
Yes (MR only)
Yes (CT only)
Yes
Yes
Yes (MR only)
Yes (CT only)

Images can be displayed by
Yes
Yes
Yes
Yes

Multiplanar Reformat (MPR)
Yes
No
Yes
Yes
Yes
No
Yes

Navigation Tools
Navigation Tools
Panning,
Panning,

Series/slices and phases
Series/slices and phases

Distance
Perimeter
Area
Signal Intensity
Volume
Mass
Coordinates

Distance
Perimeter
Area
Signal Intensity
Volume
Coordinates
Agatston Score

Distance
Perimeter
Area
Signal Intensity
Volume
Coordinates
Angle
Stenosis

Distance
Perimeter
Area
Signal Intensity
Volume
Agatston Score
Stenosis
End Diastolic Volume (EDV)
End Systolic Volume (ESV)
Stroke Volume (SV)
Ejection Fraction (EF)
Cardiac Output (CO)
End Diastolic Mass (EDV)
End Systolic Mass (ESV)
Body Surface Area (BSA)

Displacement
Velocity
Strain
Strain Rate
Time to Peak
Torsion

End Diastolic Volume (EDV)
End Systolic Volume (ESV)
Stroke Volume (SV)
Ejection Fraction (EF)
Cardiac Output (CO)
Cardiac Index (CI)
End Diastolic Mass (EDM)
End Systolic Mass (ESM)
Body Surface Area (BSA)

Table 3. Regulatory and technological features comparison.


Primary Predicate
cmr42 (K082628)
Manufactured by Circle

Predicate
ct42 (K111373)
Manufactured by Circle

Predicate
cvi42 (K141480)
Manufactured by Circle

Predicate
cvi42 Auto (K213998)
Manufactured by Circle

Predicate
Strain (K232661)
Manufactured by Circle

Predicate
CT Function (K241038)
Manufactured by Circle

Quantitative assessment of
cardiac function

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

Manual segmentation of four
heart chambers in long and
short-axis views

Manual segmentation, and
semi-automatic segmentation
of four heart chambers in short-
axis views

Manual segmentation, and
semi-automatic segmentation
of four heart chambers in long
and short-axis views

Manual segmentation, 2D
functional analysis of
myocardial deformation

Semi-automatic determination
of epicardial and endocardial
contours, via segmentation of
the LV cavity, LV myocardium,
and RV cavity and the ED and
ES phases using Machine
Learning Techniques

Assessment of cardiac blood flow
Quantification of blood flow
Quantification of blood flow

using MR velocity encoded
using MR velocity encoded

Centerline placement in coronary
vessels

Manual and semi-automatic
using Machine Learning
technique

Workstation operating system
masOS,
macOS,
macOS,
macOS,
macOS,
macOS,
macOS,

Microsoft Windows
Microsoft Windows
Microsoft Windows
Microsoft Windows
Microsoft Windows
Microsoft Windows
Microsoft Windows


5000 Compact Series Ultrasound Systems
Auto Measure

Diagnostic Ultrasound System and Transducers

Classification Description 21 CFR § Product Code
Primary
System, imaging, pulsed doppler,
892.1550 IYN
ultrasonic
Secondary
System, imaging, pulsed echo,
892.1560 IYO
ultrasonic
Transducer, ultrasonic, diagnostic 892.1570 ITX
Automated Radiological Image
892.2050 QIH
Processing Software

K222648 5000 Compact Series Diagnostic Ultrasound Systems

K211597 Philips Affiniti Series Diagnostic Ultrasound System VM 9.0


Classification Description
21 CFR §
Product Code

System, imaging, pulsed doppler,
ultrasonic

System, imaging, pulsed echo,
ultrasonic

Transducer, ultrasonic, diagnostic
892.1570
ITX

Automated Radiological Image
Processing Software

Common Name Diagnostic Ultrasound System and Transducers


5000 Compact Series
5000 Compact Series
Affiniti Diagnostic Ultrasound

Ultrasound Systems with Auto
Ultrasound Systems
System VM9.0

Feature
Measure Version 2
(K 222648 )
(K211597)
Comparison

Primary predicate device
Reference Device

Abdominal,
Cardiac Adult,
Cardiac Pediatric,
Carotid,
Cerebral Vascular,
Cephalic (Adult),
Cephalic (Neonatal),
Fetal Echo,
Fetal/Obstetric,
Gynecological,
Intraoperative (Vascular),
Lung,
Musculoskeletal (Conventional),
Musculoskeletal (Superficial),
Ophthalmic,
Pediatric,
Peripheral Vessel,
Small Parts
Transesophageal (Cardiac),
Transrectal,
Transvaginal, and
Urology.

Abdominal,
Cardiac Adult,
Cardiac Pediatric,
Carotid,
Cerebral Vascular,
Cephalic (Adult),
Cephalic (Neonatal),
Fetal Echo,
Fetal/Obstetric,
Gynecological,
Intraoperative (Vascular),
Lung,
Musculoskeletal (Conventional),
Musculoskeletal (Superficial),
Ophthalmic,
Pediatric,
Peripheral Vessel,
Small Organ (Breast, Thyroid,
Testicle),
Transesophageal (Cardiac),
Transrectal,
Transvaginal, and
Urology.

Abdominal,
Cardiac Adult,
Cardiac Other (Fetal),
Cardiac Pediatric,
Cerebral Vascular,
Cephalic (Adult),
Cephalic (Neonatal),
Fetal/Obstetric,
Gynecological,
Intraoperative (Vascular),
Intraoperative (Cardiac),
Musculoskeletal (Conventional),
Musculoskeletal (Superficial),
Other:
Urology,
Pediatric,
Peripheral Vessel,
Small Organ (Breast, Thyroid,
Testicle),
Transesophageal (Cardiac),
Transrectal,
Transvaginal,
Lung

Identical to Predicate
Small Parts and Small Organs are
same. To align with terminology in
Compact 5000 Systema and User
Manual,
Updated to Small Parts

Trained healthcare professionals
Intended for sonographers,

devices are substantially equivalent to the predicate devices K222648 & Reference Device K211597


physicians, and biomedical
engineers who operate and
maintain your product.
Trained healthcare Professional
Before use of the system and
user information, the user must
be familiar with ultrasound
techniques. Sonography training
and clinical procedures are not
included in the User Manual or
with the 5000 Compact Series
Ultrasound Systems.

physicians, and biomedical
engineers who operate and
maintain your product.
Trained healthcare Professional
Before use of the system and user
information, the user must be
familiar with ultrasound techniques.
Sonography training and clinical
procedures are not included in the
User Manual or with the Affiniti
Series Diagnostic Ultrasound
System.

Clinics, hospitals, and clinical
point-of-care for diagnosis of
patients.

Clinics, hospitals, and clinical point-
of-care for diagnosis of patients.

Class II
Class II
Class II
Identical to Predicate

Primary
21 CFR 892.1550
21 CFR 892.1550
21 CFR 892.1550
Identical to Predicate


System, imaging, pulsed echo,
ultrasonic
Transducer, ultrasonic,
diagnostic
Automated Radiological Image
Processing Software

Difference No.1
The detectors for a subset of
measurements available with
the Auto Measure feature have
undergone additional training
since the original release of
the feature on Affiniti Series
Ultrasound SystemVM9.0.
Architecture of all detectors,
training procedure and all
acceptance criteria for the
additional training data are
identical to Auto Measure
Version 1


5000 Compact Series
5000 Compact Series
Affiniti Diagnostic Ultrasound

Ultrasound Systems with Auto
Ultrasound Systems
System VM9.0

Feature
Measure Version 2
(K 222648 )
(K211597)
Comparison

Primary predicate device
Reference Device

Limited (≤ 24 hours)
Limited (≤ 24 hours)
Limited (≤ 24 hours)
Identical to Predicate

Auto Measure is an optional
software feature on the 5000
Compact Series
Ultrasound System that
provides the end user with
semiautomated adult
echocardiography 2D and
Doppler measurements through
an AI-algorithm, training via
machine-learning techniques. It is
intended to be used with an Adult
Cardiology Transthoracic
transducer and acquisitions that
include an ECG
These measurements are
routinely collected during a
transthoracic ECG, per The
American Society of Echo
cardiography (ASE)
recommendations
2D modes include the following:
IVSd
LVIDd
LVPWd
LVIDs
Asc Ao Diam
LVOT Diam
Ao Sinus Diam

The healthcare professional
performs 2D and Doppler
measurements during a
transthoracic echocardiogram
by Manually positioning the
calipers on the ultrasound’s
system waveform or image.
2D modes include the following:
IVSd
LVIDd
LVPWd
LVIDs
Asc Ao Diam
LVOT Diam

Auto Measure is an optional
software feature on the Affiniti
Series
Diagnostic Ultrasound System that
provides the end user with
semiautomated adult
echocardiography 2D and Doppler
measurements through an AI-
algorithm, training via machine-
learning techniques. It is intended
to be used with an Adult Cardiology
Transthoracic transducer and
acquisitions that include an ECG.
These measurements are routinely
collected during a transthoracic
ECG, per The American Society of
Echo
cardiography (ASE)
recommendations
2D modes include the following:
IVSd
LVIDd

Difference No.2
Auto Measure is being included in
the subject submission with 5000
Compact Series Ultrasound
Systems


LVPWd
LVIDs
Asc Ao Diam
LVOT Diam
Ao Sinus Diam
Ao STJ Diam
RV Base, RV Mid, RV Length
TV Annulus
Doppler modes include the
following:
MV Peak E Vel, MV Peak A Vel
MV Inflow (MV Dec Time, MV
Peak E Vel, MV Peak A Vel)
LVOT VTI, LVOT Vmax
AV VTI, AV Vmax
PV VTI, PV Vmax
TR Vmax
Lat E’ Vel, Lat A’ Vel
Lat Vel (Lat E’ Vel, Lat A’ Vel)
Med E’ Vel, Med A’ Vel
Med Vel (Med E’ Vel, Med A’ Vel)
RV S Vel

Compatible
S5-1 and S4-2
S5-1 and S4-2
S5-1 and S4-2 ,x5-1, x5-1c
Identical to Predicate


User selects an adult
echocardiography 2D or Doppler
measurement to perform then the
caliper positions are initialized
based on the output of the AI
detection algorithm.
The user can edit, accept, or
reject the measurements.
Alternately The healthcare
professional performs 2D and
Doppler measurements during a
transthoracic echocardiogram by
Manually positioning the calipers
on the ultrasound’s system
waveform or image.

T he healthcare professional
p erforms 2D and Doppler
m easurements during a
t ransthoracic echocardiogram by
M anually positioning the calipers
on the ultrasound’s system
w aveform or image.


Parameter
Detector
N
Z score (A.
Measured Limits of Agreement (LoA)
Checked Limits

(Sample
Carkeet)
(detector prediction vs manual ground
of Agreement

Detector performance results of Auto Measure detectors


• Asian: 30.2 %
• Black: 26.4 %
• White: 36.2
• Other: 7.3 %

studies. The available data per detector are listed in column 3 above. Demographic distribution for all validation studies is as follows:


Radiological acquisition and/or optimization
guidance system

Image Acquisition And/Or Optimization
Guided By Artificial Intelligence

Secondary Contact Email bertrand.moal@deski.ai


Image Acquisition and/Or
Optimization Guided by
Artificial Intelligence

The Caption Guidance
software is intended to
assist medical
professionals in the

The HeartFocus software
is intended to assist
healthcare professionals in
the acquisition of cardiac


acquisition of cardiac
ultrasound images. Caption
Guidance software is an
accessory to compatible
general purpose diagnostic
ultrasound systems.

ultrasound images. The
HeartFocus software is an
accessory to compatible
general purpose
diagnostic ultrasound
systems.

The Caption Guidance
software is indicated for
use in two-dimensional
transthoracic
echocardiography
(2D-TTE) for adult patients,
specifically in the
acquisition of the following
standard views:
Parasternal Long-Axis
(PLAX), Parasternal
Short-Axis at the Aortic
Valve (PSAX-AV),
Parasternal Short-Axis at
the Mitral Valve
(PSAX-MV), Parasternal
Short-Axis at the Papillary
Muscle (PSAX-PM), Apical
4-Chamber (AP4), Apical
5-Chamber (AP5), Apical
2-Chamber (AP2), Apical
3-Chamber (AP3),
Subcostal 4-Chamber
(SubC4), and Subcostal
Inferior Vena Cava
(SC-IVC).

The HeartFocus software
is indicated for use in
two-dimensional
transthoracic
echocardiography
(2DTTE) for adult patients,
specifically in the
acquisition of the following
standard views:
Parasternal Long-Axis
(PLAX), Parasternal
Short-Axis at the Aortic
Valve
(PSAX-AV), Parasternal
Short-Axis at the Mitral
Valve (PSAX-MV),
Parasternal Short-Axis at
the
Papillary Muscles
(PSAX-PM), Apical
4-Chamber (A4C), Apical
5-Chamber (A5C), Apical
2-
Chamber (A2C), Apical
3-Chamber (A3C),
Subcostal 4-Chamber
(SC-4C), and Subcostal
Inferior
Vena Cava (SC-IVC).

Same
*A4C, A5C, A2C,
A3C, SC-4C are
the American
Society of
Echocardiography
Acronyms:
Guidelines for
Performing a
Comprehensive
Transthoracic
Echocardiographic
Examination in
Adults:
Recommendations
from the American
Society of
Echocardiography
(asecho.org)

Medical professionals
(including expert
sonographers)

The uSmart 2300t Plus
ultrasound system with the
2300t-compatible Terason
4v2A linear phased array
probe

Clarius Scanner PAHD3
(wireless), PAHD, PAL,
and Clarius Ultrasound
Scanner Software

Substantially
Equivalent: Both
the predicate
devices and the
proposed device
function as a

DESKi HeartFocus 510(k) Summary Apr 4, 2025


direct the user to a
probe position that
will enable
acquisition of a
diagnostic quality
clip. Hence, this
difference does not
raise new
questions of safety
and/or
effectiveness.

Quality Meter: real-time
feedback from the Quality
Meter advises the user on
the expected diagnostic
quality of the resulting clip,
such that the user can
make decisions to further
optimize the quality, for
example by following the
prescriptive guidance
feature

HeartFocus algorithm
provides analysis of the
diagnostic-quality of the
image and indicates to the
user when the current
frame is diagnostic-quality
to hold the position for a
recording
Further, a score on the
expected diagnostic
quality of the Auto-Record
and Best-Effort-Record
clips is indicated such that
the user can
make decisions to further
optimize the quality

Manual recording if
prompted by the user

Automatic Capture
of Clips and
Predicted
Diagnostic Quality

Auto-Capture: The Caption
Guidance Auto-Capture
feature triggers an
automatic capture of a clip
when the quality is
predicted to be diagnostic,
emulating the way in which
a sonographer knows
when an image is
of sufficient quality to be
diagnostic and records it.

Auto-Record: automates
the capture of
diagnostic-quality
recordings, emulating how
a sonographer
knows when an image is
of sufficient quality to be
diagnostic and records it.

Retrospectively
Recording of
Highest Quality
Clip

Save Best Clip: This
feature continually
assesses clip quality while
the user is scanning and, if
the user is not able to
obtain a clip sufficient for

Best-Effort-Record: this
feature stores the clip that
has been evaluated as
having the best expected
diagnostic quality during
an exam for a given
reference view. If the user


Auto-Capture, the software
allows the user to
retrospectively record the
highest quality clip
obtained so far, mimicking
the choice a sonographer
might make when
recording an exam.

does not manage to
perform an auto-record for
the reference view, the
Best-Effort-Record clip is
proposed to the user.


Ability to classify
ultrasound
images with
similar accuracy
as experts

Cohen’s kappa
score between the
model’s predictions
and the ground
truth labels made by
experts (by frame)

Lower bound of the
95% confidence
interval of the Cohen’s
kappa score > 0.6
(substantial
agreement) for the 10
reference view

Ability to provide
successful
guidance cues
on ultrasound
frames

Positive predictive
value of successful
guidance cues (by
frame)

Lower bound of the
95% confidence
interval of the positive
predictive value > 0.8
for the 10 reference
views

Ability to save
high-quality
records
according to
experts

Positive predictive
value of high-quality
records among
auto-records (by
clip)

Lower bound of the
95% confidence
interval of the positive
predictive value > 0.6
and point estimate of
the positive predictive
value > 0.8 for the 10
reference views

Table 2: Primary objectives, endpoints, and success criteria


Percent of diagnostic quality
% [95% Wilson CI*]

Qualitative Visual Assessment of LV Size

Qualitative Visual Assessment of LV Function


Qualitative Visual Assessment of RV Size

Qualitative Visual Assessment of Non-Trivial
Pericardial Effusion


Qualitative Visual Assessment of RV Function

Qualitative Visual Assessment of LA Size

Qualitative Visual Assessment of RA Size

Qualitative Visual Assessment of Segmental
Kinetics of the LV

Qualitative Visual Assessment of AV
98.8 [96.4;99.6]


Qualitative Visual Assessment of MV
100 [98.4;100]

Qualitative Visual Assessment of TV
95.4 [92.0;97.4]

Qualitative Visual Assessment of IVC Size


Table 5: Study results: diagnostic-quality clips


Table 6: Rationale and testing for each modification

Enhance the performance of the
AI models in the perspective of
extending the use of the ML-DS
with new ultrasound systems

Execute AI performance on
the reference test set and
system-level validation tests

M2 - Extend the use of
the ML-DSF with new
ultrasound systems

Enable support for a broader
range of ultrasound systems

Execute AI performance tests
on new system test set and
system-level validation test

M3 - Extend the use of
the ML-DSF with new
operating systems

Enable support for a broader
range of devices

Table 7 describes the requirements for the ultrasound systems that can be cleared through the


FDA-cleared ultrasound system
compatible with HeartFocus’ intended
use

● Ultrasound system shall be FDA-cleared
● Validated for cardiac use
● Validated for use on adults

● Technical integration shall allow HeartFocus
to access live ultrasound stream
● Compatible for an integration on
HeartFocus’ operating systems

● The ultrasound system shall provide an
image stream at a minimum of 15 frames
per second.
● Center or nominal frequency within 2–5
MHz, suitable for adult transthoracic
echocardiography
● At least 30 cm depth, to allow full
visualization of adult cardiac structures
● 2D B-mode imaging (grayscale)
● Cone-shaped field of view with an opening
angle of at least 80 degrees


Ability to classify
ultrasound
images with
similar accuracy
as experts

Cohen’s kappa
score between the
model’s predictions
and the ground
truth labels made by
experts (by frame)

Lower bound of the
95% confidence
interval of the Cohen’s
kappa score > 0.6
(substantial
agreement) for the 10
reference view

Ability to provide
successful
guidance cues
on ultrasound
frames

Positive predictive
value of successful
guidance cues (by
frame)

Lower bound of the
95% confidence
interval of the positive
predictive value > 0.8
for the 10 reference
views

Ability to save
high-quality
records
according to
experts

Positive predictive
value of high-quality
records among
auto-records (by
clip)

Lower bound of the
95% confidence
interval of the positive
predictive value > 0.6
and point estimate of
the positive predictive
value > 0.8 for the 10
reference views

Table 8: Primary objectives, endpoints, and success criteria for M1+M2 modifications.


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

Classification Name
Radiological computer-assist


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 CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT
SYSTEM (ETT-XR-100)

Radiological computer-assisted triage and notification software


EFAI CHESTSUITE XR
MALPOSITIONED ETT
ASSESSMENT SYSTEM (EFAI
ETTXR) is a radiological
computer-aided triage and notification
software indicated for use in the
analysis of chest X-ray (CXR) images
in adults. The device is intended to
assist hospital networks and
appropriately trained medical
specialists in workflow triage by
flagging and communicating
suspected positive cases of vertically
malpositioned endotracheal tube
(ETT) in relation to the carina.
Findings are flagged when the ETT
distal tip is assessed as being more
than 7 cm above the carina, less than 3
cm above the carina, or when it is
below the carina (i.e in the right or left
mainstem bronchus). The device
assesses solely the vertical position of
the ETT distal tip relative to the
carina, does not factor patient
positioning, and cannot detect
esophageal intubation. The device is
tested in the single lumen
endotracheal tube, while it may trigger
a false prioritization alert in the case
of properly positioned double lumen
ETT.
EFAI ETTXR analyzes cases using
deep learning algorithms to identify
suspected malpositioned ETT
findings. It makes case-level output
available to a PACS/workstation for
worklist prioritization or triage. EFAI
ETTXR is not intended to direct
attention to specific portions of an
image or to anomalies of an image. Its
results are not intended to be used on a
stand-alone basis for clinical

BriefCase is a radiological
computeraided triage and notification
software indicated for use in the
analysis of frontal chest X-ray (CXR)
images in adults or transitional
adolescents aged 18 and older. The
device is intended to assist hospital
networks and appropriately trained
medical specialists in workflow triage
by flagging and communicating
suspected positive cases of vertically
malpositioned endotracheal tube
(ETT) in relation to the carina.
Findings are flagged when the ETT
distal tip is assessed as being more
than 5 cm above the carina, less than 2
cm above the carina, or when it is
below the carina (i.e in the right or left
mainstem bronchus).
The device assesses solely the vertical
position of the ETT distal tip relative
to the carina, does not factor patient
positioning, and cannot detect
esophageal intubation. The device
does not provide results when the
carina is not well-visualized on the
x-ray image. The device does not
discriminate types of ETTs, as such a
properly positioned double lumen ETT
may trigger a false prioritization alert.
BriefCase uses an artificial
intelligence algorithm to analyze
images and highlight cases with
detected findings on a standalone
application in parallel to the ongoing
standard of care image interpretation.
The user is presented with
notifications for cases with suspected
findings. Notifications include
compressed preview images that are

6. Comparison of Technological Characteristics with Predicate Device


decision-making nor is it intended to
rule out malpositioned ETT or
otherwise preclude clinical assessment
of chest radiographs.

meant for informational purposes only
and not intended for diagnostic use
beyond notification. The device does
not alter the original medical image
and is not intended to be used as a
diagnostic device.
The results of BriefCase are intended
to be used in conjunction with other
patient information and based on the
user’s professional judgment, to assist
with triage/prioritization of medical
images. Notified clinicians are
responsible for viewing full images
per the standard of care.

Hospital networks and appropriately
trained medical specialists

No. No cases are removed from
Worklist or deprioritized.

No. No cases are removed from
desktop app or deprioritized

Artificial intelligence algorithm with
database of images.


Device Trade
Regulation
Common name
Device
Product
Classification

Medical charged-particle
radiation therapy system

MUJ
Associated
Product
Code(s):
QKB, LLZ

System,
Planning,
Radiation
Therapy
Treatment


Predicate trade name
(primary predicate is listed
first)


The proposed device and primary
predicate share an AI delineation
method.

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

The RPA provides
autocontouring for a range
of structures

The proposed device proposes
segmentation on the same
localizations with the same medical
images of different modalities using AI.

The proposed device and the primary
predicate allow AI automatic
contouring.

additional claim for the proposed device.


In addition, the SW architecture was changed to separate the image

communication platform from the BriefCase-Triage SW. The subject device consists of only the

algorithm analysis module which can be integrated with image communication platforms that meet the

BriefCase-Triage input and output requirements.

BriefCase-Triage for CSF (K203508) are identical in most aspects and differ mostly with respect to


Subject Device
Aidoc BriefCase-Triage for CSF

BriefCase-Triage is a radiological
computer aided triage and notification
software indicated for use in the
analysis of CT scans that include the
cervical spine, in adults or transitional
adolescents aged 18 and older. The
device is intended to assist hospital
networks and appropriately trained
medical specialists in workflow triage by
flagging and communication of linear
lucencies in the cervical spine bone in
patterns compatible with fractures.
BriefCase-Triage uses an artificial
intelligence algorithm to analyze images
and highlight cases with detected
findings in parallel to the ongoing
standard of care image interpretation.
The user is presented with notifications
for cases with suspected findings.
Notifications include compressed
preview images that are meant for
informational purposes only and not
intended for diagnostic use beyond
notification. The device does not alter
the original medical image and is not
intended to be used as a diagnostic
device.
The results of BriefCase-Triage are
intended to be used in conjunction with
other patient information and based on
their professional judgment, to assist
with triage/prioritization of medical
images. Notified clinicians are
responsible for viewing full images per
the standard of care.

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

Hospital networks and appropriately
trained medical specialists


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

No. No cases are removed from
desktop app or deprioritized

Inclusion/
Exclusion
criteria for
clinical
performance
testing

Inclusion Criteria:
● CT with or without contrast that
includes the cervical spine
(bone and standard kernel)
● Scans performed on
adults/transitional adults ≥ 18
years of age.
● Slice thickness: 0.3 mm - 5.0
mm
Exclusion Criteria
● All studies that have an
inadequate field of view

Inclusion Criteria:
● Non-enhanced cervical spine CT
(bone kernel only).
● Scans performed on
adults/transitional adults ≥ 18
years of age.
● Scans performed on CT scanners
with 64 or greater number of
detectors.
● Slice thickness: 0.625 mm - 2.5
mm axial or 2-3 mm sagittal or 2-3
mm. coronal reconstructions with
bone kernel.
Exclusion Criteria
● All scans that are technically
inadequate, including motion
artifacts, severe metal artifacts, or
an inadequate field of view.

Artificial intelligence algorithm with
database of images.


Exclusion ● CT with or without contrast that ● Non-enhanced cervical spine CT


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

- AHS module ( image acquisition);
- ACS module (image processing);
- Aidoc Desktop Application for
workflow integration (Feed/Worklist
(alternate names) and non-diagnostic
Image Viewer).


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

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


Table 5. Frequency Distribution of Gender *


Table 6. Frequency Distribution of Manufacturer


Medical image management and processing system


Device Manufacturer Name
DEEPTEK MEDICAL IMAGING PRIVATE LIMITED


physicians, radiologists, nurses,
and medical technicians.

certified medical practitioners,
including physicians, radiologists,
and medical technicians.

10.
Modalities
Various Image sources
Various image sources
Same

• Log In
• Worklist – Search Filter
• Worklist – Open image
• Work list- study List
• Worklist – Report
• Worklist – Series
• Viewer – View exam
• Viewer – Control View
window
• Viewer – view mode (real
resolution)
• Viewer – View mode
(Highlight)
• Viewer – Stacking
• Viewer – Changing the layout
• Viewer – Comparative study
• Viewer – Preset filter
• Viewer - Zoom
• Viewer – Panning
• Viewer - Invert Image
• Viewer – Viewing mode
(Normal/ Image/ Stack/
Custom/ Annotation)
• Viewer – Comparative study
• Viewer – Rotation
• MIP/MPR Reconstruction
• Viewer – Reference line
• Viewer – Sharpening

• Log In
• Worklist – Search Filter
• Worklist – Open image
• Work list- study List
• Worklist – Report
• Worklist – Series
• Viewer – View exam
• Viewer – Thumbnail view of the
series
• Viewer–Changing the layout
• Viewer–Comparative study
• Viewer – Preset filter
• Viewer - Zoom
• Viewer – Panning
• Viewer - Invert Image
• Viewer – Viewing mode
(Normal/Stack/ Custom/ Annotation/
synchronized screening)
• Viewer – Comparative study
• Viewer – Rotation
• Viewer- MIP/MPR Reconstruction
• Viewer – Reference line
• Viewer–Measurement- rectangular,
ellipse, angle/cobb angle
• Viewer – Cine/movie mode
• Viewer- Overlaying/ Hide or display
overlay
• Viewer- Magnify
• Viewer- Probe/HU


• Viewer – Measure
• Viewer – Inverting image color
• Viewer – Cine
• Viewer- Overlaying.

Optional
Integration of
FDA-cleared
3rd party AI
models

Web environment-based
PACS.
Viewing and handling DICOM
medical images. Review and
report study located on a
server.

Web environment-based
PACS.
Viewing and handling DICOM
medical images. Review, modify,
and approve the study located in a
server.


Specification
Subject Device
Primary Predicate Device
Secondary Predicate Device
Comparison

Device Name
V5med Lung AI
AVIEW Lung Nodule CAD
ClearRead CTTM

Medical Image Management and
Processing System

Medical image management and
processing system

Device Class
Class II
Class II
Class II
Same

Product Code
OEB/LLZ
OEB/LLZ
OEB/LLZ
Same

Review Panel
Radiology
Radiology
Radiology
Same

V5med Lung AI is a Computer-Aided
Detection (CAD) software designed to
assist radiologists in detecting
pulmonary nodules (with diameter of
4-30 mm) during CT examinations of
the chest for asymptomatic
populations. This software provides
adjunctive information to alert
radiologists to regions of interest with
suspected lung nodules that may
otherwise be overlooked. It can be
used in a concurrent read mode, where
the AI analysis results are displayed
alongside the original CT images
during both the initial review and any
subsequent reviews by the radiologist.
V5med Lung AI does not replace the
radiologist’s critical judgment or
diagnostic processes and should not
be used in isolation from the original
CT series.

AVIEW Lung Nodule CAD is a
Computer-Aided Detection (CAD)
software designed to assist
radiologists in the detection of
pulmonary nodules (with diameter 3-
20 mm) during the review of
CT examinations of the chest for
asymptomatic populations. AVIEW
Lung Nodule CAD provides
adjunctive information to alert the
radiologists to regions of interest
with suspected lung nodules that may
otherwise be overlooked. AVIEW
Lung Nodule CAD may be used as a
second reader after the radiologist
has completed their initial read. The
algorithm has been validated using
non-contrast CT images, the majority
of which were acquired on Siemens
SOMATOM CT series scanners;
therefore, limiting device use to use
with Siemens SOMATOM CT series
is recommended.

ClearRead CT™ is comprised of
computer assisted reading tools
designed to aid the radiologist in the
detection of pulmonary nodules
during review of CT examinations of
the chest on an asymptomatic
population. The ClearRead CT
requires both lungs be in the field of
view. ClearRead CT provides
adjunctive information and is not
intended to be used without the
original CT series.

V5med Lung AI and AVIEW Lung
Nodule CAD (predicate device) are
both Computer-Aided Detection (CAD)
software designed to assist radiologists
in detecting pulmonary nodules during
CT examinations of the chest for
asymptomatic populations. Both
provide adjunctive information to alert
radiologists to regions of interest with
suspected lung nodules that may
otherwise be overlooked.
However, V5med Lung AI supports a
broader nodule diameter range (4-30
mm), compared to AVIEW's range of
3-20 mm, making it similar to
ClearRead CT™, which also covers
nodules from 5-20 mm. Additionally,
V5med Lung AI does not restrict the
use to non-contrast images, unlike
AVIEW, aligning it more closely with
ClearRead CT™. Furthermore, V5med
Lung AI does not limit the scanner
source, while AVIEW is validated

Functional Specification Comparison Table for the V5med Lung AI and AVIEW Lung Nodule CAD (K221592):


primarily with Siemens SOMATOM
CT series scanners, making V5med
Lung AI more flexible like ClearRead
CT™. These differences do not impact
the safety and effectiveness of V5med
Lung AI.

The V5med Lung AI is a software
product designed to detect nodules in
the lungs. The detection model is
trained using a Deep Convolutional
Neural Network (CNN) based
algorithm, enabling automatic
detection of lung nodules ranging
from 4 to 30 mm in chest CT images.
The system integrates algorithm logic
and database on the same server,
ensuring simplicity and ease of
maintenance. It accepts chest CT
images from a Picture Archiving and
Communication System (PACS),
Radiological Information System
(RIS), or directly from a CT scanner,
analyzes the images, and provides
output annotations regarding lung
nodules.

The AVIEW Lung Nodule CAD is a
software product that detects nodules
in the lung. The lung
nodule detection model was trained
by Deep Convolution Neural
Network (CNN) based algorithm
from the chest CT image. Automatic
detection of lung nodules of 3 to
20mm in chest CT images.
By complying with DICOM
standards, this product can be linked
with the Picture Archiving and
Communication System (PACS) and
provides a separate user interface to
provide functions such as analyzing,
identifying, storing, and transmitting
quantified values related to lung
nodules. The CAD’s results could be
displayed after the user’s first read,
and the user could select or de-select
the mark provided by the CAD. The
device’s performance was validated
with SIEMENS’ SOMATOM series
manufacturing. The device is
intended to be used with a cleared
AVIEW platform.

ClearRead CT is a dedicated post-
processing application that
generates a secondary vessel
suppressed Lung CT series with
CADe marks and associated region
descriptors intended to aid the
radiologist in the detection of
pulmonary nodules.

V5med Lung AI and AVIEW Lung
Nodule CAD both utilize Deep
Convolutional Neural Network (CNN)
based algorithms for lung nodule
detection in chest CT images. However,
V5med Lung AI supports a broader
nodule detection range of 4 to 30 mm,
compared to AVIEW’s 3 to 20 mm,
making it similar to ClearRead CT™,
which also covers nodules from 5 - 20
mm.Unlike AVIEW, which is
validated with SIEMENS’ SOMATOM
series and intended to be used with a
cleared AVIEW platform, V5med Lung
AI does not restrict the scanner source
or the use of non-contrast images,
aligning it more closely with ClearRead
CT™ in terms of flexibility. These
differences do not impact the safety and
effectiveness of V5med Lung AI.

Pulmonary nodules in
screening and diagnostic chest CT
acquisitions.

Pulmonary nodules in
non-contrast chest CT
acquisitions

Pulmonary nodules in
chest CT acquisitions.

The detection targets of V5med Lung
AI are similar to the detection targets of
the predicate devices.


Diameter:
• Pulmonary nodules 4 – 30 mm
Locations:
• Full range: central, peripheral

Diameter:
• Pulmonary nodules 3 – 20 mm
Locations:
• Full range: central, peripheral
Contours:
• Round, irregular

Diameter:
• The diameter range of V5med
Lung AI is similar to the
predicate devices
Locations:
• Same as primary predicate

A bounding box is provided around
nodules

Automatically locate
and identify lung
nodules

No specific hosting platform. Same as
cleared ClearRead CT.

No specific hosting application. Same
as cleared ClearRead CT.

DICOM GSPS (Grayscale Softcopy
Presentation State)
XML (Coordinate of detected
nodules)
Able to view results
on AVIEW, AVIEW
LCS viewer page

Similar to AVIEW Lung Nodule CAD
but lacks XML output and specific
viewer capabilities of AVIEW.
The outputs of V5med Lung AI are
similar to the outputs of the predicate
devices.

Multi-vendor and multi-detector
CT (MDCT) scanners
(Siemens, GE, Philips, and
Toshiba)

V5med Lung AI does not restrict the
brand or specifications of the scanner,
same as ClearRead CT.


concerns, it demonstrates substantial equivalence to the predicate device.


Items
Proposed MR Contour DL
Predicate: Auto
Comparison

MR Contour DL is intended
to be used as a workflow
tool for initial anatomy
segmentation of organs at
risk on MR images as an aid
in radiation therapy
planning after user
confirmation.

Auto Segmentation is
intended to be used as a
workflow tool for initial
anatomy segmentation of
organs at risk on CT images
as an aid in radiation
therapy planning after
user confirmation.

Both the proposed device and the
predicate device are intended to be
used as a workflow tool for initial
anatomy segmentation of organs at risk
as an aid in radiation therapy planning
after user confirmation. The proposed
device is intended for post-processing
MR images, whereas the predicate is
intended for post-processing of CT
images. All features and performance of
the proposed device have been verified
and validated per GE HealthCare’s
quality system. No safety and
effectiveness issues were raised. In
addition, the performance testing
conducted on MR Contour DL
successfully demonstrate the devices
performance, particularly the anatomy
segmentation of organs at risk on MR
images. The intended use of the
proposed device falls within the general
intended use of the predicate device
and do not create a new Intended Use.

Table 1: Comparison of Intended Use between Proposed Device and Predicate Device


MR Contour DL generates a
Radiotherapy Structure Set
(RTSS) DICOM with
segmented organs at risk
which can be used by
trained medical
professionals. It is intended
to aid in radiation therapy

Auto Segmentation
generates a Radiotherapy
Structure Set (RTSS)
DICOM with segmented
organs at risk which can
be used by dosimetrists,
medical physicists, and
radiation oncologists as

Substantially Equivalent – Both the
proposed device and the predicate
device are indicated to generate a
Radiotherapy Structure Set (RTSS)
DICOM with segmented organs at risk.
Both the proposed device and the
predicate device are:
• Used by trained medical

Table 2: Comparison of Indications for Use between Proposed Device and Predicate Device


planning by generating
initial contours to
accelerate workflow for
radiation therapy planning.
It is the responsibility of
the user to verify the
processed output contours
and user-defined labels for
each organ at risk and
correct the contours/labels
as needed. MR Contour DL
is intended to be used with
images acquired on MR
scanners, in adult patients.

initial contours to
accelerate workflow for
radiation therapy
planning. It is the
responsibility of the user
to verify the processed
output contours and user-
defined labels for each
organ at risk and correct
the contours/labels as
needed. Auto
Segmentation may be
used with images acquired
on CT scanners, in adult
patients.

professionals, including the
professions listed by the
predicate device.
• Intended to aid in radiation
therapy planning by generating
initial contours to accelerate
workflow for radiation therapy
planning.
• Declare that it is the
responsibility of the user to
verify the processed output
contours and user-defined
labels for each organ at risk and
to correct the contours/labels
as needed.
MR Contour DL is intended to be used
with images acquired on MR scanners in
adult patients while the predicate
device is intended to be used on images
acquired on CT scanners in adult
patients.
All features and performance of the
proposed device have been verified and
validated per GEHC’s quality system. No
safety and effectiveness issues were
raised. Additionally, the testing
performed on MR Contour DL
demonstrates the devices performance,
particularly the anatomy segmentation
of organs at risk on MR images. The
indications for use for the proposed
device is substantially equivalent to that
of the predicate.

510(k) Premarket Notification Submission – MR Contour DL


Items
Proposed MR
Predicate:
Reference 1:
Reference 2:
Comparison


Radiologists,
Radiation
oncologists,
Dosimetrists,
and Medical
Physicists

Radiation
Oncologists,
Dosimetrists,
and Medical
Physicists

Medical
Professionals
who do
radiation
therapy
treatment
planning

Substantially Equivalent – both the
proposed device and the predicate
device are intended for trained
medical professionals in the
radiation oncology field

Substantially Equivalent – both the
proposed device and the predicate
device contain deep learning auto
segmentation algorithms

Substantially Equivalent – While the
predicate device is only intended to
segment CT Images, both reference
devices are intended to segment
both CT and MR images. Reference 1
uses DICOM-compliant image data
(CT or MR) to automatically contour
various structures of interest for RT
planning. Reference device 2 is
intended to assist in the automated
processing of digital medical images
of modalities CT and MR to create
contours using machine learning
algorithms. The proposed device
aligns with the performance of
commercially available products on
the market, specifically Reference
Devices 1 and 2.

Head/neck
Prostate
Thorax
Abdomen
Lungs and
Liver

Substantially Equivalent – Both the
predicate and proposed device cover
the Head/neck and Pelvis regions.

Automated
execution of
the software
with no user
interaction,
other than
configuration
settings.
Generated

Contains both
an automated
processing
component,
Data
Visualization,
and Graphical
User Interface

Designed for
use in the
processing of
medical
images and
operates on
Windows,
Mac, and
Linux


contours are
automatically
transmitted to
review
workstation(s)
supporting
RTSS objects
for review and
editing, as
needed.

computer
systems.
Deployed on a
remote server
using the
MIMcloud
service for
data
management
and transfer;
or locally on
the
workstation or
server running
MIM software.

Compatible
on MR
Scanners,
DICOM
compliance
required.

No limitation
on scanner
model,
DICOM
compliance
required.

Substantially Equivalent – The
proposed device is compatible with
MR scanner models and DICOM
compliance is required. The
proposed device has only been
tested on GEHC data. If a non-GEHC
MR image is sent to MR Contour DL,
a notice message will display after
contouring.
The predicate device has no
limitation on scanner models and
DICOM compliance is also required.
This difference does not raise any
new safety or effectiveness
concerns.

Cloud-based
deployment
and locally
deployed (or
installed)

Identical – Both the proposed
device and the predicate device
are deployed on Edison HealthLink
(EHL), GE Healthcare’s
computational platform providing
hosting infrastructure and services.


Trade/Device/Model Name
OncoStudio / OS-01

Medical Image Management and Processing System

3. Trade Name, Common Name, Classification [21 CFR 807.92(a)(2)]


Trade/Device/Model Name
AutoContour Model RADAC V3

Medical Image Management And Processing System


Trade/Device/Model Name
AI-Rad Companion Organs RT


Item
OncoStudio
AutoContour Model RADAC
AI-Rad Companion

Medical Image Management
And Processing System

OncoStudio provides deep-
learning-based automatic
contouring to organs at risk
in DICOM-RT format from CT
images. This software could
be used as an initial
contouring for the clinicians
to be confirmed by the
radiation oncology
department for treatment
planning or other professions
where a segmented mask of
organs is needed.
• Deep learning
contouring from Head &
Neck, Thorax, Abdomen,
and Pelvis
• Generates DICOM-RT
structure of contoured
objects
• Manual Contouring
• Receive, transmit, store,

AutoContour is intended to
assist radiation treatment
planners in contouring and
reviewing structures within
medical images in
preparation for radiation
therapy treatment
planning

AI-Rad Companion
Organs RT is a post-
processing software
intended to
automatically
contour DICOM CT
and MR predefined
structures using
deep-leaming-based
algorithms. Contours
that are generated
by AI-Rad
Companion Organs
RT may be used as
input for clinical
workflows including
external beam
radiation therapy
treatment planning.
AI-Rad Companion
Organs RT must be
used in conjunction
with appropriate

[Table 1. Comparison of Proposed Device to Predicate Device and Reference Device]


retrieve, display, and process
medical images and DICOM
objects

software such as
Treatment Planning
Systems and
Interactive
Contouring
applications, to
review, edit, and
accept contours
generated by AI-Rad
Companion Organs
RT. The output of AI-
Rad Companion
Organs RT are
intended to be used
by trained medical
professionals. The
software is not
intended to
automatically detect
or contour lesions.

• A_Aorta
• A_Carotid_L
• A_Carotid_R
• A_Coronary_R
• A_Iliac_L
• A_Iliac_R
• A_LAD
• A_Subclavian_L
• A_Subclavian_R
• Anus
• Atrium_L
• Atrium_R
• Autochthon_L
• Autochthon_R
• Bag_Bowel
• Bladder
• Bone_Mandible
• Bowel_Large
• Bowel_Small
• BrachialPlex_L
• BrachialPlex_R
• Brachiocephalic_Trunk
• Brain
• Brainstem
• Breast_L
• Breast_R

• LN_Sclav_L_RTOG
• LN_Sclav_R_ESTRO
• LN_Sclav_R_RTOG
• Larynx
• Lens_L
• Lens_R
• Liver
• Lobe_Temporal_L
• Lobe_Temporal_R
• Lung_L
• Lung_LLL
• Lung_LUL
• Lung_R
• Lung_RLL
• Lung_RML
• Lung_RUL
• OpticChiasm
• OpticNrv_L
• OpticNrv_R
• Pancreas
• Parotid_L
• Parotid_R
• Pharynx
• Pituitary
• Prostate
• Rectum

• A_Aorta
• A_Aorta_Asc
• A_Aorta_Dsc
• A_LAD
• A_Pulmonary
• Bladder
• Bladder_F
• Bone_Ilium_L
• Bone_Ilium_R
• Bone_Mandible
• Bone_Pelvic
• Bone_Skull
• Bone_Sternum
• Bowel
• Bowel_Bag
• Bowel_Large
• Bowel_Small
• BrachialPlex_L
• BrachialPlex_R
• Brain
• Brainstem
• Breast_L
• Breast_R
• Bronchus
• BuccalMucosa
• Carina

• LN_Ax_L
• LN_Ax_L1_L
• LN_Ax_L1_R
• LN_Ax_L2_L
• LN_Ax_L2_L3_L
• LN_Ax_L2_L3_R
• LN_Ax_L2_R
• LN_Ax_L3_L
• LN_Ax_L3_R
• LN_Ax_R
• LN_IMN_L
• LN_IMN_R
• LN_IMN_RC_L
• LN_IMN_RC_R
• LN_Inguinofem_L
• LN_Inguinofem_R
• LN_Neck_IA
• LN_Neck_IB-V_L
• LN_Neck_IB-V_R
• LN_Neck_II_L
• LN_Neck_II_R
• LN_Neck_II-IV_L
• LN_Neck_II-IV_R
• LN_Neck_II-V_L
• LN_Neck_II-V_R
• LN_Neck_III_L

(166 OARs)
Full list of OAR is not provided


•
CaudaEquina
•
Rib02_L
• Cavity_Oral_Ext
•
LN_Neck_IV_R

•
Colon
•
Rib05_L
• Cochlea_L
•
LN_Neck_VIIB_L

•
Duodenum
•
Rib07_L
• Cornea_R
•
LN_Pelvic_NRG

•
Esophagus
•
Rib07_R
• Duodenum
•
LN_Sclav_L

•
Gallbladder
•
Rib11_L
• Femur_Head_L
•
Lung_R

•
Glnd_Submand_R
•
Sacrum
• Femur_RTOG_L
•
Marrow_Ilium_R

•
Gluteus_Medius_L
•
Skull
• Genitals_M
•
OpticChiasm

•
Gluteus_Medius_R
•
SpinalCord
• Glnd_Lacrimal_L
•
OpticNrv_L

•
Gluteus_Minimus_L
•
Spleen
• Glnd_Lacrimal_R
•
OpticNrv_R

•
Gluteus_Minimus_R
•
Sternum
• Glnd_Submand_L
•
Pancreas

• Bronchus_L • Rib01_L • CaudaEquina • LN_Neck_III_R


• Kidney_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_IMN_L
• LN_IMN_R
• LN_Neck_IA
• LN_Neck_IB_L
• LN_Neck_IB_R
• LN_Neck_III_L
• LN_Neck_III_R
• LN_Neck_II_L
• LN_Neck_II_R
• LN_Neck_IVA_L
• LN_Neck_IVA_R
• LN_Neck_IVB_L
• LN_Neck_IVB_R
• LN_Neck_VA_L
• LN_Neck_VA_R
• LN_Neck_VBC_L
• LN_Neck_VBC_R
• LN_Sclav_L_ESTRO

• VB_L4
• VB_L5
• VB_S1
• VB_T01
• VB_T02
• VB_T03
• VB_T04
• VB_T05
• VB_T06
• VB_T07
• VB_T08
• VB_T09
• VB_T10
• VB_T11
• VB_T12
• V_Brachioceph_L
• V_Brachioceph_R
• V_Iliac_L
• V_Iliac_R
• V_Portal_And_Splenic
• V_Pulmonary
• V_Venacava_I
• V_Venacava_S
• Ventricle_L
• Ventricle_R

• Kidney_Outer_R
• Larynx
• Larynx_Glottic
• Larynx_NRG
• Larynx_SG
• Lens_L
• Lens_R
• Lips
• Liver

• Rib_R
• SeminalVes
• SpinalCanal
• SpinalCord
• Spleen
• Stomach
• Trachea
• UteroCervix
• V_Venacava_I
• V_Venacava_S
• VB
• VB_C1
• 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

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


with Linux. Windows
python-based automatic
contouring application
supporting Microsoft
Windows 10 (64-bit) and
Microsoft Windows Server
2016.

1) Deep learning contouring
from Head & Neck, Thorax,
Abdomen, and Pelvis
2) Generates DICOM-RT
structure of contoured
objects
3) Manual Contouring
4) Receive, transmit, store,
retrieve, display, and process
medical images and DICOM
objects

1) Automatically contour
various structures of
interest for radiation
therapy treatment
planning
2) Allow the user to review
and modify the resulting
contours
3) Generate DICOM-
compliant structure set
data the can be imported
into a radiation therapy
treatment planning system

CT or MR series of
images serve as
input for AI-Rad
Companion Organs
RT and are acquired
as part of a typical
scanner acquisition.
Once processed by
the AI algorithms,
generated contours
in DICOM-RTSTRUCT
format are reviewed
in a confirmation
window, allowing
clinical user to
confirm or reject the
contours before
sending to the target
system. Optionally,
the user may select
to directly transfer
the contours to a
configurable DICOM
node

Algorithm
Deep Learning
Deep Learning
Deep Learning
Same

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

Head & Neck,
Thorax, Abdomen &
Pelvis Head & Neck
lymph nodes

Automatically processes input
image data contour organs


SUBJECT:
AudaxCeph Cephalogram
Analysis Software

PREDICATE:
CephX Cephalometric Analysis
Software

Designed for use by
specialized dental practices
for storing and presenting
patient images and assisting
in treatment planning.
Results produced by the
software's treatment planning
tools are dependent on the
interpretation of trained and
licensed practitioners.

Indicated for use by dentists who
provide orthodontic treatment for
image analysis, simulation,
profilogram, VTO (Visual
Treatment Objective), and patient
consultation. Results produced by
the software’s diagnostic,
treatment planning and simulation
tools are dependent on the
interpretation of trained and
licensed practitioners or dentists.
The device is only for use on
patients 14 years old and above.

Orthodontic, oral maxillofacial
surgery, and dental-specialty
practices.

Materials, Additives, &
Finishing Agents

Energy Delivery
N/A, software-only device
N/A, software-only device

Anatomical Area
Dental, Maxilla, Mandible
Dental, Maxilla, Mandible

2D Image Acquisition/Digitizing
(radiological/image capture
interface/controls)

Table 1: Comparison of Subject Device with Predicate Device


Medical device software - Software life
cycle processes

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

Medical devices - Application of risk
management to medical devices

Table 2: Non-Clinical Testing Standards & Guidance


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

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

Principles for medical device security - Risk
management.


Parameter
Acceptance Criteria
Conclusion

Table 3: Automatic Landmark Detection Accuracy Study Results


Predicate Device & Reference Device Comparison

Topic
Proposed Device
Predicate Device
Reference Device

Manufacturer
Artrya Ltd.
Circle Cardiovascular
TeraRecon, Inc.

Model Name
Salix Central
cvi42 Auto Imaging
iNtuition-Structural Heart

Salix Central is intended to
complement standard care
as an adjunctive tool and is
not intended as a
replacement to a medical
professional’s
comprehensive diagnostic
decision-making process.
The software’s semi-
automated features are
intended for an adult
population and should only
be used by qualified
medical professionals
experienced in examining
and evaluating cardiac CT
images.

The target population for
cvi42 Auto’s manual
workflows is not
restricted; however, Salix
Central’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, for the purpose of
obtaining diagnostic
information as part of a
comprehensive diagnostic
decision-making process.

tools and features that
facilitate: - Automatic and
manual centerline
detection. - Segmentation
of cardiovascular
structures

Cardiologists, Radiologists
and Clinical Specialists


Series, slices, and phases
Series, slices, and phases

Centerline
Wall
Signal Intensity Overlay

Distance
Diameter
Area
Hounsfield Unit (HU)
Lumen % diameter
reduction

Distance
Perimeter
Area
Hounsfield Unit (HU)
Volume

Manual and semi-automatic
/ user-editable

Manual and semi-
automatic using Machine
Learning technique


Salix Central Output
Statistic
Estimate
Acceptance
Result

Salix Central performance exceeded all the pre-defined acceptance criteria for all validation tests.


Real-Time Guidance
for Additional 2D TTE
Views

Added cardiac views to standard
echocardiography views, focusing
on various anatomical regions
(e.g., PLAX, PSAX, Apical,
Subcostal, Suprasternal).

Expands the list of views in previously
authorized PCCP by adding additional
standard cardiac subset views. These
additional cardiac ultrasound views are
focused on enhanced diagnostic imaging and
coverage of specific anatomical regions.

Labeling compatibility
of new compatible
ultrasound systems
and UI Enhancement

Ensured software compatibility
with ultrasound systems of similar
screen sizes.

Builds on compatibility with additional UI/UX
improvements, incl. support for various screen
sizes such as mobile platforms.

Adds standalone test protocol to enable
validation of modified device performance by
the intended user groups, ensuring
equivalency to the original device based on
predefined clinical endpoints.

Introduces Human Factors Testing to evaluate
user interactions and ensure critical tasks
remain unaffected by UI/UX modifications.


Retraining/optimization/
modification of core algorithm(s)

These changes enhance model
robustness, reduce bias, improve
generalizability, and enable
Caption Health to respond
effectively real-world feedback.

Repeating verification tests and
the system level validation test to
ensure the pre-defined acceptance
criteria are met.

Real-time guidance for additional
2D TTE views: This modification
expands the list of views by adding
additional cardiac subset views

These changes enhance the
system’s diagnostic capabilities by
providing more focused, specific
views that assist expert clinicians
in making more accurate cardiac
assessments, improving overall
usability.

Repeating verification tests and
two system level validation tests
including usability testing to
ensure the pre-defined acceptance
criteria are met for the additional
views.

Optimization of the core
algorithm(s) implementation in
the software such as optimization
of algorithm thresholds, averaging
logic, transfer functions,
frequency, and refresh rate

These optimizations improve the
performance of the algorithm,
making it more efficient and
accurate without changing its
fundamental functionality, ensure
consistency across different
devices and enhance the usability
of the device

Repeating relevant verification
test(s) and the system level
validation test to ensure the pre-
defined acceptance criteria are
met.

Addition of new types of
prescriptive guidance for existing
views such as patient positioning
and breathing guidance, guidance

These changes provide additional
real-time feedback to users
enhances system’s utility and user
experience in obtaining diagnostic

Repeating relevant verification
tests and two system level
validation tests including usability

A summary of these software modifications, rationale and test methods are presented in the table below:


Predicate Device
uMR Omega (K240540,
K230152, K220332)

Type of Magnet
Superconducting
Superconducting
Same


2.30 ppm @ 50cm DSV
0.80 ppm @ 45cm DSV
0.38 ppm @ 40cm DSV
0.08 ppm @ 30cm DSV
0.02 ppm @ 20cm DSV
0.002 ppm @ 10cm DSV

Resonant frequencies
128.23 MHz
128.23 MHz
Same


Patient Table:
width 640mm, height
880mm, length 2620mm
Detachable Table:
width 810mm, height
880mm, length 2505mm

Patient Table:
width 640mm, height
880mm, length 2620mm
Detachable Table:
width 826mm, height
880mm, length 2578mm

Patient Table:
310 kg
Detachable Table:
310kg

Wireless UIH Gating Unit
REF 453564324621
ECG module Ref
989803163121
SpO2 module Ref
989803163111
(alternative)

ECG, Rrespiration and
pulse module
uVWMERP
Wireless gating trigger unit
uMVRX
(alternative)


Table 2 Comparison to Reference device#1


Table 3 Comparison to Reference device#2


The intended use of Breast Coil - 12 is essentially identical to previously cleared Breast
Coil - 10. There are two differences between Breast Coil – 12 and Breast Coil – 10. One
is that Breast Coil - 12 can be used with Biopsy Configuration to provide breast biopsy
function. The other one is the number of channels of the receiver coil.
The difference did not raise new safety and effectiveness concerns.

The intended use of Head Coil - 16 is equivalent to previously cleared Head Coil - 32.
The only difference between them is the number of channels of the receiver coil.
The difference did not raise new safety and effectiveness concerns.

The intended use of Tx/Rx Head Coil is essentially identical to previously cleared Head
Coil - 12. There are two differences between Tx/Rx Head Coil and Head Coil – 12. One
is that Tx/Rx Head Coil is of a 16 Legs High-Pass Birdcage used for transmitter coil. The
other one is the number of channels of the receiver coil.
The difference did not raise new safety and effectiveness concerns.

The dimensions of detachable table were changed from width 826mm, height 880mm,
length 2578mm to width 810mm, height 880mm, length 2505mm.
The difference did not raise new safety and effectiveness concerns.

The model name of mmw component was changed from mmw100 to mmw101.
The difference did not raise new safety and effectiveness concerns.

Inline ECV aims to calculate the pixel-wise ECV (extracellular volume fraction) images
from the native and post T1 mapping.
The difference did not raise new safety and effectiveness concerns.

Inline MOCO is a function that perform motion correction on MR images, which can
reduce the motion caused by physiological factors such as breathing and heart beat in
the images.
The difference did not raise new safety and effectiveness concerns.


MTP is substantially equivalent to GRE and acquires two flip angles and multi-echo
images with one scan, and then uses specific image processing to attain multi-parametric
images.
The difference did not raise new safety and effectiveness concerns.

TI Scout automatically selects the TI frame which has the darkest ventricular
myocardium of the TI Scout image, allowing users to achieve the best inversion time
(TI) and simplifying the workflow which needed the TI.
The difference did not raise new safety and effectiveness concerns.

Breast Biopsy is a workflow that can provide biopsy instruction for technicians based
on the hardware and lesion parameters, the instruction include the biopsy grid
coordinate, needle block cell and needle depth.
The difference did not raise new safety and effectiveness concerns.

During this submission, Body Part Recognition function was included in uVision, which
allows assist patient positioning by performing image recognition on human natural
images through a 3D camera during the positioning stage.
The difference did not raise new safety and effectiveness concerns.

EasyScan of the proposed device supports more body parts than that of the predicate
device. In this submission, breast and pelvis and hip and ankle and thorax are included.
The difference did not raise new safety and effectiveness concerns.

SparkCo is an algorithm that can detect and correct spark artifacts (a specific occurring
MRI artifact that is caused by electromagnetic interference (EMI)) in MRI images.
The difference did not raise new safety and effectiveness concerns.

During this submission, inline ED/ES Phases Recognition for Real Time Cine was
included in Inline Cardiac function, which allows automatically rearrange the cardiac
images into an aligned cardiac cycle.
The difference did not raise new safety and effectiveness concerns.

Inline MRS of the proposed device are the same as the predicate device. The difference
is that the algorithms of Inline MRS are shifted from the post-processing workstation to
inline console.
The difference did not raise new safety and effectiveness concerns.

EasyCrop of the proposed device supports more body parts than that of the predicate
device. In this submission, head and carotid and renal are included.
The difference did not raise new safety and effectiveness concerns.


Table 4. The demographic distribution of real-world spark testing dataset


Test parts
Test Methods
Accept criteria
Test Results

Based on the real-world
testing dataset, calculating
the detection accuracy by
comparing the spark
detection results with the
ground-truth.

The average detection
accuracy need be larger
than 90%

Test on the
spark
correction
performance

Based on the simulated spark
testing dataset, calculating
the PSNR(Peak signal-to-
noise ratio) of the spark-
corrected images and original
spark images
Based on the real-world
spark dataset, evaluating the
image quality improvement
between the spark-corrected
images and spark images by
one experienced evaluator.

The average PSNR of
spark-corrected images
need to be higher than
the spark images.
Spark artifacts need to
be reduced or corrected
after enable the
SparkCo.

The average PSNR of
spark-corrected images
is 1.6 higher than the
spark images.
The images with spark
artifacts were
successfully corrected
after enable the
SparkCo.

Table 6 The test methods and test results of SparkCo


To verify the effectiveness of the algorithm, the subjective evaluation
method was used. The segmentation result of each case was obtained with
the algorithm, and the segmentation mask was evaluated with the following
criteria. The test pass criteria was: no failure cases, satisfaction rate
S/(S+A+F) exceeding 95%.
The criteria is as follows:
Satisfied (S): the segmentation myocardial boundary adheres to the

myocardial boundary and blood pool ROI is within the blood pool excluding
the papillary muscles.
Acceptable (A): These are small missing or redundant areas in the

myocardial segmentation but not obviously and the blood pool ROI is within
the blood pool excluding the papillary muscles.
Fail (F): The myocardial mask does not adhere to the myocardial

boundary or the blood pool ROI is not within the blood pool, or the blood
pool ROI contains papillary muscles.

Table 7 Validation type and acceptance criteria


Table 9 Segmentation algorithm subgroup analysis


Kenny M Bello
Regulatory Affairs
monsuru.bello@siemens-healthineers.com
+1(202) 856-6099

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

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

Siemens Healthineers AG
Siemensstr 1
D-91301 Forchheim, Germany
Establishment Registration Number: 3004977335

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


syngo.CT LVO Detection
syngo.CT Brain Hemorrhage

syngo.CT LVO Detection is a radiological post-
processing application for the analysis of CT
angiography (CTA) head images. syngo.CT LVO
Detection supports computer-aided triage, and it
addresses vascular abortions in the CTA of the
brain, commonly referred to as large vessel
occlusion (LVO), in the ICA, M1, and M2 segment.
The output for triage is intended for
informational purposes only. It is not intended
for diagnostic use and does not alter the original
medical image.

syngo.CT Brain Hemorrhage is designed to assist
the radiologist in prioritizing cases of suspected
intracranial hemorrhage, also in the
subarachnoid space, on non-contrast CT
examinations of the head. It makes case-level
output available to a CT scanner or other PACS
system for worklist prioritization.
The output is intended for informational
purposes only and is not intended for diagnostic
use. The device does not alter the original
medical image and is not intended to be used as
a standalone diagnostic device.

6. Indications for Use Comparison to the Predicate Device


syngo.CT LVO Detection
(SOMARIS/8 VB80)
(K243145)

Notification-only, parallel
Yes
Yes
Same

Radiologists and clinical
administrators

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

Artificial Intelligence algorithm
Yes
Yes
Same

Limited to analysis of imaging data
Yes
Yes
Same

Scanner Manufacturer of Input
Siemens and other vendors
Siemens and other vendors
Same

Suspected hemorrhage/
Processing finished


Standard
Version
Content
FDA Recognition

62306 Edition 1.1
2015-06
CONSOLIDATED
VERSION

Medical device software - Software life cycle processes

Digital Imaging and Communications in Medicine
(DICOM) Set

Application of Risk Management to Medical Devices

Edition 1.1 2020-06
CONSOLIDATED
VERSION

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


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

First edition 2021-
04 Corrected
version 2021-12

Medical devices - Information to be supplied by the
manufacturer


Subject Device
TeraRecon Aorta.CT
K243158

TeraRecon Aorta.CT is
intended to provide an
automatic 3D segmentation
and label anatomical
landmarks of the Aorta. The
results of TeraRecon
Aorta.CT are intended to be
used in conjunction with
other patient information by
trained professionals who
are responsible for making
any patient management
decision per the standard of
care. TeraRecon Aorta.CT
is a software as a medical
device (SaMD) deployed as
a containerized application.
The device inputs are CT
Angiography with contrast
DICOM images. The device
outputs are DICOM result
files which may be viewed
utilizing DICOM-compliant
systems. The device does
not alter the original input
data and does not provide a
diagnosis.
TeraRecon Aorta.CT is
indicated to generate results
from aortic CT Angiography
scans taken of adult
patients except patients with
pre-existing aortic device,
bicuspid aortic valve
anomaly, aortic dissection,
aortic rupture, and
abdominal metallic devices.
The device is not specific to
any gender, ethnic group, or
clinical condition.

AutoSeg-H is medical
imaging software that is
intended to provide trained
medical professionals with
tools to aid them in reading,
interpreting, reporting, and
treatment planning.
AutoSeg-H accepts DICOM
compliant medical images
acquired from imaging
device (Computed
Tomography).
AutoSeg-H provides the
tools for specific analysis
applications which provide
custom UI, targeted
measurements and
reporting functions
including:
- Coronary Artery Analysis
for CT coronary
arteriography images: which
is intended for the
qualitative and quantitative
analysis of coronary
arteries.
- Valve Analysis: which is
intended for automatic
extraction of the heart and
aorta regions, automatic
detection of the contour of
the aorta and valves,
measurement of the vicinity
of the valves.
- 4-Chamber Analysis:
which is intended for
automatic extraction of the
left atrium, left ventricle,
right atrium, and right
ventricle from CT.

Differences:
The subject device does not include
functionality to provide professionals with
tools to read, interpret, report or treatment
plan.
The predicate device provides analysis tools
for coronary artery analysis and 4-chamber
analysis that the subject device does not
provide.
The predicate device provides measurements
of the vicinity of the cardiac valves which the
subject device does not provide.
The subject device includes landmarks of the
aorta that the predicate device does not.
The subject device indicates that it is not for
use on images of adult patients with pre-
existing aortic device, bicuspid aortic valve
anomaly, aortic dissection, aortic rupture, and
abdominal metallic devices. The predicate
device does not mention similar non use
scenarios.
Similarities:
The subject and predicate devices are both
indicated for use with Computed Tomography
(CT) DICOM images.
The subject and predicate devices are both
indicated for use on images of adult patients.
The subject and predicate devices both
provide segmentation results.
Summary:
The noted differences do not render the
subject device NSE because the identified
differences do not raise different safety and
effectiveness questions. The similarities show
that similar device output is provided based
on the same technology utilized in the
devices.

Supervised deep learning-
based algorithms


Results in form of 2D
Images, 3D Images,
Information and Results

Differences:
The predicate device outputs measurement
results that are not available in the subject
device.
Similarities:
Both the subject and predicate devices
output results following the DICOM
standard.
Summary:
The noted differences do not render the
subject device NSE because the identified
differences do not raise different safety and
effectiveness questions. The similarities
show that similar device output is provided
based on the same technology utilized in
the devices.


Training Dataset
Tuning Dataset
Validation Dataset

(n=676 samples)
(n=240 samples)
(n=217 samples)

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


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

Tumor Volume (n=184)
cubic centimeters (cc)
5.22 ± 15.58

Tumor-to-breast volume ratio (n=184)
%
0.51 ± 1.48

Tumor longest dimension (n=202)
centimeters (cm)
1.60 ± 1.93

Tumor-to-nipple distance (n=200)
centimeters (cm)
1.20 ± 1.37

Tumor-to-skin distance (n=202)
centimeters (cm)
0.63 ± 0.61

Tumor-to-chest distance (n=202)
centimeters (cm)
0.91 ± 1.14

Tumor center of mass (n=184)
centimeters (cm)
0.72 ± 1.42

Performance data for the automated measurements is summarized below:


Tumor segmentation (n=184)
Volumetric Dice
0.75 ± 0.24

Results of Dice and surface Dice are summarized below:


Manufacturer
SimBioSys, Inc.
SimBioSys, Inc.

Medical image management and
processing system

Device Common Name
Image Processing System
Image Processing System

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

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


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

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

"-non-invasive software package
-DICOM compatible"

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


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

Interactive rotation of 3D
volume rendering


Performance N Metric Predicate/ TumorSight Predicate/ Inter-

Measurement TumorSight Viz/ Ground radiologist

± Std. Dev.)
Std. Dev.)
Std. Dev.)
± Std. Dev.)


Classification Name
Computed tomography x-ray system


Bunkerhill AVC is a software device
intended for use in detecting presence
and estimating quantity of aortic valve
calcification for adult patients aged 40
years and above. The device
automatically analyzes non-gated, non-
contrast chest computed tomography
(CT) images collected during clinical
care and outputs the region of interest
(intended for informational purposes
only) and quantification of detected
calcium.

iCAC is a software device intended for
use in estimating presence and quantity
of coronary artery calcium for patients
aged 30 years and above during routine
care. The device automatically analyzes
non-gated, non-contrast chest computed
tomography (CT) images collected
during routine care and outputs a visual
representation of estimated coronary
artery calcium segmentation (intended
for informational purposes only) and
both exact and four-category quantitative
estimates of the patient’s coronary artery
calcium burden in Agatston units.

A table comparing the intended use of the subject and predicate devices is provided below.


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 quantification 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 region of
interest in a diagnostic image viewer.
The subject device output in no way
replaces the original patient report or the
original non-gated, non-contrast 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.

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.


Regulation number
21 CFR §892.1750
21 CFR §892.1750
Same

Slice thickness
Up to 5 mm
Up to 5 mm
Same

Visual output in the form
a Circular Region of
Interest (ROI) around
detected calcification
(intended for
informational purposes
only). The estimated
visual output can be
viewed by the physician
in a

Outputs a visual
representation of
estimated coronary
artery calcium
segmentation
(intended for
informational
purposes only). The

Predicate device shows
estimated segmentation
as one of the device
outputs whereas subject
device shows a ROI, not
segmentation.


diagnostic image viewer.
The physician’s standard
method for viewing
unaltered chest CT scans
in PACS will remain
available to them even if
the subject device is
being used. However, the
physician will also have
an option to view the
visual output estimated
by the subject device as a
separate series within
PACS.

estimated calcium
segmentation can be
viewed by the
physician in a
diagnostic image
viewer. The
physician’s standard
method for viewing
unaltered chest CT
scans in PACS will
remain available to
them even if the
subject device is being
used.
However, the
physician will also
have an option to view
the calcium
segmentation
estimated by the
subject device as a
separate series within
PACS.

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


Yes, estimated
Agatston-equivalent
score and binary
output
(presence/absence)

Yes, Coronary Calcium
Detection Category and
estimated Agatston score
4 detection categories

iCAC provides
estimation of coronary
calcium burden
whereas subject device
provides estimation of
calcification on aortic
valve.

Qualified medical
professionals such as
cardiologists or
radiologists

Intended location
Medical facility
Medical facility
Same

Measurement scale
Agatston-equivalent units
Agatston-equivalent units
Same


Primary Predicate
Overjet Dental
Assist
K210187

Reference Device
Videa Perio Assist
K223296

Manufacturer
Pearl Inc.
Overjet, Inc.
VideaHealth, Inc.

Image Modality
Radiograph
Radiograph
Radiograph

Dental automated
image processing
software device to aid
in bone level
measurement dental
radiograph review by
HCP

Second Opinion®
BLE is a radiological
automated image
processing software
device intended to
identify and display
bone level
measurements in
bitewing and
periapical
radiographs. It should
not be used in lieu of
full patient evaluation
or solely relied upon
to make or confirm a
diagnosis.
It is designed to aid
dental health
professionals to
review bitewing and
periapical radiographs
of permanent teeth in
patients 12 years of
age or older as a
concurrent and
second reader.

Overjet Dental Assist
is a radiological
semi-automated
image processing
software device
intended to aid dental
professionals in the
measurements of
mesial and distal bone
levels associated with
each tooth from
bitewing and
periapical
radiographs. It should
not be used in lieu of
full patient evaluation
or solely relied upon
to make or confirm a
diagnosis. The system
is to be used by
trained professionals
including, but not
limited to, dentists and
dental hygienists.

Videa Perio Assist is
a radiological
semi-automated
image processing
software device
intended to aid dental
professionals in the
measurements and
visualization of mesial
and distal bone levels
associated with each
tooth from bitewing
and periapical
radiographs.
Measurements are
made available as
linear distances or
relative percentages.
It should not be used
in-lieu of full patient
evaluation or solely
relied upon to make or
confirm a diagnosis.
The system is to be
used by trained
professionals
including, but not
limited to, dentists and
dental hygienists.


Utilizes computer
vision neural
network
algorithms,
developed from
open-source
models using
supervised
machine learning
techniques

Detection & display of
linear bone level
measurements


The demographics of the data set was as follows:


both precision & recall for bitewing and periapical images.


Primary Predicate
Second Opinion
K210365

Manufacturer
Pearl Inc.
Pearl Inc.
Overjet, Inc.

Dental CADe to aid in
dental radiograph review
by HCP

Table 1: Comparison of Second Opinion CS with the predicate devices.


Second Opinion® CS is a
computer aided detection
("CADe”) software to aid in
the detection and
segmentation of caries in
periapical radiographs.
It is designed to aid dental
health professionals to review
periapical radiographs of
permanent teeth in patients
12 years of age or older as a
second reader.

Second Opinion® is a computer
aided detection ("CADe”) software
to identify and mark regions in
relation to suspected dental
findings which include Caries,
Discrepancy at the margin of an
existing restoration, Calculus,
Periapical radiolucency, Crown
(metal, including zirconia &
non-metal), Filling (metal &
non-metal), Root canal, Bridge,
and Implants.
It is designed to aid dental health
professionals to review bitewing
and periapical radiographs of
permanent teeth in patients 12
years of age or older as a second
reader.

Overjet Caries Assist (OCA) is a
radiological, automated,
concurrent-read, computer-assisted
detection (CADe) software intended
to aid in the detection and
segmentation of caries on bitewing
and periapical radiographs. The
device provides additional
information for the dentist to use in
their diagnosis of a tooth surface
suspected of being carious. The
device is not intended as a
replacement for a complete dentist’s
review or their clinical judgment that
takes into account other relevant
information from the image, patient
history, or actual in vivo clinical
assessment.

Utilizes computer vision
neural network algorithms,
developed from
open-source models using
supervised machine
learning techniques

Automated, concurrent-read,
CADe software that utilizes
machine learning

Detection of radiological dental
findings: 5 restorations
(crowns, bridges, implants, root
canals, fillings), 4 pathologies
(caries, margin discrepancy,
calculus, periapical
radiolucency) using bounding
boxes.


Classification Name
Medical Image Analyzer


Predicate Device
Lung-CAD by Imagen Technologies
(K230085)

Scan type
Multi-frame ultrasound images
Digital X-ray images

Identify and mark regions of
interest (ROIs) on lung
Ultrasound

Identify and mark regions of
interest (ROIs) on chest
radiographs and label the box
around the ROI as lung
hyperinflation

Device is intended for use as a
concurrent reading aid for
trained healthcare
professionals qualified in
interpreting lung ultrasounds

Device intended for use as a
concurrent reading aid for
physicians interpreting chest
radiographs

Artificial intelligence, including
non-adaptive machine learning
algorithms trained with clinical
data

Comparison of Technological Characteristics with the Predicate Device


Supervised Deep Learning
including Deep Convolutional
Neural Networks for
Segmentation, Landmark
Detection and Classification

Intended Users
Trained healthcare professionals
Physicians


Lung Finding
Detection Performance Metric

Pleural Effusion
Se = 0.97 (95% CI 0.94 – 0.99)


Lung Finding
Localization Performance Metric

Pleural Effusion
Se = 0.85 (95% CI 0.80 – 0.89)

Table 2. Summary of Lung AI localization performance


883 Santa Cruz Ave, Suite 205
Menlo Park, CA 94025 United States

Applicant Contact Telephone:
(925) 324-8467

611 Gateway Blvd, Ste 120
South San Francisco, CA 94080 United States

Correspondent Contact:
Mr. Jared Seehafer

Correspondent Contact Telephone:
(415) 638-9554

Medical image management and processing system

Classification Name:
System, Image Processing, Radiological

Primary Predicate #: K230854
Predicate Trade Name: SwiftMR
Predicate Manufacturer: AIRS Medical Inc.
Secondary Predicate #: K223623
Predicate Trade Name: SubtleMR
Predicate Manufacturer: Subtle Medical, Inc.

Table 1. Contact Details and Device Name


SwiftMR
(Primary
Predicate
Device)
(K230854)

SubtleMR
(Secondary
Predicate
Device)
(K223623)

The software operates
on DICOM files,
enhances the images,
and sends the
enhanced images to
any desired destination
with an AE Title (e.g.,
PACS, MR device,
workstation, and
more). Enhanced
images coexist with
the original images.

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

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

SubtleHD can send
enhanced images to
any destination as long
as it is associated with
an IP and AE Title.
SubtleMR by default is
intended to send
enhanced images to
PACS, but can be
configured to other
destinations. SwiftMR
can send enhanced
images to PACS or an
MR Device.
Substantially
Equivalent.

Software package
Operates on a virtual
machine

Intended User
Radiologists
Same
Same
Same

Medical facility
(hospitals, clinics,
imaging center, etc.)

SubtleHD and
SubtleMR are PC or
Mac, while SwiftMR is
only PC. Substantially
Equivalent.

The software
processes
DICOM-compliant
image data.

Table 2. Comparison of Technological Characteristics


SubtleHD software
implements an image
enhancement
algorithm using a
convolutional neural
network based filtering.
Original images are
enhanced by running
through a cascade of
filter banks, where
thresholding and
scaling operations are
applied. A single
neural network is
trained for adaptive
noise reduction and
sharpness increase.
The parameters within
the neural network
were obtained through
an image-guided
optimization process.
Additional nonlocal
mean based denoising
and unsharp masking
based sharpening
filters are applied to
the deep learning
processed image.

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

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

Single SRE/DNE
model with filters/
pre/post-processing.

DNE is a model; SRE
is achieved via multiple
filters

SRE is a model, DNE
is a model, with filters/
pre/post-processing

Deep-learning
algorithm and
processing steps for
noise reduction and
sharpness
enhancement.
Substantially
Equivalent.

Combined SRE and
DNE, can turn either
off as an option.

Separate, DNE head,
spine, neck, abdomen,
pelvis, prostate,
breast, and
musculoskeletal, SRE
head only.

Optional high
denoising level and
optional high
sharpening level.

Denoising level from
level 0 to level 8,
Sharpness level from
level 0 to level 5.

SubtleHD and SwiftMR
are substantially
equivalent. SubtleMR
has a single SRE and
DNE option.

Endpoints and
acceptance criteria
using retrospective
clinical images for both
noise reduction and
sharpness increase
functions.

Subtle Medical Inc. - 510(k) – SubtleHD – 510(k) Summary


Test Set
Study Test
Result (mean ± std dev)
T-Test P-Value

Paired Test Set
(Unaligned SOC as
the Reference)

Paired Test Set
(Unaligned
SubtleHD-enhanced
SOC as the
Reference)

Table 3. Standalone Image Quality Metric Summary


SNR shall improve by at least 40% in
homogenous ROI regions for at least 90%
of the dataset.

SubtleHD performs
denoising, in terms of
improved SNR, MRI images.

SNR shall improve by at least 40% in
homogenous ROI regions for at least 95%
of the dataset.

Sharpness
(Image Intensity
Change) Primary
Endpoint

Slope in a line ROI is increased for at least
90% of the dataset.

SubtleHD sharpens, in terms
of improvement in visibility of
the edge at a tissue interface
by image intensity slope
measure, MRI images.

Slope in a line ROI is increased for at least
95% of the dataset.

Sharpness
(Image Intensity
Change for
Brains)
Secondary
Endpoint

Thickness, in terms of FWHM in a line ROI,
is reduced for at least 90% of the dataset.

SubtleHD sharpens, in terms
of improvement in visibility of
an anatomical structure by
image intensity FWHM
measure, MRI images.

Thickness, in terms of FWHM in a line ROI,
is reduced for at least 95% of the dataset.

Sharpness and
Over Smoothing
(Gradient
Entropy) Primary
Endpoint

At least 90% of cases demonstrate a lower
gradient entropy value after SubtleHD
processing.

SubtleHD does not result in
over-smoothed images, in
terms of improvement in
gradient entropy.

At least 95% of cases demonstrate a lower
gradient entropy value after SubtleHD
processing.


There is a statistically significant
improvement in gradient entropy when
comparing the original and SubtleHD
enhanced images across the performance
dataset per a two-sided paired t-test.


Statistically significantly
better with p-value < 0.05
or not statistically
significantly different in a
Wilcoxon signed rank test

SubtleHD performs
denoising, in terms of
improved SNR, for MRI
Images.

Overall Image Quality
/ Diagnostic
Confidence

SubtleHD performs
sharpening and does not
over-smoooth images, in
terms of improved visibility
of small structures, for MRI
images.


SubtleHD-enhanced
images do not contain
artifacts that could impact
diagnosis or b) both input
and SubtleHD-enhanced
images are deemed to
contain artifacts that could
impact diagnosis.

SubtleHD does not introduce
artifacts into MRI images.


FRANCOIS Adeline
VP QARA and Clinical Affairs

Contact Information
Email: adeline@avatarmedical.ai


QIH, automated radiological image processing software

LLZ, system, image processing, radiological


Planning software for Total Shoulder Arthroplasty

Classification Name
System, Image Processing, Radiological

Medical Image Management and Processing System


Subject Device:
brAIn™ Shoulder Positioning

Intended Users
Healthcare Professionals
Healthcare Professionals

Healthcare facilities such as hospitals and clinics

Type of software
Web-based software
Web-based software

Use time
Pre-operatively
Pre-operatively

Imaging used
CT-scan images
CT-scan images

Anatomical and reverse shoulder implants

User profiles define the authorized actions for the
user.

Case management
List of cases
List of cases

Tools
Milling tool
Milling tool (called reaming)

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

Patient last name
Patient first name
Patient sex
Patient birthdate
Patient age
Patient height
Patient weight
Shoulder side
Surgery date

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

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

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


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

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

Manual measurements performed:
Shoulder Measurements, scapula neck length,
posterior subluxation, glenoid reaming depth.

Pre-positioning or Semi-automatic
measurements performed:
Shoulder Measurements, baseplate
Seating, posterior subluxation, glenoid
reaming depth.

Pre-positioning or Semi-automatic initial
placement performed.

Planning report comprising preoperative and planned
parameters.

Planning report comprising patient
information, implant and measurement
pre and Post position, with 8 images to
illustrate the chosen implant
configuration.

Does not include any predictions and
recommendations.


volumetric values (mL) by Brainomix 360 e-ASPECTS. This is summarized in Table 1 below.


Reference Device
Rapid ASPECTS (v3)
(K232156)

Parameter
Brainomix 360 e-ASPECTS
Brainomix 360 e-ASPECTS

Brainomix 360 e-ASPECTS is a
computer-aided diagnosis
(CADx) software device used
to assist the clinician in the
assessment and
characterization of brain
tissue abnormalities using CT
image data.
The software automatically
registers images and uses an
atlas to segment and analyze
ASPECTS regions. Brainomix
360 e-ASPECTS extracts image
data from individual voxels in
the image to provide analysis
and computer analytics and
relates the analysis to the
atlas defined ASPECTS regions.

Brainomix 360 e-ASPECTS is a
computer-aided diagnosis
(CADx) software device used
to assist the clinician in the
assessment and
characterization of brain
tissue abnormalities using CT
image data.
The software automatically
registers images and uses an
Atlas to segment and analyze
ASPECTS regions. Brainomix
360 e-ASPECTS extracts image
data from individual voxels in
the image to provide analysis
and computer analytics and
relates the analysis to the
atlas defined ASPECTS regions.

Rapid ASPECTS is a computer-
aided diagnosis (CADx)
software device used to assist
the clinician in the assessment
and characterization of brain
tissue abnormalities using CT
image data.
The Software automatically
registers images and segments
and analyzes ASPECTS Regions
of Interest (ROls). Rapid
ASPECTS extracts image data
for the ROI(s) to provide
analysis and computer
analytics based on
morphological characteristics.
The imaging features are then
synthesized by an artificial

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