In addition to the user-configured
analysis, standard orthodontic
tracing analysis include: Downs,
Steiner, Jarabek, McNamara,
Ricketts, Eastman, Kim, Wits.

In addition to the user-
configured analysis, standard
orthodontic tracing analysis
include: Downs, Steiner,
Jarabek, McNamara, Ricketts,
Eastman, Kim, Wits.

Paint, draw, magnify, line
drawing, distance measure (px or
mm), 3-point angle,
ruler(calibrate), select region
crop.

Treatment Planning,
Simulation and follow
up

-Translate, tip and rotate incisors,
reposition molars, rotate
mandible and skeletal structures -
Superimpose one or more growth
tracings over original tracing.

-Translate, tip and rotate
incisors, reposition molars,
rotate mandible - Superimpose
one or more growth tracings
over original tracing.

Different -
Has no safety or
security impact on
the product. The
Skeletal structure
difference is
explained and
detailed below.

Supported Area
Dental, Maxilla, Mandible
Dental, Maxilla, Mandible
Same


Document name: 510(k) Summary CEPHX - Cephalometric Analysis Software


and quantifying segmentable brain
structures identified on MR images.
The users are trained healthcare
professionals who work with medical
imaging.
The product is used in an office-like
environment.


Submitted by
Resonance Health Analysis Servic

US Contact (US Agent)
Michael van der Woude

Trade/proprietary Name
HepaFatSmart (V2.0.0)

CFR Section
892.1000 Magnetic Resonance D


892.1000 Magnetic Resonance Diagnostic Device


System, Nuclear
Magnetic Resonance
Imaging, System, Image
Processing Radiological

Device Name
HepaFatSmart
HepaFat-AI
HepaFat-Scan

Standalone software
platform designed to
automatically analyse
within seconds magnetic
resonance imaging (MRI)
datasets using the
method of HepaFat-Scan
with the liver ROI
predicted to generate an
estimate of the patient’s
volumetric liver fat
fraction (VLFF),
converted into proton
density fat fraction
(PDFF) and steatosis
grade. No user input is
required for the analysis
thus minimising the
impact of human error
on obtained results.

Standalone software
platform designed to
automatically analyse
within seconds magnetic
resonance imaging (MRI)
datasets to generate an
estimate of the patient’s
volumetric liver fat fraction
(VLFF), converted into
proton density fat fraction
(PDFF) and steatosis grade.
No user input is required
for the analysis thus
minimising the impact of
human error on obtained
results.

Standalone software
application to facilitate the
import and visualization of
multi-slice, gradient-echo
MRI data sets
encompassing the
abdomen, with
functionality independent
of the MRI equipment, to
provide objective and
reproducible
determination of the
triglyceride fat fraction in
magnetic resonance
images of the liver. It
utilises magnetic
resonance images that
exploit the difference in
resonance frequencies
between hydrogen nuclei
in water and triglyceride
fat. The quantitative
triglyceride fat fraction is
based on the
measurement of a
magnetic resonance
parameter that reflects
the ratio of the proton
density signal of
triglyceride fat to the total
proton density signal in
the liver.


Convolutional neural
networks for the
prediction of the liver
region of interest (ROI).
Algorithmic for the
image quality checking
and calculated Alpha
conversion into VLFF,
PDFF and Steatosis
grade.
Algorithms for the
measurement and
calculation of Alpha
VLFF, PDFF and Steatosis
grade.

Convolutional neural
networks for the image
analysis.
Algorithmic for the image
quality checking and Alpha
conversion into VLFF.

Algorithmic, with human
interaction for Region of
Interest (ROI) selection.

HepaFatSmart is
intended for the
quantitative
measurement of
volumetric liver fat
fraction (VLFF), proton
density fat fraction
(PDFF) and steatosis
grading.
HepaFatSmart is an
application that is used
for the non-invasive
evaluation of liver tissue
by utilising magnetic
resonance images to
evaluate the difference
in resonance frequencies
between hydrogen
nuclei in water and
triglyceride fat. The
quantitative triglyceride
fat fraction is based on
the measurement of a
magnetic resonance
parameter that reflects
the ratio of the proton
density signal of
triglyceride fat to the
total proton density
signal in the liver.

HepaFat-AI is intended for
quantitative measurement
of the triglyceride fat
fraction in magnetic
resonance images of the
liver, also known as
volumetric liver fat fraction
(VLFF).
It utilises magnetic
resonance images that
exploit the difference in
resonance frequencies
between hydrogen nuclei
in water and triglyceride
fat. The quantitative
triglyceride fat fraction is
based on the
measurement of a
magnetic resonance
parameter that reflects the
ratio of the proton density
signal of triglyceride fat to
the total proton density
signal in the liver.
When interpreted by a
trained physician, the
results provide information
that can aid in diagnosis.

HepaFat-Scan is a software
device intended for
quantitative measurement
of the triglyceride fat
fraction in magnetic
resonance images of the
liver. It utilises magnetic
resonance images that
exploit the difference in
resonance frequencies
between hydrogen nuclei
in water and triglyceride
fat. The quantitative
triglyceride fat fraction is
based on the
measurement of a
magnetic resonance
parameter that reflects
the ratio of the proton
density signal of
triglyceride fat to the total
proton density signal in
the liver.
When interpreted by a
trained physician, the
results provide
information that can aid in
diagnosis.

• Support clinical
diagnoses in
individuals with

HepaFat-AI is indicated to:
• Assess the volumetric
liver fat fraction,


confirmed or
suspected fatty liver
disease;
• Support the
subsequent clinical
decision-making
processes for patients
under management
for fatty liver related
disease or metabolic
syndromes;
Aid in the assessment
and screening of living
donors for liver
transplant.
• Results, when
interpreted by a
trained physician can
be used to support
clinical diagnoses
about the status of
liver fat content, the
subsequent clinical
decision-making
processes for the
management of fatty
liver related diseases,
metabolic syndromes,
liver donor screening
and lifestyle change.
HepaFatSmart can be
used to analyse the
MRI images of patients
of all population
independent of age
and gender, with
suspected clinical
conditions related to
the level of liver fat.

proton density fat
fraction and steatosis
grade in individuals
with confirmed or
suspected fatty liver
disease;
• Monitor liver fat
content in patients
undergoing weight loss
management;
Aid in the assessment and
screening of living donors
for liver transplant.

of the triglyceride fat
fraction in magnetic
resonance images of the
liver. It utilises magnetic
resonance images that
exploit the difference in
resonance frequencies
between hydrogen nuclei
in water and triglyceride
fat. The quantitative
triglyceride fat fraction is
based on the
measurement of a
magnetic resonance
parameter that reflects
the ratio of the proton
density signal of
triglyceride fat to the total
proton density signal in
the liver.
When interpreted by a
trained physician, the
results provide
information that can aid in
diagnosis.

Image-type utilized
Magnetic Resonance
Magnetic Resonance
Magnetic Resonance


• Unique Report ID
• Patient ID, patient
name, and date of
birth for full
identification of the
patient.
• Scan date, and
analysis date.
• Referrer and MRI
centre.
• Results displayed:
VLFF (%), PDFF (%)
and Steatosis grade,
associated with
confidence intervals
and normal range.
• Pictures of the 2 TEs
of the analysed slice
and analysis liver ROI
placed on 1 TE.
Liver colour map (for
illustration purpose only,
not for diagnostic)

• Unique Report ID
• Patient ID, patient
name, and date of birth
for full identification of
the patient.
• Scan date, and analysis
date.
• Referrer and MRI
centre.
• Results displayed: VLFF
(%), PDFF (%) and
Steatosis grade,
associated with
confidence intervals
and normal range.
• Pictures of the 3 TEs of
the analysed slice.
Liver colour map (for
illustration purpose only,
not for diagnostic)

• Unique Report ID
• Patient ID, patient
name, and date of
birth for full
identification of the
patient.
• Scan date, and analysis
date.
• Referrer and MRI
centre.
• Results displayed: VLFF
(%) associated with
confidence intervals
and normal range.
• Picture of the analysed
slice.

PDF (encrypted) and
secondary capture
(DICOM)


Figure 1. Plot A of HepaFat-Scan VLFF measured at scan 2 against HepaFat-Scan VLFF measured at scan 1 for the 42 subjects

in the repeatability study. Plot B is for HepaFatSmart (41 subjects). The solid line is the line of equivalence. Note, 41 instead

of 42 subjects were used in the HepaFatSmart related analysis as a single case was identified as a high iron case with the

newly introduced excessive iron assessment algorithm. All the results are closely scattered around the equivalency line,

indicating good performance and substantial equivalence for both the predicate HepaFat-Scan and the device HepaFatSmart,

the same analysis outcome. Briefly, the results are summarised as following:


bias and both repeatability coefficients for the HepaFatSmart are

slightly better than those obtained from the repeated scans of HepaFat-Scan, indicating the

performance of the HepaFatSmart is comparable (no worse) than human for the repeatability data

analysed. This does not suggest yet that the HepaFatSmart is better than human analyst as the original

human analysis (HepaFat-Scan) historically used two small liver ROIs rather than a single large liver

ROI used in the HepaFatSmart with potentially slightly larger sampling error in the original HepaFat-

repeatability coefficients for the HepaFatSmart, indicating the substantial equivalence to the

predicate HepaFat-Scan and a possibility that the results from HepaFatSmart and HepaFat-Scan could

From the Bland Altman analysis shown in Figure 2 for both the reference standard HepaFat-Scan (plot


Figure 3. Linear regression analysis of the validation study (n = 281) by comparing HepaFatSmart with the reference standard

HepaFat-Scan: plot A shows the full VLFF range and plot B shows the VLFF range between 0 – 10%. The solid line is the line

of equivalence. 281 datasets out of 300 passed the IQC rules. All the results are closely scattered around the equivalency

line, indicating good performance and substantial equivalence for both the predicate HepaFat-Scan and the device

size is smaller than 300 as per the IQC module in place):


and both 95% limits for the HepaFatSmart are significantly better than those obtained from the

predicate HepaFat-AI, indicating the much improved and safer performance of the HepaFatSmart,

which is as expected as the analysis outcomes are solely dependent on the liver ROI..

of 0.2% is statistically significant (due to small variability), it is small and unlikely clinically significant.

Both upper and lower 95% limits are small and close to the repeatability coefficients found in the

repeatability study but significantly smaller than the predicate HepaFat-AI, indicating the new device

HepaFatSmart is substantially equivalence to the predicate HepaFat-Scan and better than the


Figure 4. Bland Altman analysis of the validation study (n = 281) of the HepaFatSmart VLFF (plot A) and HepaFat-AI (plot B)

by comparing with the reference standard HepaFat-Scan VLFF. For the HepaFatSmart, the Bias of 0.2% is statistically

significant (due to small variability) but small and unlikely clinically significant. Both upper and lower 95% limits are small and

close to the repeatability coefficients found in the repeatability study. Compared with the predicate HepaFat-AI, significant

improvement is indicated by the small bias and 95% confident limits.


HepaFat-AI v1.2.17 Limits
of Agreement (full
quaran�ne dataset)

HepaFatSmart v2.0.0
Limits of Agreement (full
quaran�ne dataset)


Lunit Inc.
4-8 F, 374, Gangnam-daero, Gangnam-gu,
Seoul, 06241, Republic of Korea
Tel: + 82-70-5066-0849
FAX: +82-2-6919-2702
E-mail: ra_rad@lunit.io

Harry Hyung Tak Han
Regulatory Affairs Specialist
Email: hhan@lunit.io

Suhyoung Bahk
Regulatory Affairs Specialist
Email: sbahk@lunit.io


Radiological Computer Assisted Detection/Diagnosis Software For Suspicious
Lesions For Cancer


Radiological Computer Assisted
Detection/Diagnosis Software For Suspicious
Lesions For Cancer

Lunit INSIGHT DBT is a computer-assisted
detection and diagnosis (CADe/x) software
intended to be used concurrently by
interpreting physicians to aid in the detection
and characterization of suspected lesions for
breast cancer in digital breast tomosynthesis
(DBT) exams from compatible DBT systems.
Through the analysis, the regions of soft
tissue lesions and calcifications are marked
with an abnormality score indicating the
likelihood of the presence of malignancy for
each lesion. Lunit INSIGHT DBT uses screening
mammograms of the female population.
Lunit INSIGHT DBT is not intended as a
replacement for a complete interpreting
physician’s review or their clinical judgment
that takes into account other relevant
information from the image or patient
history.

Lunit INSIGHT MMG is a radiological
Computer-Assisted Detection and Diagnosis
(CADe/x) software device based on an
artificial intelligence algorithm intended to aid
in the detection, localization, and
characterization of suspicious areas for breast
cancer on mammograms from compatible
FFDM systems. As an adjunctive tool, the
device is intended to be viewed by
interpreting physicians after completing their
initial read. It is not intended as a
replacement for a complete physician’s
review or their clinical judgement that takes
into account other relevant information from
the image or patient history. The Lunit
INSIGHT MMG uses screening mammograms
of the female population.

Target patient
Women undergoing mammography
Women undergoing mammography

Physicians interpreting screening
mammograms

Lunit INSIGHT DBT is powered by artificial
intelligence/machine learning-based software
algorithm

Lunit INSIGHT MMG is powered by artificial
intelligence/machine learning-based software
algorithm


Predicate Device
FDA Clearance Number and
Product
Manufacturer

Reference Device
FDA Clearance Number and
Product
Manufacturer


Subject Devices
Predicate Device
Reference Devices*

MAGNETOM Aera,
MAGNETOM Vida with
MAGNETOM Sola with

MAGNETOM Skyra,
syngo MR XA50A
syngo MR XA51A

Summary hardware comparison table for the subject and predicate/reference device


Software
MAGNETOM Vida with software
MAGNETOM Vida with syngo

Summary software comparison table for the subject and predicate devices


New features as listed in the Device
Yes

Spectroscopy pulse sequence types
Yes
Yes

User interface and user interaction
Yes
Yes

Workflow and software utilization
Yes
Yes

Reconstruction
as listed in the Device Description


Performance Test
Source/Rationale for test

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

Guidance for submission of Premarket
Notifications for Magnetic Resonance
Diagnostic Devices

Image quality
assessment by sample
clinical images

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

The following performance testing was conducted on the subject devices:


Performance Test
Tested Hardware or Software
Source/Rationale for test

- SNR and image uniformity
measurements for coils
- Heating measurements for coils

Guidance for Submission of
Premarket Notifications for
Magnetic Resonance Diagnostic
Devices

reference devices and can be reused for the subject devices:


• TSE: more than 25,000 slices
• HASTE: pre-trained on the TSE dataset
and refined with more than 10,000
HASTE slices
• EPI Diffusion: more than 1,000,000
slices
The data covered a broad range of body
parts, contrasts, fat suppression techniques,
orientations, and field strength.

on more than 10,000 high resolution 2D
images.
The data covered a broad range of body
parts, contrasts, fat suppression techniques,
orientations, and field strength.

Test Statistics and
Test Results
Summary

The impact of the network has been
characterized by several quality metrics
such as peak signal-to-noise ratio (PSNR)
and structural similarity index (SSIM). Most
importantly, the performance was
evaluated by visual comparisons to
evaluate e.g., aliasing artifacts, image
sharpness and denoising levels.

The impact of the network has been
characterized by several quality metrics
such as peak signal-to-noise ratio (PSNR),
structural similarity index (SSIM), and
perceptual loss. In addition, the feature has
been verified and validated by inhouse
tests. These tests include visual rating and
an evaluation of image sharpness by
intensity profile comparisons of


reconstructions with and without Deep
Resolve Sharp.

No clinical subgroups have been defined for the collected dataset.

Due to reasons of data privacy, we did not record gender, age and ethnicity during data
collection.

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

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


[1] Bae SH et al., Clinical feasibility of accelerated diffusion
weighted imaging of the abdomen with deep learning
reconstruction: Comparison with conventional diffusion
weighted imaging, Eur J Radiol., 154 (2022)
[2] Lee EJ et al., Feasibility of deep learning k-space-to-image
reconstruction for diffusion weighted imaging in patients
with breast cancers: Focus on image quality and reduced scan
time, Eur J Radiol., 157 (2022)
[3] Afat S et al., Acquisition time reduction of diffusion-
weighted liver imaging using deep learning image
reconstruction. Diagn Interv Imaging, (2022).
[4] Benkert T et al., Improved Clinical Diffusion Weighted
Imaging by Combining Deep Learning Reconstruction, Partial
Fourier, and Super Resolution, ISMRM (2022)
[5] Kim et al., Deep Learning–Accelerated Liver Diffusion-
Weighted Imaging - Intraindividual Comparison and
Additional Phantom Study of Free-Breathing and Respiratory-
Triggering Acquisitions, Invest Radiol (2023)

of the following features and functions:


[1] Herrmann J et al., Diagnostic Confidence and
Feasibility of a Deep Learning Accelerated HASTE Sequence of
the Abdomen in a Single Breath-Hold, Investigative
Radiology, Volume 56, Number 5, May 2021.
[2] Shanbhogue K et al. Accelerated single-shot T2-
weighted fat-suppressed (FS) MRI of the liver with deep
learning-based image reconstruction: qualitative and
quantitative comparison of image quality with conventional
T2-weighted FS sequence. Eur Radiol. 2021 May 7.
[3] Herrmann J et al., Development and Evaluation of
Deep Learning-Accelerated Single-Breath-Hold Abdominal
HASTE at 3 T Using Variable Refocusing Flip Angles. Invest
Radiol. 2021 Apr 22.
[4] Han S et al., Evaluation of HASTE T2 weighted image
with reduced echo time for detecting focal liver lesions in
patients at risk of developing hepatocellular carcinoma. Eur J
Radiol. 2022 Nov 1;157:110588.
[5] Mule S et al., Fast T2-weighted liver MRI: Image
quality and solid focal lesions conspicuity using a deep
learning accelerated single breath-hold HASTE fat-
suppressed sequence. Diagn Interv Imaging. 2022
Oct;103(10):479-485.
[6] Ginocchio LA et al., Accelerated T2-weighted MRI of
the liver at 3 T using a single-shot technique with deep
learning-based image reconstruction: impact on the image
quality and lesion detection. Abdom Radiol (NY). 2022 Sep 28.
[7] Herrmann J et al., Comprehensive clinical evaluation
of a deep learning-accelerated, single-breath-hold abdominal
HASTE at 1.5 T and 3 T. Acad Radiol. 2022 Apr 22:S1076-
6332(22)00195-7.
[8] Ichinohe F. et al., Usefulness of Breath-Hold Fat-
Suppressed T2-Weighted Images With Deep Learning–Based
Reconstruction of the Liver, Invest Radiol., 2022

[1] Guenthner C. et al. Ristretto MRE: A generalized multi-
shot GRE-MRE sequence. NMR Biomed 2019; 32:e4049.


Device Name
StrokeViewer Perfusion
iSchemaView Rapid
StrokeViewer HALO

Radiological Computer Aided
Triage and Notification Software

Classification Panel
Radiology
Radiology
Radiology


Medical devices – Applications of risk
management to medical devices

Medical devices Part 1: Application of
usability engineering to medical
devices, including Amendment 1

Medical device software – Software
lifecycle processes (including
Amendment 1 (2016))

Digital Imaging and Communications in
Medicine (DICOM) Set

StrokeViewer to uphold claims of safety and effectiveness of the intended purpose of the device:


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

20417 First edition
2021-04 Corrected
version 2021-12

Medical devices – Information to be
supplied by the manufacturer

Health Software – Part 1: General
requirements for product safety


Standard Designation Number
Title of Standard

Medical Devices—Quality Management Systems—Requirements for
Regulatory Purposes

Information Security, Cybersecurity and privacy protection—Information
security management systems—Requirements

Health informatics—Information security management in healthcare—
Part 1: Management System

Health informatics—Information security management in healthcare—
Part 2: Controls


Predicate Device
FDA Clearance Number and
Product
Manufacturer

Reference Device
FDA Clearance Number and
Product
Manufacturer

Siemens Shenzhen
Magnetic Resonance Ltd.


Hardware
Subject Devices
Predicate Device
Reference Devices

MAGNETOM Cima.X
MAGNETOM Vida with
MAGNETOM Sola with

with software syngo MR
syngo MR XA50A
syngo MR XA51A

New or modified features as
listed in the Device
Description above

Summary hardware comparison table for the subject and predicate device


Software
Subject Device
Predicate Device

MAGNETOM Cima.X with software
MAGNETOM Vida with syngo

SE-based pulse sequence types
New feature as listed in the Device
Yes

GRE-based/Steady-State pulse
New or modified pulse sequences
Yes

EPI-based pulse sequence types
New features as listed in the Device
Yes

Spectroscopy pulse sequence types
Yes
Yes

Other features and
Modified features and
Yes

User interface and user interaction
Yes
Yes

Viewing and post-processing
New or modified viewing and post-
Yes

Workflow and software utilization
Yes
Yes

Scanning
Modified and new features and
Yes


Performance Test
Source/Rationale for test

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

Guidance for submission of Premarket
Notifications for Magnetic Resonance
Diagnostic Devices

Image quality
assessment by sample
clinical images

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

Performance bench test
new and modified hardware

Electrical, mechanical,
structural, and related
system safety test

Electrical safety and
electromagnetic
compatibility (EMC)


Performance Test
Tested Hardware or Software
Source/Rationale for test

- SNR and image uniformity
measurements for coils
- Heating measurements for coils

Guidance for Submission of
Premarket Notifications for
Magnetic Resonance Diagnostic
Devices

reference devices and can be reused for the subject device:


 TSE: more than 25,000 slices
 HASTE: pre-trained on the
TSE dataset and refined with
more than 10,000 HASTE
slices
 EPI Diffusion: more than
1,000,000 slices
The data covered a broad range
of body parts, contrasts, fat
suppression techniques,
orientations, and field strength.

on more than 10,000 high
resolution 2D images.
The data covered a broad range
of body parts, contrasts, fat
suppression techniques,
orientations, and field strength.

Test Statistics and Test Results
Summary

The impact of the network has
been characterized by several
quality metrics such as peak
signal-to-noise ratio (PSNR) and
structural similarity index (SSIM).
Most importantly, the
performance was evaluated by
visual comparisons to evaluate
e.g., aliasing artifacts, image
sharpness and denoising levels.

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

No clinical subgroups have been defined for the collected dataset.

Due to reasons of data privacy, we did not record gender, age and
ethnicity during data collection.

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

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


[14_1] Bae SH et al., Clinical feasibility of accelerated
diffusion weighted imaging of the abdomen with deep
learning reconstruction: Comparison with conventional
diffusion weighted imaging, Eur J Radiol., 154 (2022)
[14_2] Lee EJ et al., Feasibility of deep learning k-
space-to-image reconstruction for diffusion weighted
imaging in patients with breast cancers: Focus on
image quality and reduced scan time, Eur J Radiol., 157
(2022)
[14_3] Afat S et al., Acquisition time reduction of
diffusion-weighted liver imaging using deep learning
image reconstruction. Diagn Interv Imaging, (2022).
[14_4] Benkert T et al., Improved Clinical Diffusion
Weighted Imaging by Combining Deep Learning
Reconstruction, Partial Fourier, and Super Resolution,
ISMRM (2022)

[14_6] Herrmann J et al., Diagnostic Confidence and
Feasibility of a Deep Learning Accelerated HASTE
Sequence of the Abdomen in a Single Breath-Hold,
Investigative Radiology, Volume 56, Number 5, May
2021.
[14_7] Shanbhogue K et al. Accelerated single-shot T2-
weighted fat-suppressed (FS) MRI of the liver with
deep learning-based image reconstruction: qualitative
and quantitative comparison of image quality with
conventional T2-weighted FS sequence. Eur Radiol.
2021 May 7.
[14_8] Herrmann J et al., Development and Evaluation
of Deep Learning-Accelerated Single-Breath-Hold
Abdominal HASTE at 3 T Using Variable Refocusing Flip
Angles. Invest Radiol. 2021 Apr 22.
[14_9] Han S et al., Evaluation of HASTE T2 weighted
image with reduced echo time for detecting focal liver
lesions in patients at risk of developing hepatocellular
carcinoma. Eur J Radiol. 2022 Nov 1;157:110588.
[14_10] Mule S et al., Fast T2-weighted liver MRI:
Image quality and solid focal lesions conspicuity using
a deep learning accelerated single breath-hold HASTE
fat-suppressed sequence. Diagn Interv Imaging. 2022
Oct;103(10):479-485.
[14_11] Ginocchio LA et al., Accelerated T2-weighted
MRI of the liver at 3 T using a single-shot technique
with deep learning-based image reconstruction:


impact on the image quality and lesion detection.
Abdom Radiol (NY). 2022 Sep 28.
[14_12] Herrmann J et al., Comprehensive clinical
evaluation of a deep learning-accelerated, single-
breath-hold abdominal HASTE at 1.5 T and 3 T. Acad
Radiol. 2022 Apr 22:S1076-6332(22)00195-7.
[14_13] Ichinohe F. et al., Usefulness of Breath-Hold
Fat-Suppressed T2-Weighted Images With Deep
Learning–Based Reconstruction of the Liver, Invest
Radiol., 2022

[14_14] Guenthner C. et al. Ristretto MRE: A
generalized multi-shot GRE-MRE sequence. NMR
Biomed 2019; 32:e4049.

[14_24] Hoge WS, Polimeni JR. Dual-polarity GRAPPA
for simultaneous reconstruction and ghost correction of
echo planar imaging data. Magn Reson Med. 2016
Jul;76(1):32-44. doi: 10.1002/mrm.25839. Epub 2015

[14_5] Polimeni JR, Bhat H, Witzel T, Benner T, Feiweier
T, Inati SJ, Renvall V, Heberlein K, Wald LL. Reducing
sensitivity losses due to respiration and motion in
accelerated echo planar imaging by reordering the
autocalibration data acquisition. Magn Reson Med.
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2015 Mar 23. PMID: 25809559; PMCID: PMC4580494.

[14_25] D. Polak, D N. Splitthoff, etc. Scout accelerated
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Neuroradiology 2023 Jan.

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Classification Name:
Magnetic Res
onance Diagnostic
Device


Predicate Device
FDA Clearance Number
Product
Manufacturer

Siemens Shenzhen
Magnetic Resonance Ltd.

Table 2. Predicate devices and reference devices


Hardware
Free.Max with
Free.Star with
Free.Max with
Free.Star with

software syngo MR
software syngo MR
Error! Reference
Error! Reference

RF System
Yes, same as predicate device
Yes


Patient Table
Yes, same as predicate device
Yes

Yes, modified compared to predicate
device:
-new host computer hardware
-new MaRS hardware

Yes,
new coil compared to predicate device:
Contour Knee coil

Yes,
Modified compared to predicate device:
-myExam 3D Camera
New compared to predicate device:
-Respiratory Sensor


Software
Free.Max with
Free.Star with
Free.Max with
Free.Star with

software syngo MR
software syngo
software syngo
syngo MR XA50A

Yes,
new feature migrated from reference device
MAGNETOM Sola with syngo MR XA51A
(K221733)

Yes,
new feature migrated from reference device
MAGNETOM Amira with syngo MR XA50M
(K223343)

Yes,
new feature migrated from reference device
MAGNETOM Vida with syngo MR XA50A
(K213693)


Yes,
new feature migrated
from reference device
MAGNETOM Sola with
syngo MR XA51A
(K221733)

Yes, modified compared to predicate
device


Performance Test
Tested Hardware or Software
Source/Rationale for test

New and modified software
features, pulse sequence types

Guidance for Submission of
Premarket Notifications for
Magnetic Resonance
Diagnostic Devices

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

The following performance testing was conducted on the subject devices.


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

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

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

Medical devices - Application of
risk management to medical
devices

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

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

Acoustic Noise Measurement
Procedure for Diagnosing
Magnetic Resonance Imaging
Devices

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

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

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


Primary Predicate
Device HealthCCSng
(K210085)

BriefCase-Quantification
for Coronary Artery
Calcification (CAC)

The HealthCCSng device
is intended for use as a
non-invasive post-
processing software to
evaluate calcified plaques
in the coronary arteries,
which present a risk for
coronary artery disease.
The software generates an
estimated coronary artery
calcium detection
category. The
HealthCCSng device
analyzes existing non-
cardiac-gated CT studies
that include the heart of
adult patients above the
age of 30. The device
generates a three-
category output
representing the estimated
quantity of calcium
detected together with
preview axial images of the
detected calcium meant for
informational purposes
only. The device output will
be available to the
radiologist as part of their
standard workflow. The
HealthCCSng results are
not intended to be used on
a stand-alone basis for risk
attribution, clinical
decision-making or
otherwise preclude clinical
assessment of CT studies.

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.
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

BriefCase-Quantification is a
software intended for use in
the analysis of non-cardiac-
gated non-contrast CT
(NCCT) images that include
the heart in adult patients
aged 30 and older.
The device is intended to
assist physicians by
providing the user with a four-
category Coronary Artery
Calcification (CAC) of
plaques, which present a risk
for coronary artery disease,
together with preview axial
images of the detected
calcium meant for
informational purposes only.
The BriefCase-Quantification
results are not intended to be
used on a stand-alone basis
for clinical decision-making or
otherwise preclude clinical
assessment of cases.
Clinicians are responsible for
viewing full images per the
standard of care.


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.

Hospital networks and
appropriately trained medical
specialists

Non-cardiac-gated CT
studies that include the
heart

Non-cardiac-gated non-
contrast CT images that
include the heart


Artificial intelligence
algorithm with database of
images.

Slice Thickness
Up to 3.0 mm
Up to 5.0 mm
Up to 5.0 mm

Patient population
Patients aged 30 and older
Patients aged 30 and older
Patients aged 30 and older

Yes, Coronary Calcium
Detection Category.
3 categories:
● 0-99
● 10-399
● >400

Yes, Coronary Calcium
Detection Category and
exact Agatston score.
4 categories (for detection
category):
● 0
● 1-99
● 100-399
● >_400

Yes, Coronary Calcium
Detection Category: very low,
low, medium, and high.
● The categories
composing the output
of the device
correspond with a
validated visual
assessment
categorization of
none, mild, moderate,
and severe in
agreement with
categorized Agatston
scores indicated in
the literature (very
low: 0; low: 1-100;
medium: 101-400;
high: ≥400).

- HealthCCSng is an
algorithm module
that receives a
non-cardiac-gated
CT study from the

- The iCAC Device
takes as an input
non-contrast, non-
gated chest CT
scans via DICOM

- BriefCase-
Quantification, is
hosted on a cloud
server, analyzes
applicable CT images


Primary Predicate
Device HealthCCSng
(K210085)

BriefCase-Quantification
for Coronary Artery
Calcification (CAC)

storage
application,
Zebra’s Imaging
Analytics Platform
(IMA).
- For each CT study
received, the
software shall
validate there is at
least one compliant
series in which the
entire heart is
present, and
perform an
analysis.

transfer from the
clinicians imaging
database such as
PACS or DICOM
router.
- The device uses a
deep learning-
based computer
vision algorithm for
its assessment.

that are acquired on
CT scanner that are
forwarded to
BriefCase-
Quantification
- The results of the
analysis are exported
in DICOM format, and
are sent to a PACS
destination for review
by medical
specialists, to assist
in the evaluation of
CAC.


Table 3. Frequency Distribution of Gender


Manufacturer
Very Low
Low
Medium
High
All

Table 4. Frequency Distribution of Manufacturer


are not intended to be used on a stand-alone basis for clinical decision making or otherwise preclude

packages with similar technological characteristics and principles of operation, incorporating deep


• Addition of a tracer option. With Veuron-Brain-pAb3, users can select the tracer for beta amyloid.

By providing an option (reference region) for tracer, helps users identify appropriate brain regions


Subject device
Predicate device (K213801)

Manufacturer
Heuron Co., Ltd.
Heuron Co., Ltd.

The Veuron-Brain-pAb3 is software for
the registration, fusion, display, and
analysis of medical images from multiple
modalities including MRI and PET. The
software aids clinicians in the
assessment and quantification of
pathologies from PET Amyloid scans of
the human brain. It enables anatomic
analysis and visualization of amyloid
protein concentration through the
calculation of standard uptake volume
ratio (SUVR) within target regions of
interest and comparison to those within
the reference regions. The software is
deployed via medical imaging
workplaces and is organized as a series
of workflows which are specific to use
with radio-tracer and disease
combinations.

The Veuron-Brain-pAb2 is software
for the registration, fusion, display,
and analysis of medical images from
multiple modalities including MRI
and PET. The software aids
clinicians in the assessment and
quantification of pathologies from
PET Amyloid scans of the human
brain. It enables automatic analysis
and visualization of amyloid protein
concentration through the calculation
of standard uptake volume ratio
(SUVR) within target regions of
interest and comparison to those
within the reference regions. The
software is deployed via medical
imaging workplaces and is organized
as a series of workflows which are
specific to use with radiotracer and
disease combinations.

Import DICOM data
Perform automatic post-processing
Provide the user confirmation
Export the resulting data through a
network or USB

Import DICOM data.
Perform automatic post-processing.
Provide the user confirmation
Export the resulting data through a
network or USB

Physical characteristics
Software package
Software package

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


Operates on off-the-shelf hardware
(multiple vendors)

Server: Linux (Ubuntu 18.04 LTS or
higher)
Client: Windows 10 or higher

Server: Ubuntu 16.04 LTS or higher
Client: Windows 10, 64-bit

Tested in accordance with verification
and validation process and planning. The
testing results support that all the system
requirements have met their acceptance
criteria and are adequate for its intended
use.

Tested in accordance with
verification and validation processes
and planning. The testing results
support that all the system
requirements have met their
acceptance criteria and are
adequate for its intended use.

Compatible input data format and
modality

Input patient data
Manual through keyboard/mouse
Manual through keyboard/mouse

Yes, can select the tracer for beta amyloid
Tracer list
 18F-Flutemetamol (FMM)
 18F-Florbetapir (FBP)
 18F-Florbetaben (FBB)

No, cannot select the tracer for beta
amyloid

 Calculate the volume by using a
Convolutional Neural Network
(CNN) model.
 CNN model has trained 3D
brain MR images were collected
from one domestic institution.


DHT8B: Division of Radiological Imaging Devices and

Office of Product Evaluation and Quality


Submitter Information [21 CFR 807.929(a)(1)]

2121 Sage Road, Suite 300
Houston, Texas 77056

Pending 510(k) clearance and marketing of device

Name of the device [21 CFR 807.92(a)(2)]

Medical Image Management and Processing System

Legally marketed device to which
equivalence is claimed
[21 CFR 807.92(a)(3)]

Device description
[21 CFR 807.92(a)(4)]

Spine CAMP™ is a fully-automated image processing software
device. It is designed to be used with X-ray images and is
intended to aid medical professionals in the measurement and
assessment of spinal parameters. Spine CAMP™ is capable of
calculating distances, angles, linear displacements, angular
displacements, and mathematical combinations of these metrics
to characterize the morphology, alignment, and motion of the
spine. These analysis results are presented in the form of
reports, annotated images, and visualizations of intervertebral
motion to support their interpretation.

Indications for use
[21 CFR 807.92(a)(5)]

Spine CAMP™ is a fully-automated software that analyzes X-
ray images of the spine to produce reports that contain static
and/or motion metrics. Spine CAMP™ can be used to obtain
metrics from sagittal plane radiographs of the lumbar and/or
cervical spine and it can be used to visualize intervertebral
motion via an image registration method referred to as
“stabilization.” The radiographic metrics can be used to
characterize and assess spinal health in accordance with
established guidance. For example, common clinical uses
include assessing spinal stability, alignment, degeneration,
fusion, motion preservation, and implant performance. The
metrics produced by Spine CAMP™ are intended to be used to
support qualified and licensed professional healthcare
practitioners in clinical decision-making for skeletally mature
patients of age 18 and above.


Summary of the technological characteristics of the device compared to the predicate device
[21 CFR 807.92(a)(6)]

Spine CAMP™ v1.0
(Predicate Device K221632)

Automated Radiological Image
Processing Software

Vertebral body detection;
Vertebral body landmark
specification; Vertebral body
registration

Display of Image Alignment /
Stabilization

Intended User
Trained professionals
Trained professionals

Spine CAMP™ v1.1 is designed to utilize the same analysis methodology as the predicate device,
Spine CAMP™ v1.0 (K221632). The types of inputs and outputs are identical between the two
devices. The devices are nearly identical in all respects. The primary differences are:
 Spine CAMP’s primary component, the AI Engine, was updated by retraining its AI models with
more imaging for improved generalization and performance. Improvements were also made to the
AI Engine’s logic to address potential failure modes.
 Spine CAMP™ v1.1 is able to identify the femoral heads in lateral lumbar X-rays in order to
produce spinopelvic measurements.
 Configuration capabilities were expanded to derive outputs from the existing calculated results
and to conditionally format report outputs according to the clinical user’s preferences.

Summary of bench tests (non-clinical) conducted for determination of substantial equivalence
[21 CFR 807.92(b)(1)]

Software verification and validation testing was completed to demonstrate functionality of the device
across multiple datasets that had not been used to train any of the AI models. The same methodology
was utilized for the performance qualification (PQ) tests for both Spine CAMP 1.1 and its primary
component, the AI Engine v3.2, as had been previously utilized for the predicate device, Spine CAMP
v1.0 and its primary component, the AI Engine v3.1. The software functioned as intended and all results
observed were as expected.
Additional bench testing was performed by evaluating Spine CAMP™ v1.1 performance on a large
dataset that was previously analyzed by Spine CAMP™ v1.0. Additionally, this dataset was analyzed
by five experienced operators using the reference device, QMA, for spinopelvic measurements that
Spine CAMP™ v1.0 was not designed to produce. This dataset included 215 lateral cervical spine


Subject Device
Primary Predicate Device
Reference Device

Manufacturer
Edgecare Inc.
Verathon Inc.
Butterfly Network, Inc.

Trade Name
EdgeFlow UH10
BladderScan® PRIME PLUS system
Auto 3D Bladder Volume Tool

Product Code
IYO, ITX, QIH
IYO, ITX
IYO, ITX

The EdgeFlow UH10 is an ultrasound
device intended to be used for
measuring the urine volume in the
bladder noninvasively. It is intended
for use in professional healthcare
facilities, such as hospitals, clinics, by
qualified and trained healthcare
professionals. The EdgeFlow UH10
supports B-mode and harmonic
imaging modes.

The BladderScan® Prime PLUS
System is an ultrasound device
intended to be used for measuring the
urine volume in the bladder
noninvasively.

The Butterfly Auto 3D Bladder
Volume Tool is a software application
package. It is designed to view,
quantify and report results acquired on
Butterfly Network ultrasound systems
for noninvasive volume measurements
of the bladder, to support physician
diagnosis. Indicated for use in adult
populations

It is contraindicated for fetal use and
for use on pregnant patients. And it
should not be used by those who are
allergic to coupling agent and who
have abdomen wound and skin disease.

The BladderScan® Prime PLUS
System is not intended for fetal use or
for use on pregnant patients, patients
with ascites, or patients with open skin
or wounds in the suprapubic region.

The Auto 3D Bladder Volume Tool is
not intended for fetal or pediatric use
or for use on pregnant patients,
patients with ascites, or patients with
open skin or wounds in the suprapubic
region.

User
Physicians/Medical Professionals
Physicians/Medical Professionals
Physicians/Medical Professionals


Technology
Neural network technology
Neural network technology
Neural network technology

Sterility Non-sterile Non-sterile Non-sterile

Energy Delivered
Ultrasound
Ultrasound
Ultrasound

Measurement Range
0 to 999 mL
0 to 999 mL
0 to 740 mL

Automatically Calculating Function
Yes
Yes
Yes

Electronic Sector Scanning
(Phased Array)

Sector Angle
120 degrees
120 degrees
100 degrees

Calibration
No Calibration recommended
No Calibration recommended
No Calibration recommended

Data Connections
USB, Wireless (to PC)
USB, SD card
Wireless ( to Mobile App, Cloud)

Cradle
Power Adapter
USB Cable
Thermal Paper

Printer, battery
battery charger
power cord
mobile cart

FDA Ultrasound Track
Track 3
Track 1
Track 3


General requirement for basic safety and
essential performance

General requirement for safety –
Electromagnetic disturbances


1
Wireless Coexistence
• ANSI IEEE C63.27-2017

2
Wi-Fi Performance Test
• In house hold

performed in accordance with following standards.


General requirement for safety –
Programmable electrical medical systems
(PEMS)

• IEC 62304:2006/A1:2015
• FDA Guidance (“Guidance for the
Content of Premarket Submissions for
Software Contained in Medical
Devices”)
• FDA Guidance (“Off-the-Shelf Software
Use in Medical Devices”)

• AAMI/UL 29001-:2017
• IEC 81001-5-1:2021
• FDA Guidance (“Cybersecurity in
Medical Devices: Quality System
Considerations and Content of Premarket
Submissions”)


3
Deep Neural Network Performance
Manufacturing SOP


Demographics of the clinical trial (Total number of subjects: 90)


Yes
(manually, semi-automated and
fully automated
some segmentations are
automated using the new
software module “inHEART
Models AI”)

macOS for imaging processing
software module
Windows or macOS for web-
based platform and compatible
with following browser: Google
Chrome, Apple Safari (for
macOS), Microsoft Edge (for
Windows)

macOS for imaging
processing software module
Windows or macOS for
web-based platform and
compatible with following
browser: Google Chrome,
Apple Safari (for macOS),
Microsoft Edge (for
Windows)

A standard internet connection is
needed for the web-based
platform.

DICOM compliant medical
images acquired from a variety of
imaging devices including, CT,
MR

Table 1 Device Features and Technical Characteristic comparison matrix


DICE
ASSD
Volume diff. (mL)
Volume diff. (%)

Median Mean StdDev Median Mean StdDev Median Mean StdDev Median Mean StdDev


Parameter
Subject Device
Predicate Device

The iCAS -LV is intended to receive multi-
phase volume datasets of reconstructed
studies from PACS devices, to process
them and to transfer the processing output
to the PACS in DICOM format. It is a PC-
based, self-contained, noninvasive image
analysis software application. The device
provides tools for visualization,
measurements, segmentation, annotation,
images registration, processing, and
reporting. The device is intended for use by
trained physicians.

Indications for Use (intended use): The IQQA-
Liver is a PC-based, self-contained,
noninvasive image analysis software application
for reviewing serial multi-phase CT acquisitions
of the liver. Combining image viewing,
processing and reporting tools, the software is
designed to support physicians in the
visualization, evaluation and reporting of liver
and physician-identified liver lesions. The
software supports a workflow based on
automated image registration for viewing and
analyzing multi-phase volume datasets. It also
includes tools for interactive segmentation and
labeling of liver segments and vascular
structures. The software provides functionalities
for manual or interactive segmentation of
physician-identified lesions, and allows for
regional volumetric analysis of such lesions in
terms of size, shape, position and enhancement
pattern, providing information for physician's
assessment of lesion characterization. The
software is designed for use by trained
physicians. Image source: DICOM

Further, the iCAS-LV is indicated to support
the physicians in visualization of CT
reconstructed images and evaluation of
physician-identified liver lesions. The
combination of the visualization, interactive
segmentation, measurements, automatic
registration, and volumetric analysis,
supports the physician in evaluation of the
lesions in terms of size, shape, position and
changes over time. The iCAS should not be
used in isolation for diagnosis and making
patient management decisions.

PC-based self-contained post processing
SW package

control procedures. A comparison table is presented below.


Data inputs
CT reconstructed images in DICOM format
CT reconstructed images in DICOM format

The HighRAD uniquely designed SW,
supports a workflow, which is based on
automated image registration, manual or
interactive segmentation of physician-
identified liver lesions, for viewing and
analysing multi-phase volume datasets, It
also includes tools for Labeling and
reporting.

Similar SW functions performed by EDDA
unique design.

A graphical user interface for users to
interact with the software, to visualize CT
data, to select tools and to drive the
workflow


Device Name
Ez3D-i v5.5(E3)
Ez3D-i v5.4(E3)

Ez3D-i is dental imaging software that is
intended to provide diagnostic tools for
maxillofacial radiographic imaging. These
tools are available to view and interpret a
series of DICOM compliant dental radiology
images and are meant to be used by trained
medical professionals such as radiologist
and dentist.
Ez3D-i is intended for use as software to
load, view and save DICOM images from
CT, panorama, cephalometric and intraoral
imaging equipment and to provide 3D
visualization, 2D analysis, in various MPR
(Multi-Planar Reconstruction.) functions

Platform
IBM-compatible PC or PC network
IBM-compatible PC or PC network

Operating System
Microsoft Window 10 or higher
Microsoft Window 10 or higher

User Interface
Mouse, Keyboard
Mouse, Keyboard

Images can be scanned, loaded from digital
cameras or card readers, or imported from a
radiographic imaging device

Length, Multi Length, Angle, Multi Angle,
Circle, ROI/Area, Volume, Profile

Length, Multi Length, Angle, ROI/Area,
Volume, Profile

Image viewing
Full, side by side, gallery, thumbnail
Full, side by side, gallery, thumbnail

Grayscale, invert, emboss, brightness,
contrast, gamma, sharpen, median,
despeckle, hue, saturation, equalize, flip,
mirror, masking, rotate, magnify, annotation,
cephalometric tracing, ceph growth
projections, implant simulations

Ez3D-i can view, transfer and process 3D
radiographs. Furthermore, it supports Smart
Click, Smart Clipping, Implant Simulation
and Canal Draw.

Text, paint, ellipse, pointer, select, draw,
magnify, line, rectangle, polygon, ruler,
protractor, smile library, smudge, brush,
redeye reduction, select region, copy / paste

8.1. Comparison chart of features and specifications


HIGH-FREQUENCY LINEAR ARRAY TRANSDUCER
18L5s

I-SHAPED INTRAOPERATIVE TRANSDUCER I14C5I

T-SHAPED INTRAOPERATIVE TRANSDUCER I14C5T

BIPLANE INTRAOPERATIVE TRANSDUCER I12C5b

CURVED ARRAY CRANIOTOMY TRANSDUCER N13C5

Table 2: Transducers used with Ultrasound System 1300


Manufacturer
BK Medical ApS
BK Medical ApS
Same

Common Name
Ultrasound System
Ultrasound System
Same

Added:
- bkSpecto 1300-25- a
model without battery.
- Prostate Volume Assist
(PVA), AI software
feature.

B, M, PW, CFM, P, THI,
SE, CW
Combination modes:
2D+M, 2D+PW,
2D+C+PW, 2D+P+PW,
2D+2D, 2D+2D (Biplane
Imaging), 2D+(2D+C),
2D+(2D+P), 2D+THI,
2D+SE
• 2D (B-Mode)
(including Tissue
Harmonic imaging)
• M-Mode
• Vector Flow Imaging
(VFI)

Same – primary predicate
Equivalent – ref. predicate

Table 3: Substantial Equivalence Table of the proposed device with its predicate devices


• Strain Elastography
• CW Doppler
• Contrast Imaging
• PWD Mode
• • CFM Mode
(Includes Color
Doppler and
Amplitude (Power)
Doppler)

Intended Use:
The system is a
diagnostic ultrasound
imaging system used by
qualified and trained
healthcare professionals
for ultrasound imaging,
human body fluid flow
analysis and puncture and
biopsy guidance.
Environment:
The Ultrasound System
1300 is intended for use
in the professional
healthcare environment
(e.g., hospitals, physician
offices).
Contraindications:
The Ultrasound System
1300 is not intended for
ophthalmic use or any use


causing the acoustic beam
to pass through the eye.
The Cardiac Adult
application is not
intended for direct use on
the heart.

• Fetal (including
Obstetrics)
• Abdominal
• Pediatric1
• Intraoperative1,2
• Intraoperative Neuro
(also known as
Neurosurgery)1
• Small Organ (Small
Parts)3
• Adult Cephalic
(cephalic is also known
as Adult trans-cranial)1
• Neonatal Cephalic1
• Transrectal
• Transvaginal
• Musculoskeletal
(Conventional)
• Musculoskeletal
(Superficial)
• Cardiac Adult
• • Peripheral Vessel
(Peripheral
Vascular))


Professional healthcare
facility environment

Patient Population
Adult, Pediatric
Adult, Pediatric
Same

Since 2017 the following
transducers have been
terminated:9022 and 9067

The Ultrasound System
does not come in contact
with the patient.

Clinical display monitor
(CDM):
• 19” Optical bonded
glass front.
• Can be tilted and
moved sideways.
Cart:
• adjustable height and
with 4 lockable wheels
Keyboard:
Glass touch UI


Scan engine:
• 3 Transducer ports
• 64 TX/RX channels

Scan engine:
• 3 Transducer ports
64 TX/RX channels

- 3D Freehand
- 3D Professional
- DICOM Encrypted
- Vector Flow Imaging
(VFI)
- Varian Interface
- Strain Elastography
- Needle Enhancement
(X-shine)
- Wi-Fi
- bkViewer (SW running
on a mac/windows pc) –
not a medical device
- Prostate Volume Assist
(PVA)

- Proposed a new Prostate
Volume Assist (PVA), AI
software feature that has
been recently cleared
under K223830 for the
Reference Predicate device
Ultrasound System 2300.
PVA provides a workflow
improvement to an existing
prostate volume
measurement and
calculation tool.
It will require the same
biplane/triplane transrectal
transducers (9008, 9018,
9048) that are being used
for existing prostate
volume calculations and
measurements.

Speckle reduction,
compound imaging,
tissue harmonic imaging
(thi), trapezoid scanning
(virtual convex) strain,
elastography (se)


-19-inch Clinical Monitor
and touch input device
for user interaction.
-Touchpad track pad for
cursor control (Glass
Touch UI)
-Full configurable
interaction controls
(size/position)

Same – primary predicate
Equivalent – ref. predicate

ELECTROMAGNETIC
COMPATIBILITY
Complies with
requirements for Class A
devices of IEC 60601-1-2
SAFETY
ANSI/ AAMI/ES 60601-
1,
IEC 60601-2-37
SOFTWARE/
FIRMWARE
IEC62304
CLEANING
VALIDATION
AAMI TIR12, AAMI
TIR30
DICOM
NEMA PS3.1-3.20
Digital Imaging and
Communications in
Medicine (DICOM)

ELECTROMAGNETIC
COMPATIBILITY
Complies with
requirements for Class A
devices of IEC 60601-1-
2
SAFETY
ANSI/ AAMI/ES 60601-
1,
IEC 60601-2-37
SOFTWARE/
FIRMWARE
IEC62304
CLEANING
VALIDATION
AAMI TIR12, AAMI
TIR30
DICOM
NEMA PS3.1-3.20
Digital Imaging and


Includes images from
Holland dataset that are
only used as Test
images.

• The table summarizes number of datasets used for each different purpose.


Subject Device:
Predicate Device:
Predicate:

Contour ProtégéAI
Contour ProtégéAI
MIM
4.1 SEAS


4.1.0 CT
Dice
Contour
Contour
Evaluation

Model:
Structure:
MIM Atlas
ProtégéAI
ProtégéAI
Score

Table 2: DICE, MDA and External Evaluation Score Comparison Per Structure


Whole Body
–
Physiologic
al Uptake
Organs


4.1.0 CT Models: Structure: Relevant FOV Whole Body CT


4.1.0 CT Models:
Structure:
Relevant FOV


Level P, 24 Campbell Street
Sydney, NSW 2000
Australia


Device Name
Annalise Enterprise CTB Triage Trauma

Radiological computer aided triage and notification software
(21CFR892.2080)


Finding
Slice Thickness Range
Operating Point
Sensitivity % (Se)
Specificity % (Sp)

results of the study are summarized in the table below.


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

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

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

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

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

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

with the following FDA-recognized standards.


Validation Type
Definition
Acceptance Criteria

Number of correctly detected frames
×100
Total number of frames with nerve

1000
Average latency time of each frame (msec)


Validation Type
Average
Standard Deviation
95% CI

Summary Performance data, Standard Deviations & Confidence Intervals:


Number of correctly segmented frames
Total number of frames with nerve
×100


Summary Performance data, Standard Deviations & Confidence Intervals for Segmentation:


Testing Data Information for Segmentation:


Common or Usual Name:
Analyzer, Medical Image

Classification Name:
Medical image analyzer


Name of Device:
Auto Lung Nodule Detection

Manufacturer:
Samsung Electronics Co., Ltd


Device Name
Auto Lung Nodule Detection
qXR-LN

Regulation Description
Medical Image Analyser
Medical Image Analyser

Device type
Medical Image Analyser
Medical Image Analyser

Manufacturer
Samsung Electronics Co., Ltd
Qure.ai Technologies

The Auto Lung Nodule Detection
is computer-aided detection
software to identify and mark
regions in relation to suspected
pulmonary nodules from 10 to 30
mm in size. It is designed to aid
the physician to review the PA
chest radiographs of adults as a
second reader and be used as part
of S-Station, which is operation
software installed on Samsung
Digital X-ray Imaging systems.
Auto Lung Nodule Detection
cannot be used on the patients
who have lung lesions other than
abnormal nodules.

The qXR-LN (qXR_Lung_nodule) is
computer-aided detection software
to identify and mark regions in
relation to suspected pulmonary
nodules from 6 to 30 mm in size. The
device is intended to be used in the
incidental adult population. It is
designed to aid the physician to
review the frontal (AP/PA) chest
radiographs of adults acquired on
digital radiographic systems as a
second reader and be used with any
DICOM viewer or PACS . qXR-LN
provides adjunctive information only
and is not a substitute for the original
chest radiographic image.

Radiologists, emergency room
physicians or pulmonologists who
regularly review chest X-rays as part
of their daily practice.

Lung Nodules on PA/AP view Chest X-
rays

Technology
Machine learning
Deep/Machine Learning

ROI marked on the duplicated
input image.

Table 1 Comparison between qXR-LN and the Predicate Device


Sensitivity, FPPI and JAFROC were
calculated

Sensitivity, FPPI and AFROC were
calculated

Nodule level sensitivity 84.10 (77.97-
97.24)

JAFROC (aided – unaided) 7.8
(p=0.0003)
FPPI (aided – unaided) +0.019
Nodule level Sensitivity (aided-
unaided) 10.8

AFROC (aided - unaided) 7.62 (p < 1 x
10-5)
FPPI (aided – unaided) -0.0078
Nodule level Sensitivity (aided –
unaided) 11.96


Modality
AFROC Estimate (95% CI)
AFROC (aided-unaided)
P - Value

Table 2 Performance of readers aided vs unaided by qXR-LN


Transverse Resolution FWHM @ 1 cm
Pass
≤ 4.0 mm

Transverse Resolution FWHM @ 10 cm
Pass
≤ 4.8 mm

Transverse Resolution FWHM @ 20 cm
Pass
≤ 5.2 mm

Axial Resolution FWHM @ 1 cm
Pass
≤ 4.3 mm

Axial Resolution FWHM @ 10 cm
Pass
≤ 5.4 mm

Axial Resolution FWHM @ 20 cm
Pass
≤ 5.4 mm

Sensitivity @435 keV LLD
Pass
≥ 15 cps/kBq

Time of Flight Resolution at 5.3kBq/cc
Pass
≤ 214 ps

10mm sphere (Contrast / Background Variability)

13mm sphere (Contrast / Background Variability)

17mm sphere (Contrast / Background Variability)

22mm sphere (Contrast / Background Variability)

28mm sphere (Contrast / Background Variability)

37mm sphere (Contrast / Background Variability)


Brainomix 360 Triage LVO is Substantially Equivalent to the following Legally Marketed device:


In-plane (axial) resolution showed a range

of 0.381mm to 0.72mm with a median of 0.49mm. Four different tube voltage (KvP, peak kilovoltage)

values were encountered in the dataset (90KvP n=6, 100KvP, n=64; 120KvP n=137 and 140KvP, n=99).

Tube current was median of 526mA [IQR: 445mA - 645mA]. With respect to contrast phase

In the CTA image dataset, the slice thickness distribution was median 0.75mm [IQR: 0.625 – 0.8] with


62 studies were Early Arterial (EA), 130 Peak Arterial (PA), 110 in Equilibrium phase (EQ),

3 in Peak Venous (PV), and 3 in Late Venous (LV). All the images used a standard or a soft (low-pass

filter) convolution kernel for reconstruction (e.g., STANDARD in GE MEDICAL SYSTEMS).

Tube current was median of 526mA [IQR: 445mA - 645mA]. With respect to contrast phase during


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


Metrics
SIEMENS
GE Medical
Philips
Philips
Canon/ Toshiba


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


where appropriate. ICA = intracranial carotid artery. MCA M1 = proximal segment of the middle cerebral artery.


Brainomix 360 Triage LVO
Proposed Device

Viz.AI Viz LVO
Predicate Device (K223042)

Brainomix Triage LVO is a
notification-only, parallel
workflow tool for use by
hospital networks and trained
clinicians to identify and
communicate images of
specific patients to a specialist,
independent of standard of
care workflow.
Brainomix Triage LVO uses an
artificial intelligence algorithm
to analyze images for findings
suggestive of a prespecified
clinical condition and to notify
an appropriate medical
specialist of these findings in
parallel to standard of care
image interpretation.

Viz LVO is a notification-only,
parallel workflow tool for use
by hospital networks and
trained clinicians to identify
and communicate images of
specific patients to a specialist,
independent of standard of
care workflow.
Viz LVO uses an artificial
intelligence algorithm to
analyze images for findings
suggestive of a prespecified
clinical condition and to notify
an appropriate medical
specialist of these findings in
parallel to standard of care
image interpretation.
Identification of suspected

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


Identification of suspected
findings is not for diagnostic
use beyond notification.
Specifically, the device
analyzes CT angiogram images
of the brain acquired in the
acute setting, and sends
notifications to a
neurovascular specialist that a
suspected large vessel
occlusion LVO has been
identified and recommends
review of those images.
Images can be previewed
through a mobile application.
Brainomix Triage LVO is
intended to analyze terminal
ICA and MCA-M1 vessels for
LVOs.
Images that are previewed
through the mobile application
are compressed and are for
informational purposes only
and not intended for
diagnostic use beyond
notification. Notified clinicians
are responsible for viewing
noncompressed images on a
diagnostic viewer and
engaging in appropriate
patient evaluation and
relevant discussion with a
treating physician before
making care-related decisions
or requests.
Brainomix Triage LVO is limited
to analysis of imaging data and
should not be used in-lieu of
full patient evaluation or relied
upon to make or confirm
diagnosis.

findings is not for diagnostic
use beyond notification.
Specifically, the device
analyzes CT angiogram images
of the brain acquired in the
acute setting, and sends
notifications to a
neurovascular specialist that a
suspected large vessel
occlusion has been identified
and recommends review of
those images. Images can be
previewed through a mobile
application. Viz LVO is
intended to analyze terminal
ICA and MCA-M1 vessels for
LVOs.
Images that are previewed
through the mobile application
are compressed and are for
informational purposes only
and not intended for
diagnostic use beyond
notification. Notified clinicians
are responsible for viewing
noncompressed images on a
diagnostic viewer and
engaging in appropriate
patient evaluation and
relevant discussion with a
treating physician before
making care-related decisions
or requests.
Viz LVO is limited to analysis of
imaging data and should not
be used in-lieu of full patient
evaluation or relied upon to
make or confirm diagnosis.

Environment of use
Clinical/Hospital environment
Clinical/Hospital environment

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


Primary Users
Neurovascular Specialist
Neurovascular Specialist

Artificial intelligence algorithm
with database of images

Diagnostic application
Notification-only
Notification-only

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

Presentation of a preview of
the study for initial
informational purposes

No. Cases are not removed
from worklist or deprioritized

Mobile application and web
user interface

Standard off-the-shelf server
or virtual server

Acquires medical image data
from DICOM compliant
imaging devices and modalities

Materials
N/A – Software only device
Same

Biocompatibility
N/A – Software only device
Same

Sterility
N/A – Software only device
Same

Electrical Safety
N/A – Software only device
Same

Mechanical Safety
N/A – Software only device
Same

Chemical Safety
N/A – Software only device
Same

Thermal Safety
N/A – Software only device
Same

Radiation Safety
N/A – Software only device
Same


Proprietary/Trade Name:
QOCA® image Smart RT

5.5.
Identification of the Predicat
e Device:


Item
Subject Device
Predicate Device
Substantial

QOCA® image Smart
RT Contouring
System is a post-
processing software
intended to
automatically contour
DICOM CT imaging
data using deep-
learning-based
algorithms.

It is used by
radiation oncology
department to
register
multimodality
images and
segment (non-
contrast) CT
images, to generate
needed information

Similar
Both devices utilize
artificial
intelligence
algorithms to
automatically
contour organs at
risk, including the
head and neck, as
well as the pelvis,


provide a user
interface for data
visualization. System
settings, user settings,
progress status, and
other functionalities
are managed via a
web-based interface.
The software is not
intended to
automatically detect or
contour lesions. Only
DICOM images of
adult patients are
considered to be valid
input.

Algorithm
Deep Learning
Deep Learning
Same

Segmentation of
Organ at Risk in
the Anatomic
Regions

Brain stem,
Esophagus, Mandible,
Pharyngeal,
Constrictor Muscle
(PCM), Spinal cord,
Thyroid, Right eye,
Light eye, Right lens,
Left lens, Right optic
nerve, Left optic
nerve, Right parotid,
Left parotid,
Anorectum, Bladder,
Bowel bag, Lumbar
spine L5, Bilateral
seminal vesicles,
Right iliac, Left iliac,
Right proximal femur,

Head and Neck,
Thorax, Abdomen,
and Pelvis

Similar
The subject device
contains head and
neck, and pelvis.

QOCA® image Smart RT Contouring System K231855


Similar
The subject device
and the predicate
device are both
compatible only
with CT images for
the segmentation
feature. The
predicate device
claims to handle
only non-contrast
CT images, while
the subject device
can be used with
both contrast and
non-contrast CT
images.

No specific
requirement for the
scanner model, it is
recommended to use
multi-detector CT
(MDCT) equipment
with more than 16
slices for Radiation
Therapy Simulation
CT, and DICOM
compliance required.

No Limitation on
scanner model,
DICOM 3.0
compliance
required.

Similar
The Subject Device
and Predicate
Device are
substantially
equivalent in their
requirements for
DICOM
compliance,
ensuring
interoperability and
standardization in
medical imaging
data. The Subject
Device
recommends a
multi-detector CT


scanner with more
than 16 slices
specifically for
Radiation Therapy
Simulation CT to
provide detailed
imaging necessary
for accurate therapy
planning.

No Limitation on TPS
model, DICOM
compliance required.

No Limitation on
TPS model,
DICOM
3.0compliance
required.

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

Similar
The subject device
is designed for
adult use, and there
are no known
specific situations
that contraindicate
its usage.

The segmentation
performance was
validated using private
datasets from Taiwan
and public datasets
was collected from
various sources,
including the USA.
The datasets were
obtained from major
vendors such as GE,
Siemens, Philips, and
TOSHIBA. The

The segmentation
performance was
validated using
datasets from
China and the USA
using three major
vendors (GE,
Siemens and
Philips). The
segmentation
accuracy is
evaluated using
DICE similarity


Body Part
OARs
Acceptance Criteria
Model Performance

The results for standalone performance testing are as follows:


Device Trade Name
Lunit INSIGHT CXR Triage


Sensitivity (%)
Specificity (%)
AUC (%) (95% CI)

Table 2 Overall results of Accuracy testing for RADIFY® Triage


The sterEOS Workstation is intended for use in the
fields of musculoskeletal radiology and orthopedics in
both pediatric and adult populations as a general
device for acceptance, transfer, display, storage, and
digital processing of 2D X-ray images of the
musculoskeletal system including interactive 2D
measurement tools.
When using 2D X-ray images obtained with the EOS
imaging EOS system, sterEOS Workstation provides
interactive 3D measurement tools:
• To aid in the analysis of scoliosis and related
disorders and deformities of the spine in adult
patients as well as pediatric patients. The 3D
measurement tools include interactive analysis
based either on identification of anatomical
landmarks for postural assessment, or on a model of
bone structures derived from an a priori image data
set from 175 patients (91 normal patients, 47
patients with moderate idiopathic scoliosis and 37
patients with severe idiopathic scoliosis), and dry
isolated vertebrae data for spine modeling. The
model of bone structures is not intended for use to
assess individual vertebral abnormalities and is
indicated only for patients 7 years and older. For
postural assessment, a set of comparative tools is
provided allowing the comparison of performed
measurements to reference values for patients over
18 years old.

This cloud-based software is intended for
orthopedic applications in both pediatric and adult
populations.
2D X-ray images acquired in EOS imaging’s
imaging systems is the foundation and resource
to display the interactive landmarks overlayed on
the frontal and lateral images. These landmarks
are available for users to assess patient-specific
global alignment.
For additional assessment, alignment parameters
compared to published normative values may be
available.
This product serves as a tool to aid in the analysis
of spinal deformities, degenerative diseases,
lower limb alignment disorders, and deformities
through precise angle and length measurements.
It is suitable for use with adult and pediatric
patients aged 7 years and older.
Clinical judgment and experience are required to
properly use the software.

Yes, both devices are indicated for
the display and digital processing of
2D X-ray images. Both devices are
indicated for the same type of
disease, i.e., in the case of diseases
that have an impact on the patient's
global alignment.
The information provided by both
products is to be used as a
diagnostic aid for the user.

Table 5-1: Comparison for Substantial Equivalence


• To aid in the analysis of lower limbs alignment and
related disorders and deformities based on angle
and length measurements. The 3D measurement
tools include interactive analysis based either on
identification of lower limb alignment landmarks or
as for the spine, on a model of bone structures
derived from an a priori image data set. The model
of bone structures is not intended for use to assess
individual bone abnormalities. The 3D package
including model-based measurements and torsion
angles is indicated only for patients 15 years or
older. Only the 2D/3D ruler is indicated for
measurements in patients younger than 15 years
old.

The product code for the predicate
device is LLZ. The QIH product
code was created after clearance of
the predicate device and appears to
be a better match for VEA Align
than LLZ. VEA Align employs
machine learning based algorithms
that were trained from data
generated by EOS Imaging's
imaging systems to automate the
radiological image processing and
analysis.

Automated Radiological Image Processing
Software

Yes, both devices are classified
under 21 CFR 892.2050.

Yes, the subject device is
compatible with windows like the
predicate device. It is also


compatible with the MAC operating
system.

Clinicians (radiologists, orthopedists, radiographers),
3DServices persons, which have been trained to use
the application

Surgeons and clinical staff (physician assistants)
that have been trained to use the application

Yes, while the user population is
slightly different, the inclusion of
surgeons and clinical staff do not
affect device safety or
effectiveness.

3D measurement tools are used on adult and
pediatric patients over 7 years of age who suffer from
scoliosis and deformed spine pathology and for
patients over 15 years of age with deformed lower
limb pathology.

The device is indicated only for patients 7 years
and older.

Similar. The subject device is
dedicated to understanding the
global alignment of patients, spine
and lower limb included. The 15-
year-old limitation of the predicate
device was attached to workflow
dedicated specifically to lower limbs
only. This workflow is not present
in the subject device. This
difference does not affect device
safety or effectiveness.

2D images display and basic manipulation (zoom,
panning, distance, and angles measurements)

2D images display and basic manipulation
(zoom, panning)

Similar. In VEA Align the user does
not have the possibility to make
additional measurements after
reviewing the alignment provided
by the software. The removal of
this function does not prevent VEA
Align from achieving its intended
purpose and does not create a new
risk in VEA Align. The computation
of the specific clinical parameters
claimed in the VEA Align User
Manual is not impacted by the
removal of this function. This
function is not linked to the


performance of VEA Align.
Consequently, there is no impact
on the effectiveness of VEA Align.

The 3D model is deformed manually by the user
through control points up to matching accurately the
X-ray contours. This deformation is performed by
using the common linear least squares estimation
algorithm.

The 3D model supports the initial placement of
the patient anatomic landmarks on the images
using a machine learning-based algorithm. It is
not displayed to the user and as such it is not
part of the device outputs.

In VEA Align, the 3D model is not
visible to the user and the latter
cannot interact with it. The 3D
model is not part of the device
outputs contrary to the predicate
device. This difference does not
prevent VEA Align from achieving
its intended purpose and does not
create a new risk in VEA Align.
Therefore, this difference does not
affect device safety or
effectiveness.

Yes, the difference regarding the
software environment does not
introduce new risks, or impact
existing risks. Therefore, this
difference does not affect device
safety or effectiveness.


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

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


EFAI RTSuite CT HCAP-Segmentation System

Radiological Image Processing Software For Radiation
Therapy

Medical Image Management and Processing System


Proprietary Name
EFAI RTSuite CT HN-Segmentation System


Characteristic
Proposed Device
Predicate Device
Reference Device- 1
Reference Device- 2

Device Name
EFAI HCAPSeg
EFAI HNSeg
AccuContour™
AutoContour RADAC V2

EFAI HCAPSeg is a software
device intended to assist trained
radiation oncology
professionals, including, but not
limited to, radiation oncologists,
medical physicists, and
dosimetrists, during their
clinical workflows of radiation
therapy treatment planning by
providing initial contours of
organs at risk on non-contrast
CT images. EFAI HCAPSeg is
intended to be used on adult
patients only.

EFAI HNSeg is a software
device intended to assist trained
radiation oncology
professionals, including, but not
limited to, radiation oncologists,
medical physicists, and
dosimetrists, during their
clinical workflows of radiation
therapy treatment planning by
providing initial contours of
organs at risk in the head and
neck region on non-contrast CT
images. EFAI HNSeg is
intended to be used on adult
patients only.

It is used by radiation oncology
department to register
multimodality images and
segment (non-contrast) CT
images, to generate needed
information for treatment
planning, treatment evaluation
and treatment adaptation.

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

Table A. Comparison with the Predicate and Reference Devices


The contours are generated by
deep-learning algorithms and
then transferred to radiation
therapy treatment planning
systems. EFAI HCAPSeg must
be used in conjunction with a
DICOM-compliant treatment
planning system to review and
edit results generated. EFAI
HCAPSeg is not intended to be
used for decision making or to
detect lesions.
EFAI HCAPSeg is an adjunct
tool and is not intended to
replace a clinician's judgment
and manual contouring of the
normal organs on CT. Clinicians
must not use the software
generated output alone without
review as the primary
interpretation.

The contours are generated by
deep-learning algorithms and
then transferred to radiation
therapy treatment planning
systems. EFAI HNSeg must be
used in conjunction with a
DICOM-compliant treatment
planning system to review and
edit results generated. EFAI
HNSeg is not intended to be
used for decision making or to
detect lesions.
EFAI HNSeg is an adjunct tool
and is not intended to replace a
clinician's judgment and manual
contouring of the normal organs
on CT. Clinicians must not use
the software generated output
alone without review as the
primary interpretation.

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.

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

It is used by radiation oncology
department.

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

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

Localization
and Definition
of Objects
(ROI)

Organ-at risk of head and neck,
chest, abdomen, and pelvis

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

AutoContour is intended to
assist radiation treatment
planners in contouring and
reviewing structures within
medical images in preparation
for radiation therapy treatment
planning.
CT or MR input for contouring
of anatomical regions: Head and
Neck, Thorax, Abdomen and
Pelvis

A_Aorta, A_Carotid_L,
A_Carotid_R, Bladder,
Bone_Mandible,
BrachialPlex_L,
BrachialPlex_R, Brain,

Brain, BrainStem, Esophagus,
Eye_L, Eye_R, Lens_L,
Lens_R, Mandible,
OpticChiasm, OpticNerve_L,
OpticNerve_R, OralCavity,

A_Aorta, Bladder,
Bone_Mandible,
BrachialPlex_L,
BrachialPlex_R, Brain,
Brainstem, Breast_L, Breast_R,

CT Models:
A_Aorta, A_Aorta_Asc,
A_Aorta_Dsc, A_LAD,
Bladder, Bone_Ilium_L,
Bone_Ilium_R,


LN_Neck_VIIB_L,
LN_Neck_VIIB_R,
LN_Pelvics, LN_Sclav_L,
LN_Sclav_R, Liver, Lung_L,
Lung_R, Marrow_Ilium_L,
Marrow_Ilium_R,
Musc_Constrict, OpticChiasm,
OpticNrv_L, OpticNrv_R,
Parotid_L, Parotid_R,
PenileBulb, Pituitary, Prostate,
Rectum, Rib, SeminalVes,
SpinalCanal, SpinalCord,
Stomach, Trachea,
V_Venacava_S
MR Models:
OpticChiasm, OpticNrv_L,
OpticNrv_R, Brainstem,
Hippocampus_L,
Hippocampus_R

No Limitation on TPS model,
DICOM 3.0 compliance
required.

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

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

AccuContour automatically
processes input image data

Automatically contour, allow
the user to review and modify,
generate DICOM-compliant
structure set data.


Table B. Demographic Information for Training and Test Data Sets


Table C. Number of Images/Cases per OAR for Training and Testing Datasets


Table D. DSC Performance of EFAI HCAPSeg

EFAI HCAPSeg
Comparison Device
Statistical Result ‡

OARs Present in Both
EFAI HCAPSeg and the
Comparison Device

OAR, Organ-At-Risk; DSC, Dice Similarity Coefficient; SD, Standard Deviation.
‡ A statistical result includes outcomes obtained from data analysis using statistical methods, encompassing the non-inferiority test for OARs
presented in both EFAI HCAPSeg and the Comparison Device, as well as the superiority test for OARs unique to EFAI HCAPSeg. When the
p-value is <0.05, it indicates that EFAI HCAPSeg's performance is significantly non-inferior to or superior to the pre-specified performance,
respectively.


Table E. 95% HD Performance of EFAI HCAPSeg

OAR, Organ-At-Risk; HD, Hausdorff Distance; Q1, First Quartile; Q3, Third Quartile.
The unit of measurement for the 95% HD is millimeters (mm).
‡ A statistical result includes outcomes obtained from data analysis using statistical methods, encompassing the non-inferiority test for OARs
presented in both EFAI HCAPSeg and the Comparison Device, as well as the superiority test for OARs unique to EFAI HCAPSeg. When the
p-value is <0.05, it indicates that EFAI HCAPSeg's performance is significantly non-inferior to or superior to the pre-specified performance,
respectively.


Table F. DSC and 95% HD Performance of EFAI HCAPSeg across All Subgroups


Table G. Individual OAR performance of EFAI HCAPSeg (Proposed Device)


Sydney Neuroimaging Analysis Centre Pty Ltd (SNAC)

Level 1, 94 Mallett Street
Camperdown NSW 2050
Australia

Dr Tim (Chenyu) Wang
Director of Operations, Sydney Neuroimaging Analysis Centre

Email: tim@snac.com.au
Phone: + 61 2 9114 4187


Common Name:
Medical Image Processing Software

Classification Name
System, Image processing, Radiological


Common Name
Medical Image Processing Software

Classification Name:
System, Image processing, Radiological


Sydney Neuroimaging Analysis Centre Traditional 510(k) iQ-solutions™


Characteristic
Subject device:
Predicate Device:
Summary

• DICOM compatible
• Segmentation by machine learning and
deep learning algorithms (supervised
segmentation with Convolutional Neural
Networks)
• Image pre-processing components
• Measurement calculation
• Report generation techniques

• Segmentation by classical machine learning
and deep learning (supervised voxel
classification with Convolutional Neural
Networks)
• Image pre-processing components
• Measurement calculation
• Report generation techniques

Product Input
(Data Acquisition
Protocol)

• Pre-contrast T1-weighted MRI scans from
single or multiple time points,
• Fluid-attenuated inversion recovery (FLAIR)
MRI scans from single or multiple time
points, and
• Post-contrast T1-weighted MRI scans from
a single time point.

• T1-weighted and fluid-attenuated inversion
recovery (FLAIR) MRI images from a single
or multiple time points
• Non-contrast CT (NCCT) from a single time
point.

Similar.
Both the subject device and the predicate
device process T1 and FLAIR MRI images from
single or multiple time points.
NCCT may be used with icobrain 5.0 but not the
subject device; lack of this additional input
compatibility does not impact the safety or
clinical function of the subject device.

• Multiple electronic reports with volumetric
information on brain structures and white
matter hyperintensities (if any)
• Automatically compares results to
reference percentile data and to prior scans
when available
• Annotated DICOM images that can be
displayed on DICOM workstations and
Picture Archive and Communications
Systems (PACS)

• Multiple electronic reports with volumetric
information on brain structures and
hyperintensities and midline shift (for NCCT
only)
• Automatically compares results to a
reference healthy population data and to
prior scans when available
• Annotated DICOM images that can be
displayed on DICOM workstations and
Picture Archive and Communications
Systems (PACS)

Similar.
icobrain 5.0 additionally outputs the degree of
‘midline shift’ but this is calculated on NCCT
(relevant to specific clinical scenarios only). As
iQ-solutions™ does not analyze NCCT, this
difference does not impact the safety of the
subject device and is not clinically significant


MR:
• Normalized volume and volume changes of
the whole brain
• Normalized volume and volume changes of
gray matter
• Normalized volume and volume changes of
cortical gray matter
• Normalized cortical volumes of cortex in
brain lobes (frontal, temporal, parietal and
occipital)
• Normalized volume and volume changes in
the hippocampi
• Normalized volume and volume changes of
the thalamus
• Asymmetry index of structures, including
cortex in lobes and hippocampus
• Unnormalized volume and volume changes
of FLAIR white matter hyperintensities
• Unnormalized volume and volume changes
of T1-w white matter hyperintensities (in
post-contrast T1-w only)

MR:
• Normalized volume and volume changes of
the whole brain
• Normalized volume and volume changes of
gray matter
• Normalized volume and volume changes of
cortical gray matter
• Normalized volume and volume changes of
cortex in frontal, temporal, parietal and
occipital lobes
• Normalized volume and volume changes in
the hippocampi
• Normalized volume and volume change of
left and right hippocampus
• Asymmetry index of structures, including
cortex in lobes and hippocampus
• Unnormalized volume and volume changes
of FLAIR white matter hyperintensities
• FLAIR white matter hyperintensities in
different regions (juxtacortical,
periventricular, deep white matter and
infratentorial).
• Unnormalized volume and volume changes
of T1-w white matter hyperintensities and
hypointensities
CT:
• Normalized volumes of the whole brain
• Normalized volumes of the lateral
ventricles
• Normalized volumes of basal cisterns

Similar.
iQ-solutions™ does not process NCCT images
and therefore does not report findings for CT
images.
For head MRI analysis, iQ-solutions™ is similar
to icobrain 5.0, except that the subject device
iQ-solutions™ does not report statistics related
to regional (lobar) cortical volume change or
T1-w image-derived hypo-intensities. iQ-
solutions™ additionally reports volume analysis
of the thalamus.
These differences are not expected to affect
the safety or effectiveness of the subject
device or to be clinically significant in the
proposed intended use.


Datasets
Diagnosis
Number of
Age (mean±std)
Number of Scans

(subject/scans)
Subjects (F/M)
(Vendor:1.5T/3T)

Table 3. Demographic Summary for Used Data


Analysis Modules/Datasets
Number of Cases
Number of
Number of

(Train/Validation)
Cases (Test)
Subjects (All)

White matter hyperintensity Segmentation

Cortical Lobar and Subcortical Structure
Segmentation

Table 4. Summary of Number of Cases used in Developing/Evaluating Analysis Modules


Analysis Modules
Metric Result
Acceptable Rate
Pass/Fail

Sequence Classification
Accuracy = 100%
100%
Pass

Brain Extraction
DICE = 0.982
99.3%
Pass

Scaling Factor Estimation
STD = 0.0096
100%
Pass

Contrast-Enhancing Lesion Segmentation
DICE = 0.790
92.1%
Pass

Lesion Inpainting
PSNR = 30.79dB
97.5%
Pass

Brain Tissue Segmentation
DICE = 0.972
100%
Pass

Brain Volume Change Estimation
R2 = 0.869
96.3%
Pass

WMH Lesion Activity
R2 = 0.833
97.5%
Pass

Substructure Volume Change Estimation
STD = 0.0102
98.8%
Pass

Table 5: Summary of Performances of all the Analysis Modules


1800 Aston Ave, Ste 100
Carlsbad, CA 92008

Karen Liu, VP Quality and Regulatory
Carlsmed, Inc.
1800 Aston Avenue Suite 100
Carlsbad, CA 92008
760-766-1926
regulatory@carlsmed.com

Date Summary was Prepared:
October 31, 2023

Trade or Proprietary Name:
aprevo® Digital Segmentation

K202034 aprevo™ Intervertebral Body Fusion Device
K201232 Limbus Contour

Medical Image Management and Processing System

Classification:
Class II per 21 CFR §892.2050


Characteristic
Subject Device
Predicate Device
Differences

Similar: both are Image
Processing Systems

aprevo® Digital
Segmentation software
is intended to be used
by trained, medically
knowledgeable design
personnel to perform
digital image
segmentation of the
spine, primarily
lumbar anatomy. The
device inputs DICOM
images and outputs a
3-D model of the
spine.

Mimics Medical is
intended for use as a
software interface and
image segmentation system
for the transfer of medical
imaging information to an
output file. Mimics
Medical is also intended for
measuring and treatment
planning.
The Mimics Medical
output can be used for the
fabrication of physical
replicas of the output file
using traditional or additive
manufacturing methods.
The physical replica can be
used for diagnostic
purposes in the field of
orthopaedic, maxillofacial
and cardiovascular
applications.
Mimics Medical should be
used in conjunction with
expert clinical judgment.

Similar. The subject device
has a narrower indication for
use compared to the
predicate device.

comparison between the subject device to its predicate device, Mimics Medical (K183105)


DICOM and standard
imaging formats (such as
RAW, TIFF, BMP and
jpeg format)

Similar. The subject device
has narrower input file types.

Manual tools, Semi-
automatic tools, and
automatic algorithms

Similar. Both devices
include automated spine
segmentation tool.

User cannot review
and edit segmentation
and 3D models

Contains tools to review
and edit segmentation and
3D models

Similar: The output of both
devices can be reviewed by
the user. While the predicate
device has a viewer to
review and edit
segmentation outputs the
subject device output does
not include any viewer but
its outputs can be reviewed
as part of the entire
workflow utilizing 3rd party
software.

The predicate device
generates a 3D model

The subject device
generates an output
file.

The predicate device
generates an output file.

Trained Personnel,
Knowledgeable in
Medicine

Trained personnel,
knowledgeable in medicine


Primary verification – qualitative results:


Secondary evaluation – qualitative results:


Anatomy
DICE when successful
DICE when failed

scores (i.e., Dice coefficient) and qualitative assessment by independent clinical experts.


The LOGIQ E10 is a full featured, track 3, general purpose diagnostic

ultrasound system which consists of a mobile console approximately 585


mm wide (keyboard), 991 mm deep and 1300 mm high that provides

digital acquisition, processing and display capability. The user interface

includes a computer keyboard, specialized controls, 12-inch high-

resolution color touch screen and 23.8-inch High Contrast LED LCD

510(k) Premarket Notification Submission


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

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

Information about clinical
subgroups and confounders
present in the dataset

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

Information about equipment
and protocols used to collect
images

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

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

• The Validation set of 30 patient ( 60 images )
were loaded onto the LOGIQ validation
system.
• 2 Readers (certified sonographer/Clinician) to
ground truth the measurements: Length on the
Longitudinal views and the Height and Width
Measurements on the Transverse views.
• Board Certified Nephrologist arbitrated the
ground truth between the above two readers to
establish the reference standard for the dataset
• Summary of Results:
The Longitudinal model for length
measurements has average accuracy of 96.45
with 95% CI of ± 1.26% and average absolute
error of 0.35cm at 95% CI of ±0.12 cm.
The Transverse model for width
measurements has average accuracy of
92.94% with 95% CI of ± 3.02% and average
absolute error of 0.38cm at 95% CI of ±0.14
cm.
The Transverse model for width
measurements has average accuracy of
93.13% with 95% CI of ± 3.63% and average

Auto Renal Measure Assistant : Al Summary of Testing


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

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

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

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

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

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

Auto Abdominal Color Assistant : Al Summary of Testing


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

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

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

Auto Preset Assistant : Al Summary of Testing