Subject device:
BrightHeart View Classifier v1.1

Predicate device:
BrightHeart View Classifier v1.0

21 CFR §892.2050, Medical image
management and processing system

Healthcare professionals (HCP) trained and
qualified in fetal ultrasound.

Pregnant women aged 18 or older,
undergoing anatomic ultrasound
examination
The device is not intended for use in
multiple pregnancies.

Platform
Cloud-based stand-alone software
Cloud-based stand-alone software

Model Inputs
Ultrasound images and video clips
Ultrasound images and video clips

Model method
Neural networks
Neural networks

Classification of ultrasound images into
standard views of fetal heart.


Identifies standard views of the fetal heart
and abdomen in images and video clips.

● Annotated DICOM files within a
DICOM viewer
● Device web interface


Modification
Rationale
Testing Methods
Impact Assessment

Modification of
training and/or
validation datasets to
update model weights
and detection
thresholds

Improvements in the
view classification
performance of the
device.

Re-training of the
BrightHeart View
Classifier with new
data to optimize its
performance followed
by performance
testing and a
comparison of the
original BrightHeart
View Classifier to
the modified
BrightHeart View
Classifier (using
performance metrics)
and verification and
validation.

Improved
performance
metrics of modified
BrightHeart View
Classifier
Benefit-Risk
Analysis:
Benefits: Improved
performance;
generalization for
diverse cases.
Risks: Overfitting;
unintended bias.
Risk Mitigation:
Testing data
sequestration and
testing on new data
will ensure proper
evaluation and
mitigate risks of
overfitting.

Summary of changes to BrightHeart View Classifier per the PCCP:


Document Title: eyonisLCS1.1_K251474_510(k)
Summary

Doc #
eyonisLCS1.1_K251474_510(k)
Summary


Legal Manufacturer
ScreenPoint Medical B.V.

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


21 CFR 892.2090
Radiological Computer
Assisted Detection
And Diagnosis
Software

A concurrent reading
aid for Physicians
interpreting screening
images, to identify
findings and assess
their level of
suspicion.

Finding level:
Continuous score 1-
100 indicating the
level of suspicion of
malignancy (from low
suspicion to high
suspicion).
Organ level:
None
Exam level:

Finding level:
Discreet score 1-10
accompanied with
malignancy rate (from
probably benign to very
suspicious)
Organ level:
None
Exam level:
None

Both devices’ algorithms
yield a continuous 100-
point score, intended to be
interpreted as likelihood of
malignancy. The predicate
provides the 100-point
score directly to the user,
while Median LCS provides
a simplified 10-point score
that is monotonic with the
likelihood of malignancy.
Substantially equivalent.

effectiveness of the device when used as labeled.


10-point scale score
indicative of higher
frequency of cancer
positive.

Upon user request by
clicking in a position
of the image also
detected by
TransparaTM.

Findings are by-default
displayed when score is
equal or higher to 2.

Both are implementations
of the same intention:
reducing the number of
findings the user has to
review. Substantially
equivalent.

Distinguishes two
types of suspicious
findings (calcifications
and soft tissue
lesions).
The device output
includes the location
and the outline of
findings.
Transpara™
processing server is a
standalone system
WITHOUT a user
interface.

Only targets one type of
findings (nodules,
solid/part-solid).
Median LCS result report
includes the localization
(slice number), two
snapshots, diameters
and volume.
Same.

Despite some differences
between the predicate
device and Median LCS,
device output and user
interface are still
comparable and do not
raise new questions
regarding safety and
effectiveness of the device.

A chain of medical
image processing and
machine learning
techniques are
implemented. The
device includes ‘deep
learning’ modules for
recognition of
suspicious lesions.
These modules are
trained with very large
databases of cancer
and normal patients
proven by biopsy or
follow-up.


Standard ID
Year / Edition
Standard Title
Recognition #

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

First edition 2021-04
Corrected Version 2021-12

Medical devices – Information to be supplied by
the manufacturer

Medical Devices - Application Of Risk
Management To Medical Devices

Medical Device Software - Software Life Cycle
Processes

Health software - Part 1: General requirements
for product safety

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

Digital Imaging and Communications in
Medicine (DICOM) Set

voluntary FDA recognized standards and guidelines:


General Principles of Software Validation

The 510(k) Program: Evaluating Substantial Equivalence in Premarket
Notifications [510(k)]

Postmarket Management of Cybersecurity in Medical Devices

Applying Human Factors and Usability Engineering to Medical Devices

Design Considerations and Pre-market Submission
Recommendations for Interoperable Medical Devices

Appropriate Use of Voluntary Consensus Standards in Premarket
Submissions for Medical Devices

Recommended Content and Format of Non-Clinical Bench Performance
Testing Information in Premarket Submissions

Multiple Function Device Products: Policy and Considerations: Guidance for
Industry and Food and Drug Administration

Unique Device Identification System: Form and Content of the Unique Device
Identifier (UDI)

Technical Performance Assessment of Quantitative Imaging in Radiological
Device Premarket Submissions

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

Content of Premarket Submissions for Device Software Functions

Off-The-Shelf Software Use in Medical Devices

Cybersecurity in Medical Devices: Refuse to Accept Policy for Cyber Devices
and Related Systems Under Section 524B of the FD&C Act

Electronic Submission Template for Medical Device 510(k) Submissions

The following guidance documents were used to support this submission:


Heart Rate value into the Measurement Display Area, which can be used for specific calculations,

such as cardiac output. The subject devices support heart rate measurement entry from different


Subject device
Predicate device
Substantial

Device name
SwiftSight-Brain
Neurophet AQUA V3.1

Manufacturer
AIRS Medical Inc.
NEUROPHET, Inc

This device is intended for
automatic labelling,
visualization and volumetric
quantification of segmentable
brain structures and lesions
from a set of MR images.
Volumetric data may be

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


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

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

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

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

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


- Tissue contrast check
- Scan protocol verification
 Results must be reviewed by a
medical professional

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


Subject Device
Overjet CBCT Assist
Overjet, Inc.

Primary Predicate
Relu Creator (K233925)
Relu BV

Secondary Predicate
Planmeca Romexis
(K200572)
Planmeca Oy

21 CFR 892.2050, Medical
Image Management and
Processing System

Overjet CBCT Assist is a
software for the analysis of
dental and
craniomaxillofacial Cone
Beam Computed
Tomography (CBCT)
images. The software
utilizes artificial
intelligence/machine
learning algorithms to
provide automated
segmentations,
user-delineated or
automated measurements,
and 2D/3D visualizations.
These tools are intended to
assist dental professionals
in their review and
interpretation of CBCT
images by facilitating
anatomical assessment and
supporting their diagnostic
and treatment planning
process. The device is not
intended as a replacement
for a complete clinician’s
review or their clinical
judgement.

Relu Creator is a
software program for the
management, transfer,
and analysis of dental
and craniomaxillofacial
image information, and
can be used to provide
design input for dental
solutions. It
displays and enhances
digital images from
various sources to
support the diagnostic
process and
treatment planning. It
stores and provides
these images within the
system or across
computer
systems at different
locations.

Planmeca Romexis is a
medical imaging software
intended for use in dental and
medical care as a tool for
displaying and visualizing
dental and medical 2D and
3D image files from imaging
devices, such as projection
radiography and CBCT. It is
intended for use by
radiologists, clinicians,
referring physicians and other
qualified individuals to
retrieve, process, render,
diagnose, review, store, print,
and distribute
images of both adult and
pediatric patients.
Planmeca Romexis is also a
preoperative software used
for dental implant planning.
Based on the planned implant
position a model of a surgical
guide for a guided implant
surgery can be designed. The
designed objects can be
exported to manufacture a
separate physical product.

All three devices
are intended to
support the
visualization,
analysis, and
interpretation of
3D CBCT data in
dental and
craniomaxillofacial
applications.
While the
predicate devices
include
downstream
dental solutions,
the subject device
is limited to
diagnostic
support. These
differences do not
introduce new
questions of
safety or
effectiveness.

Table 1: Comparison to Predicate Devices


Planmeca Romexis is also a
preoperative software for
simulating / evaluating
surgical treatment options.
Planmeca Romexis is also
intended to be used for
monitoring, recording, storing
and displaying mandibular
jaw positions and movements
relative to the maxilla.
Additionally, Planmeca
Romexis includes monitoring
features for Planmeca
devices for maintenance
purposes. The software is
designed to work as a
stand-alone or as an
accessory to Planmeca
imaging and Planmeca dental
unit products in standard PC.
The software is for use by
authorized healthcare
professionals. Use of the
software for implant planning
requires that the user has the
necessary medical training in
implantology and surgical
dentistry. Use of the software
for surgical treatment
planning requires that the
user has the necessary
medical training in
maxillofacial surgery.
Indications of the dental
implants do not change with
guided surgery compared to
conventional surgery.

The subject
device is offered
as a web
application only.

Supports CBCT-based
DICOM files: standard
CBCT, enhanced CBCT,
and craniofacial 3D DICOM
formats.

Supports a broader
range of image types
used in dental
workflows, including
CBCT, IOS (intraoral
scans), and FS (facial
scans).

The subject
device is focused
exclusively on 3D
volumetric CBCT
data while the
predicate devices
are capable of


ingesting a
broader range of
imaging
modalities.

PDF report
Common formats for
dental and
craniomaxillofacial
treatment planning
applications (STL,
DICOM, PLY, OBJ)

All three devices
provide output as
PDF reports. The
predicate Relu
Creator also
supports export in
standard 3D
planning formats
as compared to
the subject device.

● Multi-Planar
Reconstruction Views
● 3D Volume Rendering
● Virtual Panoramic view
reconstruction
● Image enhancement and
manipulation (Brightness,
Contrast)
● AI-based segmentation of
dental and
craniomaxillofacial
anatomy, as well as past
restorations.
● Measurement - Distance
(length, diameter,
perimeter), Area, Angle,
Signal intensity
● Implant Site Evaluation
● 3rd molar surgery
planning by generating
linear measurement to
nearby critical structures
● Supports airway
evaluation by reporting
minimum cross-sectional
area

● Multi-Planar
Reconstruction Views
● 3D Volume Rendering
● Virtual Panoramic
view reconstruction
● Image enhancement
and manipulation
(Brightness, Contrast)
● AI-based
segmentation of
dental and
craniomaxillofacial
anatomy, as well as
past restorations.
● Preoperative Planning
● STL export supported
for general anatomical
structures.

● Multi-Planar Reconstruction
Views
● 3D Volume Rendering
● Virtual Panoramic view
reconstruction
● Image enhancement and
manipulation (Brightness,
Contrast)
● Segmentation of the jaws
● Linear, angular, area and
volumetric measurements
● Implant Planning
● Preoperative software for
simulating / evaluating
surgical treatment
● Airway volume
measurement

The differences in
specific features
do not introduce
new questions of
safety or
effectiveness.


Reference Device
Overjet Charting Assist
(K241684)
Overjet Inc.

Subject Device
Overjet CBCT Assist
Overjet, Inc.

21 CFR 892.2050, Medical Image
Management and Processing System


Overjet CBCT Assist is a software for the
analysis of dental and craniomaxillofacial
Cone Beam Computed Tomography (CBCT)
images. The software utilizes artificial
intelligence/machine learning algorithms to
provide automated segmentations,
user-delineated or automated
measurements, and 2D/3D visualizations.
These tools are intended to assist dental
professionals in their review and
interpretation of CBCT images by facilitating
anatomical assessment and supporting their
diagnostic and treatment planning process.
The device is not intended as a replacement
for a complete clinician’s review or their
clinical judgement.

Overjet Charting Assist is a Medical Image
Management and Processing System (MIMPS)
intended to detect natural dental structures
including detection of tooth anatomy (enamel,
pulp), and tooth numbering, as well as dental
structures added through past restorative
treatments: implants, crowns, endodontic
treatment (previous root canal treatment), fillings.
The device is intended to assist dental
professionals in producing dental charts based on
image analysis. The Overjet Charting Assist
detects these findings on bitewing (BW) and
periapical (PA) images for patients with primary
and/or permanent teeth (Ages 5 and above), and
panoramic (Pano) radiographs for patients with
only permanent teeth. The device is not intended
as a replacement for a complete clinician's review
or clinical judgment that considers other relevant
information from the image or patient history.

Input Data
3D CBCT volumes (DICOM format)
2D dental radiographs

AI-based algorithms for the detection of
dental and craniomaxillofacial anatomy, as
well as past restorations.

AI-based algorithms for the detection of natural
dental structures and structures added through
past restorative treatment.


510(k)
Product Code
Trade Name
Manufacturer

K234009
LLZ, QIH
Acorn 3D Software
Mighty Oak Medical


Algorithmic with little or no
direct human control

A combination of
algorithmic and direct
human control

Machine Learning algorithm
used to automatically
segment individual vertebrae
and the pelvis

Algorithmic based tools that
do not incorporate machine
learning.

Spinal anatomy:
 Thoracic (T1-T12)
 Lumbar (L1-L5)
 Sacrum

Musculoskeletal &
craniomaxillofacial bone:
 Short
 Long
 Flat
 Sesamoid
 Irregular


Device→
Acorn 3D Software & 3DP Models
Acorn 3D Software & 3DP Models
Surgimap

Acorn 3D Software(AC-SEG-4009);
Acorn 3DP Model (AC-101-XX)

Acorn 3D Software (AC-SEG-4009); Acorn 3DP
Model (AC-101-XX)

Picture Archiving and
Communication System (PACS)

Manufacturer
Mighty Oak Medical
Mighty Oak Medical
Nemaris Inc.

Acorn 3D Software is a modular image
processing software intended for use as an
interface for visualization of medical images,
segmentation, treatment planning, and
production of an output file.
The Acorn 3D Segmentation module is intended
for use as a software interface and image
segmentation system for the transfer of CT or
CTA medical images to an output file. Acorn
Segmentation is also intended for measuring
and treatment planning. The Acorn
Segmentation output can also be used for the
fabrication of physical replicas of the output file
using additive manufacturing methods, Acorn
3DP Models. The physical replica can be used
for diagnostic purposes in the field of
musculoskeletal and craniomaxillofacial
applications.
The Acorn 3D Trajectory Automation module
may be used to plan pedicle screw placement
in the spine in pediatric and adult patients.
Acorn 3D Software and 3DP Models should be
used in conjunction with expert clinical
judgment.

Acorn Segmentation is intended for use as a
software interface and image segmentation
system for the transfer of CT or CTA medical
images to an output file. Acorn Segmentation is
also intended for measuring and treatment
planning. The Acorn Segmentation output can
also be used for the fabrication of physical
replicas of the output file using additive
manufacturing methods, Acorn 3DP Models. The
physical replica can be used for diagnostic
purposes in the field of musculoskeletal and
craniomaxillofacial applications.
Acorn Segmentation and 3DP Models should be
used in conjunction with expert clinical
judgment.

The Surgimap software assists
healthcare professionals in
viewing, storing, and measuring
images as well as planning
orthopedic surgeries. The device
allows service providers to
perform generic as well as
specialty measurements of the
images, and to plan surgical
procedures. The device also
includes tools for measuring
anatomical components for
placement of surgical implants,
and offer online synchronization
of the database with the
possibility to share data among
Surgimap users. Clinical judgment
and experience are required to
properly use the software.


Acorn 3D Software & 3DP Models
(this submission)

Surgimap is image analysis and
surgical planning software
intended for use by medical
professionals to view, measure,
and annotate medical images
(including DICOM images) for
diagnostic and preoperative
planning purposes.

Automated radiological image processing
software

Medical image management
and processing system.

Acorn 3D Software is an image processing
software that allows the user to import, visualize
and segment medical images, check and
correct the segmentations, and create digital
3D models. The models can be used in Acorn 3D
Software for measuring, treatment planning and
producing an output file to be used for additive
manufacturing (3D printing). Acorn 3D Software
is structured as a modular package.
This includes the following functionality:
 Importing medical images in DICOM
format
 Viewing images and DICOM data
 Selecting a region of interest using generic
segmentation tools
 Segmenting specific anatomy using
dedicated semi-automatic tools or fully
automatic algorithms
 Verifying and editing a region of interest
 Calculating a digital 3D model and
editing the model
 Measuring on images and 3D models
 Exporting 3D models to third-party
packages
 Planning pedicle screw placement
The Acorn 3D Segmentation module contains
both machine learning based auto
segmentation as well as semi-automatic and
manual segmentation tools. The auto-
segmentation tool is only intended to be used
for thoracic and lumbar regions of the spine (T1-
T12 and L1-L5) and the pelvis (sacrum). Semi-
automatic and manual segmentation tools are
intended to be used for all musculoskeletal
anatomy.
Semi- Manual
Automatic
Automatic
Algorithmic A
Directly
with little or combination
noitinifeD controlled no direct of algorithmic
by a
human and direct
human
control human control

Acorn Segmentation is an image processing
software that allows the user to import, visualize
and segment medical images, check and
correct the segmentations, and create digital
3D models. The models can be used in Acorn
Segmentation for measuring, treatment
planning and producing an output file to be
used for additive manufacturing (3D printing).
Acorn Segmentation is structured as a modular
package.
This includes the following functionality:
 Importing medical images in DICOM
format
 Viewing images and DICOM data
 Selecting a region of interest using generic
segmentation tools
 Segmenting specific anatomy using
dedicated semi-automatic tools or fully
automatic algorithms
 Verifying and editing a region of interest
 Calculating a digital 3D model and
editing the model
 Measuring on images and 3D models
 Exporting 3D models to third-party
packages
The Acorn Segmentation module contains both
machine learning based auto segmentation as
well as semi-automatic and manual
segmentation tools. The auto-segmentation tool
is only intended to be used for thoracic and
lumbar regions of the spine (T1-T12 and L1-L5)
and the pelvis (sacrum). Semi-automatic and
manual segmentation tools are intended to be
used for all musculoskeletal anatomy.
Semi- Manual
Automatic
Automatic
Algorithmic A
Directly
with little or combination
noitinifeD controlled no direct of algorithmic
by a
human and direct
human
control human control

Surgimap is a software
application intended for use by
trained medical professionals,
including physicians, to view,
measure, and annotate medical
images (including DICOM
images) for diagnostic and
planning purposes.
Surgimap provides tools for
orthopedic surgical planning
such as templating and
measuring angles, lengths, and
other relevant anatomical data.
The software may also be used to
create preoperative plans that
can be shared among
healthcare professionals.
It is intended as a planning and
imaging tool and not for primary
image interpretation or as a sole
diagnostic device.

Algorithmic
with little or
no direct
human
control

A
combination
of algorithmic
and direct
human control


Machine
Learning
algorithm Algorithmic
used to based tools Manual
automaticall that do not tools
epyT y segment incorporate requiring
individual machine user input.
vertebrae learning.
looT
and the
pelvis
Musculoske
letal &
)s( Musculoskelet craniomaxil
noitacoL Spinal al & lofacial
anatomy: craniomaxillof
bone:
 Thoracic acial bone:
 Short
(T1-T12)  Short
 Long
lacimotanA  Lumbar  Long
 Flat
(L1-L5)  Flat
 Sesam
 Sacrum  Sesamoid
oid
 Irregular
 Irregul
ar
Acorn 3DP Model is an additively manufactured
physical replica of the virtual 3D model
generated in Acorn Segmentation. The output
file from Acorn Segmentation is used to
additively manufacture the Acorn 3DP Model.
The Acorn 3D Trajectory Automation contains
dedicated fully automatic algorithms for
planning pedicle screw trajectories. The
algorithms are only intended to be used for the
thoracic and lumbar regions of the spine (T1-T12
and L1-L5). The output file from Acorn 3D
Trajectory Automation contains information
relevant to pedicle screw placement surgery,
including entry points, end points, and screw
sizes of planned screws.

Machine
Learning
algorithm
used to
automaticall
y segment
individual
vertebrae
and the
pelvis

Algorithmic
based tools
that do not
incorporate
machine
learning.

Machine
Learning
algorithm
Algorithmic
used to
based tools Manual
automatic
that do not tools
ally
incorporate requiring
epyT segment machine user input.
individual
learning.
vertebrae
looT
and the
pelvis
Musculoske
letal &
)s( Musculoskelet craniomaxil
noitacoL Spinal al & lofacial
anatomy: craniomaxillof
bone:
 Thoracic acial bone:
 Short
(T1-T12)  Short
 Long
lacimotanA  Lumbar  Long
 Flat
(L1-L5)  Flat
 Sesam
 Sacrum  Sesamoid
oid
 Irregular
 Irregul
ar
Acorn 3DP Model is an additively manufactured
physical replica of the virtual 3D model
generated in Acorn Segmentation. The output
file from Acorn Segmentation is used to
additively manufacture the Acorn 3DP Model.

Machine
Learning
algorithm
used to
automatic
ally
segment
individual
vertebrae
and the
pelvis

Spinal
anatomy:
 Thoracic
(T1-T12)
 Lumbar
(L1-L5)
 Sacrum

Musculoskelet
al &
craniomaxillof
acial bone:
 Short
 Long
 Flat
 Sesamoid
 Irregular

Musculoske
letal &
craniomaxil
lofacial
bone:
 Short
 Long
 Flat
 Sesam
oid
 Irregul
ar

Acorn 3D Software is a standalone modular
software package. This software package
includes, but is not limited to, the following
functions:
Image import
 Importing medical images in DICOM
format (e.g. CT and CTA)
Image Processing
 Processing of images with common noise-
reduction filters
 Editing of spatial arrangement of images
Visualization
 Viewing images and DICOM data
Segmentation
 Selecting a region of interest using generic
segmentation tools
 Segmenting specific anatomy using
dedicated semi-automatic tools
 Segmenting specific vertebral anatomy
using machine-learning-based fully
automatic algorithms
 Verifying and editing a region of interest
Measurement
 Measuring on images and 3D models
Data Export
 Machine-readable format (e.g. JSON and
STL)
 Project Files (proprietary file format specific
to the software)

Acorn Segmentation is a standalone modular
software package. This module includes, but is
not limited to the following functions:
Image Import
 Importing medical images in DICOM
format (e.g. CT and CTA)
Image Processing
 Processing of images with common noise-
reduction filters
 Editing of spatial arrangement of images
Visualization
 Viewing images and DICOM data
Segmentation
 Selecting a region of interest using generic
segmentation tools
 Segmenting specific anatomy using
dedicated semi-automatic tools
 Segmenting specific vertebral anatomy
using machine-learning-based fully
automatic algorithms
 Verifying and editing a region of interest
Measurement
 Measuring on images and 3D models
Data Export
 Machine-readable format (e.g. STL)
 Project Files (proprietary file format specific
to the software)

Surgimap software includes the
following functions:
Image Import
 Importing medical images in
DICOM format (e.g. CT,
CTA, MRI, etc.)
 Standard formats (JPEG, TIFF,
PNG, PPT…)
Image Processing
 Processing of images with
common noise-reduction
filters
 Editing of spatial
arrangement of images
Visualization
 Viewing images and DICOM
data
Segmentation
 None
Measurement
 Measuring on images
Data Export
 Machine-readable format
(e.g. CSV and DICOM)
 Project Files (proprietary file
format specific to the
software)


Device→
Acorn 3D Software & 3DP Models
Acorn 3D Software & 3DP Models
Surgimap

3D Models
 Calculating a digital 3D model and editing
the model
 Smoothing a 3D model
 Importing 3D models
Treatment Planning
 Importing of third-party STLs to visualize
planned interactions with anatomy as
represented in DICOM images
 Generic Implants
o Pedicle Screw Implant
Other features
 Using a collection of images and masks as
a training dataset for machine-learning
segmentation algorithm

3D Models
 Calculating a digital 3D model and editing
the model
 Smoothing a 3D model
 Importing 3D models
Treatment Planning
 Importing of third-party STLs to visualize
planned interactions with anatomy as
represented in DICOM images
Other features
 Using a collection of images and masks as
a training dataset for machine-learning
segmentation algorithm

 Standard formats (JPG, PNG,
TIFF, PDF)
3D Models
 None
Treatment Planning
 Procedures
o Wedge
Osteotomy(ex:SPO,PSO)
o Opening Osteotomy
o Resect Osteotomy
o Un-Slip (i.e. spondylolisthesis
correction)
o Anatomical Marker
 Generic Implants
o Screw Implant
o Reference Line for Screw
o Interbody Spacer o
Freehand Rod
o SA Rod
o Coronal Free hand
 Vendor Specific Implant
o Globus Medical Spacer
Template
o Globus Rod
o K2M Interbody Spacer
Template
o K2M BACS Patient Specific
Rod
o K2M Pre-Bent Rod Template
o K2M Screw
Other features
 Various

 Surgeon Inputs / Preferences
o Screw Size
o Trajectory Angle
o Tulip Position
o Breach Criteria
 Automatic Optimization
 Review against patient imaging

 Surgeon Inputs /
Preferences
o Screw Size
o Trajectory Angle
o Entry and End Point
 Manual Optimization
 Review against patient
imaging

The Acorn 3D Segmentation module output can
be used for the fabrication of physical replicas
of the output file using additive manufacturing
methods. The physical replica can be used for
diagnostic purposes in the field of orthopedic
and musculoskeletal applications.

The Acorn Segmentation output can be used for
the fabrication of physical replicas of the output
file using additive manufacturing methods. The
physical replica can be used for diagnostic
purposes in the field of orthopedic and
musculoskeletal applications.

The Acorn 3D Segmentation module can be
used by biomedical engineers or personnel
equivalent by training or experience. Their
results should be used in conjunction with expert
clinical judgement.
The Acorn 3D Trajectory Automation module is
intended for use by medical professionals, such
as clinicians and surgeons, who are trained in
spinal procedures and the interpretation of
diagnostic imaging.
Be advised that the quality of medical images
determines the accuracy of the 3D model in
Acorn 3D Software. Scanning protocols are left
to the discretion of the user; however, we
recommend that industry standards are
referenced and followed. Only images
obtained less than six months before should be
used for planning and/or evaluating treatment
options.

Acorn Segmentation can be used by
biomedical engineers or personnel equivalent
by training or experience. Their results should be
used in conjunction with expert clinical
judgement.
Be advised that the quality of medical images
determines the accuracy of the 3D model in
Acorn 3D Software. Scanning protocols are left
to the discretion of the user; however, we
recommend that industry standards are
referenced and followed. Only images
obtained less than six months before should be
used for planning and/or evaluating treatment
options.

Surgimap is intended as a
decision support system for
persons who have received
appropriate medical training, and
should not be used as a sole
basis for making clinical
decisions pertaining to patient
diagnosis, care, or management.
All information derived from the
software must be clinically
reviewed regarding its plausibility
before use in treating patients.
Any derivation of the application
of medical information from the
program, other than the original
design or intended use thereof, is
not advised and considered a
misuse of the software product.


DHT8B: Division of Radiological Imaging Devices and

Office of Product Evaluation and Quality


Rapid SDH is a radiological
computer aided triage and
notification software indicated for
use in the triage and notification of
hemispheric SDH in non-enhanced
head images. The device is intended
to assist trained radiologists in


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

Contraindications/Limitations/Excl
usions:
• Rapid OH is intended for use
for adult patients.
• Input data image series
containing excessive patient
motion or metal implants
may impact module analysis
accuracy, robustness and
quality.
• Ventriculoperitoneal shunts
are contraindicated
Exclusions:
• Series with missing slices or
improperly ordered slices
• data acquired at x-ray tube
voltage < 100kVp or >
140kVp.


• data not representing human
head or head/neck
anatomical regions

Notifications, Reports, DICOM
Secondary Capture Series


Comparison Feature
Predicate Device: ContaCT
Subject Device: Methinks CTA Stroke

ContaCT 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, 1 independent of
standard of care workflow.
ContaCT uses an artificial intelligence algorithm
to analyze images for findings suggestive of a
pre-specified clinical condition and to notify an
appropriate medical specialist of these findings
in parallel to standard of care image
interpretation. 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 has been identified and
recommends review of those images. Images
can be previewed through a mobile application.
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 non-
compressed 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. ContaCT 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.

Methinks CTA Stroke is a radiological computer
aided triage and notification software, 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.
Methinks CTA Stroke uses an artificial
intelligence algorithm to analyze images for
findings suggestive of a pre-specified clinical
condition and to notify an appropriate medical
specialist of these findings in parallel to standard
of care image interpretation.
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 to PACS and/or 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 an image viewer.
Methinks CTA Stroke is intended to analyze
terminal ICA, MCA-M1 and MCA-M2 vessels for
LVOs.
Images that are previewed are for informational
purposes only and not intended for diagnostic
use beyond notification.
Notified clinicians are responsible for viewing
non-compressed 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. Methinks CTA Stroke 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.

User
Trained Clinicians
Trained Physicians

Input Data
CT Angiography images
CT Angiography images

Technology
Artificial intelligence/ Neural Network
AI/ML/Neural Network

The device does not highlight or direct a user’s
attention to a specific location in the image file.

The device does not mark, highlight, or direct
users’ attention to a specific location in the


Clinical SoC Workflow
In parallel to
In parallel to

Large Vessel Occlusion (LVO) Notification (one
output)


Parameter
Mean
95% CI Lower
95% CI Upper

Time to notification of CTA-LVO (minutes)

Table 2. Time to notification of Methinks CTA Stroke.


Gender
Measure
N
Estimate
Lower 95% CI
Upper 95% CI

Table 3. Performance metrics CTA-LVO by Gender.


Age
Measure
N
Estimate
Lower 95% CI
Upper 95% CI

Table 4. Performance metrics CTA-LVO by Age.


Table 5. Performance metrics CTA-LVO by vendor machine.


Table 6. Perf
ormance metrics
CTA-LVO by LVO
subgroup.

Table 7. Perf
ormance metrics
CTA-LVO by slice
thickness.

Slice thickness
Measure
N
Estimate
Lower 95%
CI
Upper 95%
CI

Table 8. Perfor
mance metrics CT
A-LVO by non-LV
O subgroup
.

Non-LVO
Measure
N
Estimate
Lower 95%
CI
Upper 95%
CI


Slice thickness
Measure
N
Estimate
Lower 95% CI
Upper 95% CI

Table 7. Performance metrics CTA-LVO by slice thickness.


Table 8. Performance metrics CTA-LVO by non-LVO subgroup.


Alphenix, INFX-8000V/B, INFX-8000V/S, V9.5

Marketed by
Canon Medical Systems USA, Inc.

Interventional Fluoroscopic X-ray System

Image-Intensified Fluoroscopic X-ray System


Device Name, Model
Alphenix, INFX-8000V/B,
Alphenix, INFX-8000V/B, INFX-8000V/S,

Number
INFX-8000V/S, V9.5
V9.6 with αEvolve Imaging

Stylish Design Cover
XGSD-120H/B1 (/BU
for upgrade)

Deep Learning Server
XIDF-DLS801 for
αEvolve Imaging


Indications for Use
Please type in the marketing application/submission number, if it is known. This
K251610 ?
textbox will be left blank for original applications/submissions.
Please provide the device trade name(s). ?

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

Please provide your Indications for Use below.

qER-CTA 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 the standard of care
workflow. qER-CTA uses a deep learning algorithm to analyze images for findings suggestive of a pre-
specified clinical condition and to notify an appropriate medical specialist in parallel to standard of care
image interpretation. Identification of suspected findings is not for diagnostic use beyond notification.
Specifically, the device analyses 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,
recommending review of those images. Images can be previewed through a mobile application. qER-CTA is
intended to analyze the internal carotid artery (ICA) and M1 segment of the middle cerebral artery (MCA) for
LVOs on CTA scans of adults (2:: 22 years of age). Images previewed through the mobile application are
compressed and for informational purposes only, not intended for diagnostic use beyond notification.
Notified clinicians are responsible for viewing non-compressed images on a diagnostic viewer, conducting
appropriate patient evaluation, and engaging in relevant discussions with the treating physician before
making care-related decisions or requests. qER-CTA is limited to the analysis of imaging data and should
not be used as a substitute for full patient evaluation or relied upon to make or confirm a diaQnosis.

Please select the types of uses (select one or both, as [gJ Prescription Use (Part 21 CFR 801 Subpart D)
D
applicable). Over-The-Counter Use (21 CFR 801 Subpart C)


Radiological computer-assisted triage and notification software


Radiological computer aided triage and notification software

Regulation Number
21 CFR 892.2080
21 CFR 892.2080

Manufacturer
Viz.ai, Inc.
Qure.ai Technologies Pvt. Ltd.

Viz LVO is a notification-only, parallel workflow tool designed
for use by hospital networks and trained clinicians to identify
and communicate images of specific patients to a specialist,
independent of the standard of care workflow. Viz LVO uses
an artificial intelligence algorithm to analyze images for
findings suggestive of a pre-specified clinical condition and to
notify an appropriate medical specialist of these findings in
parallel to standard-of-care image interpretation. The
identification of suspected findings is intended solely for
notification purposes and not for diagnostic use beyond this
notification. Specifically, the device analyzes CT angiogram
images of the brain acquired in acute settings and sends
notifications to a neurovascular specialist when a suspected
large vessel occlusion (LVO) has been identified. It also
recommends a review of those images. Images can be
previewed through a mobile application. Viz LVO is intended
to analyze the terminal ICA and MCA-M1 vessels for LVOs.
Images previewed through the mobile application are

qER-CTA 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 the standard of care workflow. qER-CTA uses
a deep learning algorithm to analyse images for findings
suggestive of a pre-specified clinical condition and to notify an
appropriate medical specialist in parallel to standard of care
image interpretation. Identification of suspected findings is
not for diagnostic use beyond notification. Specifically, the
device analyses 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, recommending review of those images. Images can
be previewed through a mobile application. qER-CTA is
intended to analyse the internal carotid artery (ICA) and M1
segment of the middle cerebral artery (MCA) for LVOs on CTA
scans of adults (≥ 22 years of age). Images previewed through
the mobile application are compressed and for informational

Table 1 Comparison between qER-CTA and the Predicate Device


compressed and are for informational purposes only. They
are not intended for diagnostic use beyond notification.
Notified clinicians are responsible for viewing non-
compressed images on a diagnostic viewer and engaging in
appropriate patient evaluation and relevant discussions with
the treating physician before making care-related decisions or
requests. Viz LVO is limited to the analysis of imaging data and
should not be used in lieu of a full patient evaluation or relied
upon to make or confirm a diagnosis.

purposes only, not intended for diagnostic use beyond
notification. Notified clinicians are responsible for viewing
non-compressed images on a diagnostic viewer, conducting
appropriate patient evaluation, and engaging in relevant
discussions with the treating physician before making care-
related decisions or requests.qER-CTA is limited to the analysis
of imaging data and should not be used as a substitute for full
patient evaluation or relied upon to make or confirm a
diagnosis.

Neurovascular specialists, such as vascular neurologists,
neuro-interventional specialists, or users with similar training
who have been pre-authorized by their Healthcare
Organization or Facility.

Neurologists, neuroradiologists, radiologists, neuro-
interventional specialist, and/or other emergency
department physicians.

Head Computed Tomography Angiography (CTA)

Target clinical conditions
Large Vessel Occlusion
Large Vessel Occlusion

Algorithm for pre-
specified critical findings
detection

Image processing algorithms for large vessel occlusion

Performance level –
accuracy of classification


Table 2 Standalone Performance Testing Results for qER-CTA


Time to Notification of
Specialist for LVO cases (mins)


subject and predicate device are medical image analyzers intended to read head CTA scans to classify

user identified large vessel occlusion. The algorithms function similarly and with the same purpose of

classification of large vessel occlusion

The comparison in Table 1 as well as the software & performance testing presented above


MEDICREA INTERNATIONAL S.A.S. (MEDTRONIC)
5389 Route de Strasbourg – Vancia
Rillieux-la-Pape, 69140
France
Phone : 00 33 4 72 01 87 87

Cécile HUMBERT
Sr Regulatory Affairs Specialist
MEDICREA INTERNATIONAL S.A.S. (MEDTRONIC)

Common Name Automated radiological image processing
software
Primary Product Code QIH
Associated Product Code LLZ
Regulation Number 21 CFR 892.2050
Regulation Name Medical image management and processing
system
Class 2

Device name UNiD™ Spine Analyzer
510(k) information K212005, cleared on 01/12/2022
Common Name system, image processing, radiological
Product Code LLZ
Regulation Number 21 CFR 892.2050
Regulation Name Medical image management and processing
system
Class 2

The UNiD™ Spine Analyzer is a web-based application developed to perform preoperative and postoperative
patient image measurements and simulate preoperative planning steps for spine surgery. It aims to make
measurements on a patient image, simulate a surgical strategy, draw patient-specific rods or choose from a
pre-selection of standard implants. The UNiD™ Spine Analyzer allows the user to:
1. Measure radiological images using generic tools and “specialty” tools
2. Plan and simulate aspects of surgical procedures
3. Estimate the compensatory effects of the simulated surgical procedure on the patient’s spine
The planning of surgical procedures is done by Medtronic as part of the service of pre-operative planning.
The surgical plan may then be used to assist in designing patient-specific implants. Surgeons will have to
validate the surgical plan before Medtronic manufactures any implant.
The UNiD™ Spine Analyzer interface is accessible in either standalone mode or connected mode.


The UNiD™ Spine Analyzer is intended for assisting healthcare professionals in viewing and
measuring images as well as planning orthopedic surgeries. The device allows surgeons and
service providers to perform generic, as well as spine related measurements on images, and
to plan surgical procedures. The device also includes tools for measuring anatomical
components for placement of surgical implants. Clinical judgment and experience are
required to properly use the software.

VI. Comparison of technological characteristics

The subject device has similar fundamental scientific technology, overall design, features, computer
configuration characteristics, intended use, and indications as the predicate device. The subject device and
the predicate device are intended to assisting healthcare professionals in viewing and measuring images as
well as planning orthopedic surgeries.
The subject device and the predicate device are compared in the table below.
Subject Device Predicate
Feature/ Attribute
UNiD™ Spine Analyzer 5.0.0 UNiD™ Spine Analyzer 4.0.0
Product code(s) QIH, LLZ LLZ
Regulation number 892.2050 892.2050
Classification 2 2
Intended use / The UNiD™ Spine Analyzer is intended for The UNiD™ Spine Analyzer is intended for
Indications for use assisting healthcare professionals in assisting healthcare professionals in
viewing and measuring images as well as viewing and measuring images as well as
planning orthopedic surgeries. The device planning orthopedic surgeries. The device
allows surgeons and service providers to allows surgeons and service providers to
perform generic, as well as spine related perform generic, as well as spine related
measurements on images, and to plan measurements on images, and to plan
surgical procedures. The device also surgical procedures. The device also
includes tools for measuring anatomical includes tools for measuring anatomical
components for placement of surgical components for placement of surgical
implants. Clinical judgment and experience implants. Clinical judgment and experience
are required to properly use the software. are required to properly use the software.
Fundamental scientific
Standalone software Standalone software
technology
Computer PC Compatible PC Compatible
Operating System Windows + Mac Windows + Mac
Supply mean Cloud-based Cloud-based
Health Data Host AWS AWS
Frontend code Angular 14 & Angular JS 1.7 Angular JS 1.7
Backend code .NET 8.0 .NET 4.8
Databases server SQL Server MySQL
Human intervention Required for interpretation and Required for interpretation and
manipulation of images manipulation of images
Supported image
JPEG; PNG; GIF JPEG; PNG; GIF
format
Access mode Standalone or connected mode Standalone or connected mode
Feature Contains all image setting tools (contrast; Contains all image setting tools (contrast;
- Image setting brightness; zoom in/out; flip, rotate, text brightness; zoom in/out; flip, rotate, text
overlay, calibration) overlay, calibration; intermodal
calibration)
Intermodal calibration has been removed
as part of a bug fix.


Contains all generic measurements tools
(Line; circle; angle; measured, simulated,
and normative values display; values color-
coding)
Color-coding thresholds are reworked but
the operating mode is similar.

Contains all spine-related measurements
tools (Llordo; TKypho; pelvic; T1SPi; SVA;
SA Analysis; Sagittal wizard; Coronal
wizard; Transitional anatomy (as part of
Sagittal wizard); Cervical; Lenke
classification; Coronal correction factor)
Sagittal Wizard, SA Analysis, Cervical tool
and Lenke tool are reworked for
improvement and adding new calculations,
but the operating mode remains similar.
The Guide Spline tool has been removed as
part of a bug fix.

Contains all spine-related measurements
tools (Llordo; TKypho; pelvic; T1SPi; SVA;
SA Analysis; Sagittal wizard; Coronal
wizard; Transitional anatomy (as part of
Sagittal wizard); Cervical; Lenke
classification; Guide spline; Coronal
correction factor)

Contains surgical tools to simulate surgical
gestures (wedge; open; resect; spondy;
wedge auto; open auto; resect auto)

Contains surgical tools to simulate surgical
gestures (wedge; open; resect; spondy,
wedge auto; open auto; resect auto)

Contains tools to place or draw rods,
screws or cages (Free Rod; cage; screw;
screw selection; Rod Auto; cage auto;
screw wizard; postop screw).
Some tools are reworked and relabeled.
Tool functionalities have been improved,
and bugs have been fixed, but there is no
change in output data of the reworked
tools.

Contains generic implants and tools to
place and/or draw rods, screws or cages
(UNiD Rod; cage; cage selection; screw;
screw selection; UNiD Rod Auto; cage auto;
screw wizard; postop screw).

Contains commercially available interbody
devices.
Some TLIF and ALIF implants have been
removed from the database.

Degenerative; Adult Deformity; Pediatric
Deformity
The Degenerative model is updated. The
functionality is similar. A new application is
added (LIV=L5)

Contains several export options (as image,
as ZIP file or to the Medtronic HUB).
The export options to the Medtronic HUB
are reworked (export default view with
transitional anatomy; link a rod design to a
rod order)


The following non-clinical performance data were provided in support of substantial
equivalence:
Software verification
The software verification is a combination of software code review, software unit test, unit
integration testing, and system level integration to ensure the software is developed
correctly and adheres to its design specifications. Software verification was conducted on the
UNiD™ Spine Analyzer in accordance with IEC 62304.
Software validation
The software validation was performed through user acceptance testing to ensure the
software satisfies the specified requirements and meets the user’s needs. It was conducted
on the UNiD™ Spine Analyzer in accordance with IEC 82304-1.
AI-enabled device software functions (AI-DSF)
The software incorporates AI-DSF, which give indication to the user about compensatory
mechanisms the patient could experience after the surgery according to the planned
instrumentation. Depending on the type of surgery (Adult, Pediatric or Degenerative), a
specific predictive model is available.
Outputs of these models are intended to be used as inputs of the software system to simulate
compensatory mechanisms after spinal fixation.
The subject device incorporates a new version of the Degenerative Predictive model
compared to the predicate device. This new version includes degenerative constructs with
Lower Instrumented level on L5 and predicts Lower non instrumented lordosis in these
situations.
Preoperative and post operative images from 1050 patient surgery cases were collected from
different clinical sites in the US only.
After the images were collected, they were then provided to and measured by highly trained
Medtronic measurement technicians, operating within a quality-controlled environment. The
viability of images for use is vetted by these technicians before beginning surgical planning.
Consequently, the manufacturer of imaging equipment does not have any impact on
performance.
Ground truth was derived from the measured images.
Each patient case was assigned a unique identifier and included only in either the testing or
the training dataset, in order to ensure the independence of both datasets.
The demographic characteristics for the global dataset are presented in the table below.
Training set (No. of Testing set (No. of Overall (No. of
Degen model
subjects=776) subjects=274) subjects=1050)
Mean age, years
62.4 (11.5) 61.0 (12.7) 62.0 (11.8)
(sd)
Gender, N (%)
Female 389 (50%) 127 (46%) 516 (49%)
Male 387 (50%) 147 (54%) 534 (51%)
Table 2: Degenerative Model Demographics global dataset
Non-inferiority of the subject device vs the predicate device was evaluated: one-tailed paired
T-tests for non-inferiority were performed between the MAEs obtained with the subject


The demographic characteristics for the global dataset are presented in the table below.


device and the ones obtained with the predicate device. The acceptance criterion for these
statistical tests were set to alpha of 0.025, and at least 90% power.
The results from the degenerative predictive model performance testing met the defined
acceptance criterion. The model showed non-inferiority compared to its predicate and is
considered acceptable for use.
Cybersecurity testing
The cybersecurity testing was conducted on the UNiD™ Spine Analyzer in accordance with
ANSI AAMI SW96 and IEC 81001-5-1 to ensure the software integrity, confidentiality and
availability. Activities performed as part of cybersecurity testing included: security risk
assessment and threat modeling, vulnerability assessment, and penetration testing.
Usability evaluation
Usability of the UNiD™ Spine Analyzer user interface was evaluated according to IEC 62366-
1. The purpose was to assess the software ergonomics and ensure no usability issues could
raise significant risks in terms of safety and effectiveness.

No clinical testing was used in order to support this submission.

Based on the information contained in this submission, the subject UNiD™ Spine Analyzer v5.0.0 device is
substantially equivalent to the following predicate device:
− Predicate 1 (Primary Predicate): UNiD™ Spine Analyzer v4.0.0 (K212005, S.E. 01/12/2022)


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

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

ITX
IYO
OBJ*
QIH
*Applicable only to Philips
EPIQ Series Diagnostic
Ultrasound System, per
clearance under K202216;
Not applicable for Philips
Affiniti Series Diagnostic
Ultrasound System

Identical.
The EMI filter is not
associated with
product code QIH.


Diagnostic ultrasonic
transducer
Ultrasonic pulsed echo
imaging
system
Diagnostic intravascular
catheter
Automated Radiological
Image
Processing Software

Limited (≤ 24 hours)
Limited (≤ 24 hours)
Identical

Introduction of the
optional spare part
Custom EMI Filter
Module

Image appears clean without
any Image artifacts potentially
caused by the external
hospital environments such as
bad non isolated grounding.

Visible imperfections or
distortions in an Ultrasound
image that are a result of
external interference. These
artifacts can be caused by
external factors such as
conductive noise induced by the
electrical powerline or poor
electrical grounding can affect
the overall quality of the image.

The proposed
device has better &
cleaner Image over
Predicate Device
when image
artifacts may arise
due to external
hospital
environmental
factors, such as
poor or non-
isolated grounding.
Verification and
Validation Testing
has been
conducted with
Custom EMI Filter
on the EPIQ &
Affiniti Systems to
support
Clear Image
without any Image
artifacts.

Custom EMI filter
doesn’t involve the
user interface of
the system


Assessed resistance to RF
fields (420–470 MHz)

Voltage Interruptions
Simulated power fluctuations
Passed


Indications for Use
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Please provide the device trade name(s). ?

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Careverse CoronaryDoc (Careverse CoronaryDoc)

Please provide your Indications for Use below.

Careverse CoronaryDoc is a web-based software application that is intended to be used by trained medical
professionals as an interactive tool for viewing and analyzing cardiac computed tomography (CT) data for
determining the presence and extent of coronary plaques (i.e., atherosclerosis) and stenosis in patients who
underwent Coronary Computed Tomography Angiography (CCTA) for evaluation of CAD or suspected
CAD. This software post processes CT images obtained using any Computed Tomography (CT) scanner.
The software provides tools for the measurement and visualization of coronary arteries.
The software is not intended to replace the skill and judgment of a qualified medical practitioner and should
only be used by people who have been appropriately trained in the software's functions, capabilities and
limitations.

Please select the types of uses (select one or both, as ~ Prescription Use (Part 21 CFR 801 Subpart D)
D
applicable). Over-The-Counter Use (21 CFR 801 Subpart C)


Item
Subject device, K251656
Predicate device (K202280)
Analysis

Regulation
21 CFR 892.2050
21 CFR 892.2050
Same

Careverse CoronaryDoc is a
web-based software application
that is intended to be used by
trained medical professionals as
an interactive tool for viewing
and analyzing cardiac computed
tomography (CT) data for
determining the presence and
extent of coronary plaques (i.e.,
atherosclerosis) and stenosis in
patients who underwent
Coronary Computed
Tomography Angiography
(CCTA) for evaluation of CAD
or suspected CAD. This
software post processes CT
images obtained using any
Computed Tomography (CT)
scanner. The software provides
tools for the measurement and
visualization of coronary
arteries.
The software is not intended to
replace the skill and judgment
of a qualified medical
practitioner and should only be
used by people who have been
appropriately trained in the

Cleerly Labs is a web-based
software application that is
intended to be used by trained
medical professionals as an
interactive tool for viewing and
analyzing cardiac computed
tomography (CT) data for
determining the presence and
extent of coronary plaques (i.e.,
atherosclerosis) and stenosis in
patients who underwent
Coronary Computed
Tomography Angiography
(CCTA) for evaluation of CAD
or suspected CAD. This
software post processes CT
images obtained using any
Computed Tomography (CT)
scanner. The software provides
tools for the measurement and
visualization of coronary
arteries.
The software is not intended to
replace the skill and judgment
of a qualified medical
practitioner and should only be
used by people who have been
appropriately trained in the

6. Comparison of Technological Characteristic with the Predicate Device


software’s functions,
capabilities and limitations.
Users should be aware that
certain views make use of
interpolated data. This is data
that is created by the software
based on the original data set.
Interpolated data may give the
appearance of healthy tissue in
situations where pathology that
is near or smaller than the
scanning resolution may be
present.

Image Input
DICOM 3.0 Compliant
DICOM 3.0 Compliant
Same

Study Analysis
Tools –
Editing/
Visualization


Stenosis Grade
n
m
Agreement
Lower 95% CI
Higher 95% CI


Output
Pearson Correlation Coefficient
Bland-Altman Agreement


Predicate #
Predicate Trade Name
Product Code

BD Prevue™ II Peripheral
Vascular Access System


Imaging Applications
Exam Type (Adult and Pediatric)

Assessment of vessels in the extremities and neck leading to or
coming from the heart, superficial veins in the arms and legs,
and vessel mapping. Assessment of superficial thoracic vessels.

Guidance for PICC, CVC, dialysis catheter, port, PIV, midline,
arterial line placement, access to fistula and grafts, and general
vein and artery access

Typical examinations performed using the BD Prevue™ II System include:


Model Training and Testing
The MuscleView 2.0 – Setting 1 model underwent supervised machine learning using a curated collection of
retrospective MRI datasets from a diverse population (n = 1294 unique subjects). Each dataset was labeled
through a consensus process by expert segmentation analysts to establish ground truth.
The model was tested on diverse datasets (n = 148 unique subjects) independent from the training datasets and
blinded to ground truth segmentations. Performance results demonstrated segmentation accuracy within the
interobserver variability range observed among experts.

Model Settings
All AI/ML inferences are performed automatically, with no configuration, calibration or exposed modifiable
parameters. The model behavior is locked and deterministic.

Privacy
Patient data is kept within the user’s network and is not used to train or test future models.


Attribute
Subject Device
Predicate Device
Reference Device
Comment

Device Name
MuscleView 2.0
MuscleView 1.0
AMRA Profiler

Manufacturer
Springbok, Inc.
Springbok, Inc.
AMRA

Product Code
LNH
LNH
LNH
No di(cid:431)erence


MuscleView is a
magnetic resonance
diagnostic software
device which
automatically segments
muscle, bone, fat and
other anatomical
structures from
magnetic resonance
imaging. After
segmentation, it
enables the generation,
display and review of
magnetic resonance
imaging data. Other
physical parameters
derived from the images
may also be produced.
When interpreted by a
trained clinician, these
images and physical
parameters may yield
information that may
assist in diagnosis.

MuscleView is intended for
use in adults and pediatric
patients aged 18 years and
older to automatically
segment muscle and bone
structures of the lower
extremities from magnetic
resonance imaging using a
machine learning-based
approach. Following
segmentation, it provides
derived metrics including
muscle volume, bone
volume, intramuscular fat
percentage, and left/right
asymmetry. The software is
intended to be used by
physicians trained in the
interpretation of MRI
images and serves as an
initial method for
segmenting muscle and
bone structures from one
or more study series.
Segmentation results must
be reviewed and, if
necessary, edited using
appropriate software.
MuscleView is intended
solely to provide
segmentation and derived
metrics for muscle and
bone structures and is not
intended to directly
support the diagnosis of
any disease. This device is
not intended for use in
patients with tumors in the
lower limbs.

Indicated for use as a
magnetic resonance
diagnostic device software
application for non-
invasive fat and muscle
evaluation, this device
enables the generation,
display, and review of 2D
magnetic resonance
medical image data. It is
designed to utilize DICOM
3.0 compliant magnetic
resonance image datasets
acquired from compatible
MR systems to display the
internal structure of the
body, including the liver.
Other physical parameters
derived from the images
may also be produced. The
software provides a
number of quantification
tools, such as Region of
Interest (ROI) placements,
to be used for the
assessment of regions
within an image to quantify
liver tissue characteristics,
including the
determination of fat
fraction in the liver, T2*,
and muscle volume. These
images and the physical
parameters derived from
them, when interpreted by
a trained clinician, yield
information that may assist
in diagnosis.

The subject device
expands upon the
capabilities of the
predicate device by
supporting
segmentation and
analysis of a broader
range of anatomical
regions of interest
(ROIs), including ROIs in
the upper body and
di(cid:431)erent types of
adipose tissue.
Furthermore, the
subject device includes
a comparative function
similar to that of the
reference device,
enabling comparison of
patient-specific metrics
to a virtual control
group.

Muscles, bones and fat
structures in the legs,
trunk and arms.

Muscles, bones and fat
structures in the legs.

Intended Users
Trained clinicians
Trained clinicians
Trained clinicians
No di(cid:431)erence

Adults and pediatric
patients aged 18 and
older

Tumors in the trunk,
arms and/or lower limbs

Subject device
contraindications reflect
the expanded
indications for use from
the predicate device.

MRI Scanners
GE, Siemens, Philips
GE, Siemens, Philips
GE, Siemens, Philips
No di(cid:431)erence


Factor
Groups
AI Setting 1
AI Setting 2 & 3

(Lower Extremity)
(Upper extremity and Adipose Tissue)

Training data
Validation data
Training data
Validation data

Ethnicity
(based on regional
demographics)


D
escrip(cid:415)ve
Sta(cid:415)s(cid:415)cs (
DSC) 95%
Confidence
Interval

Patholog
y Level
MRI
Manufactu
rer
Age/Biolo
gic Sex

ROI
Subgroup
Analysis
Subgr
oup Analy
sis
Subgroup
Analysis

Healthy
Pa(cid:415)ent
Canon
GE
Philips
Siemens
Toshiba
Male 18-21
Male Over 21
Female 18-21 F
emale Over


D
escrip(cid:415)ve
Sta(cid:415)s(cid:415)cs (DSC)
95% Co
nfi
dence
Interval

Patholog
y Level
MRI
Manufacturer
Age/Biolo
gic Sex

RO
I
Subgroup
Analysis
Subg
roup Analysis
Subgroup
Analysis

Healthy
Pa(cid:415)ent
Canon
GE
Philips Siem
ens T
oshi
ba
Male 18-21
Male Over 21
Female 18-21 Fe
male Over 2

Table 2: T
he 95% c
onfidenc
e intervale
for the RO
Is in AI se
ttings 1-3 for th
e absol
ute
volu
me di(cid:431)eren
ce metri
c.

Descrip(cid:415)v
e Sta(cid:415)s(cid:415)cs (DSC
) 95% C
on
fidenc
e Interval

RO
I
Patholo
gy Level
MR
I Manufacturer
Age/Bio
logic Sex

Subgroup
Analysis
Sub
group Analysis
Subgroup
Analysis

Healthy
Pa(cid:415)ent
Healthy
GE
Healthy Sie
mens
Hea
lthy
Healthy
Healthy


D
escrip(cid:415)ve
Sta(cid:415)s(cid:415)cs (
DSC) 95%
Confidenc
e Interval

ROI
Pathology
Level
MRI
Manufactu
rer
Age/Biolo
gic Sex

Subgroup A
nalysis
Subg
roup Analy
sis
Subgroup A
nalysis

Healthy Pa(cid:415)ent Healthy GE Healthy Siemens Healthy Healthy Healthy


R
OI
Patholo
gy Level
MRI
Manufact
urer
Age/Biol
ogic Sex

Subgroup
Analysis
Sub
group Anal
ysis
Subgroup
Analysis

Healthy
Pa(cid:415)ent
Healthy
GE
Healthy
Siemens Healthy
Healthy
Healthy

Pathology Level MRI Manufacturer Age/Biologic Sex


Modification
Rationale
Modification Protocol
Impact Assessment

#1 - Retraining the AI/ML
model for one or more ROIs
using additional training
data

To improve robustness and
generalizability of the AI/ML
model by training on
additional annotated
datasets.

Retraining of the AI/ML
model with a mixture of old
and new test datasets,
followed by performance
testing to compare the
retrained model to the
original version.

Benefit: Improved model
generalizability across
diverse cases.
Risk: Introduction of bias,
overfitting
Mitigation: Testing data
sequestration and testing on
new data will ensure proper
evaluation and mitigate risks
of overfitting.

To improve robustness and
generalizability of the AI/ML
model used to identify
anatomical regions by
modifying a predefined set
of training hyperparameters
and pre/post processing
methods during re-training.

Adjustment of preprocessing
parameters using existing
datasets, followed by
performance verification
against baseline metrics to
ensure output consistency.

Benefit: Improved model
generalizability across
diverse cases.
Risk: Introduction of bias,
overfitting
Mitigation: Predefined
parameter boundaries and
comparison to the original
metrics mitigate risks of
overfitting.

#3 - Addition of data points
to the Virtual Control Groups

To improve the statistical
significance of the Virtual
Control Group (VCG)
outputs used to generate
scored metrics.

Recalculation of linear
regression equations using
additional subject data,
followed by performance
evaluation to ensure
equivalent or improved
correlation strength.

Benefit: Improved predictive
accuracy
Risk: Introduction of bias
error due to skewed
sampling
Mitigation: Data collection
protocols align with those
contained within this
submission

MuscleView AI/ML models will be made in accordance with the PCCP summarized below:


Characteristic
Primary Predicate
Subject VEA Align
Substantially Equivalent?

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.

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 and degenerative
diseases, and 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.

VEA Align is contraindicated for cases with
vertebrae with severe congenital deformities
(e.g., hemivertebrae, spina bifida, etc.) and
supernumerary/missing vertebrae.

VEA Align is contraindicated for cases with
vertebrae with severe congenital deformities
(e.g., hemivertebrae, spina bifida, etc.).

Similar
The change to the
contraindications simply reflects an
enhancement of the software
flexibility without affecting its core
design, safety, or intended use.

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


Automated Radiological Image Processing
Software

Operating System
Windows + MAC
Windows + MAC
YES

VEA Align Alignment Assessment mode is
designed for surgeons and clinical staff, such as
physician assistants, who have been trained to
use the application.
VEA Align 3D mode is designed for 3D Services
team members.

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

Includes landmarks associated with vertebral
endplates needed to calculate coronal and
sagittal clinical parameters. These landmarks
are adjustable by the user.

Includes landmarks associated with the pelvis
and lower limbs needed to calculate coronal and
sagittal clinical parameters. These landmarks
are adjustable by the user.

Provides normative values used to assess
patients’ global alignment

Provides color-coded clinical parameters to
display variance from the defined normative
values

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


Characteristic
Primary Predicate
Subject VEA Align
Substantially Equivalent?

Patient-specific clinical parameters and
calculations based on published literature.

Alignment mode: 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.
3D mode: The 3D reconstruction model is
deformed manually by the user through control
points up to matching accurately the X-ray
contours.

Human
Intervention for
interpretation and
manipulation of
images


Characteristic
Primary Predicate
Subject Device
Substantially Equivalent?

• With Windows 10 or 11: Google Chrome in
version 121 or higher, and Edge in version
121 or higher.
• With Mac OS Monterey or Ventura: Google
Chrome in version 121 or higher.
PC Configuration:
A stable high speed internet connection is
required: DSL 100Mb/s connection or higher,
Wi-Fi or Ethernet.
Screen Resolution:
The minimum screen resolution ensuring full
display of the interface is 1366 x 768.

• With Windows 10 or 11: Google Chrome in
version 130 or higher, and Edge in version
130 or higher.
• With Mac OS 15 Sequoia: Google Chrome in
version 130 or higher.
PC Configuration:
A stable high speed internet connection is
required: DSL 100Mb/s connection or higher,
Wi-Fi or Ethernet.
Screen Resolution:
The minimum screen resolution ensuring full
display of the interface is 1366 x 768.

compatible web browsers and
Mac Operating System.

Patient’s information
X-rays images
3D landmarks of the spine generated from VEA
Align

3D spine reconstruction confirmation: allow
users to accept the 3D landmark input data or
reject the 3D landmark input data and provide
feedback on corrective adjustments.
Preoperative: allow user to consult the
preoperative state of the patient
Planning: allow user to define patient specific
surgical strategy
Rod: allow user to define the design of rods

3D spine and sacrum model reconstruction
confirmation: allow users to accept the 3D
landmark input data or reject the 3D landmark
input data and provide feedback on corrective
adjustments.
Preoperative: allow user to consult the
preoperative state of the patient
Planning: allow user to define patient specific
surgical strategy
Rod: allow user to define the design of rods
The product includes two workflows according
to the patient’s age.
• Pediatric workflow, for patient under 18 years
old: The user has access to the 3D spine
reconstruction confirmation and the
Preoperative steps

Similar
The workflow steps are identical
compared to the predicate. The
pediatric workflow prevents the
user to have access to the
planning and rod steps.
However, the user still has
access to the 3D spine
reconstruction confirmation and
the preoperative steps, with all
the image manipulation
functions available in these
steps.


• Adult workflow, for patient above 18 years
old: The user has access to all the steps
described above

• Segmental Alignment
• Interbody Implant
• Osteotomy
• Spondylolisthesis
• Rod Curvature Management (From T1 to S1)

• Segmental Alignment
• Interbody Implant
• Osteotomy
• Spondylolisthesis
• Rod Curvature Management (From T1 to S2)

Similar
Modified spineEOS allows the
user to design a patient-specific
rod until S2 vertebrae based on
the landmarks generated by
VEA Align.

Obtains an image and 3D model transferred
from other devices.

Provides normative values used to follow the
impact of the planning on the patient
alignment.

Provides color-coded clinical parameters to
display variance from the defined normative
values.

Requires human intervention for
interpretation and manipulation of images.

2D images and 3D spine display and basic
manipulation (zoom, panning, angles
measurements, plumbline, and possibility to
add comments on the image).

• Pelvic Tilt (PT)
• Sacral Slope (SS)
• Pelvic Incidence (PI)
• Pelvic Obliquity (PO)
• Sagittal Vertical Axis (SVA)
• C7-CSL
• PI-LL
• T1 Pelvic Angle (TPA)

Similar
Except for the addition of L1PA,
T4PA, the axial rotation, disc
height and disc segmental lordosis.
L1PA and T4PA measure the
same angle but taking respectively
L1 and T4 instead of T1. The


Characteristic
Primary Predicate
Subject Device
Substantially Equivalent?

• Cobb Angle
• Kyphosis/Lordosis Angle
• Knee Flexion/Extension Angle
• Lordosis Percentage Distributions
• Spondylolisthesis grade (i.e., Slippage
percentage)
• L1 Pelvic Angle (L1PA)
• T4 Pelvic Angle (T4PA)
• Axial rotation (not computed)
• Disc height (anterior and posterior)
• Disc segmental lordosis

subject device uses the same
validated point as the predicate.
The axial rotation is displayed
based on the input data received
from VEA Align. It is not computed
at the planning step.
Disc height and disc segmental
lordosis are new clinical
parameters linked to the disc.
However, they aim to calculate
distances (height) and angles
(lordosis), same as the other
existing clinical parameters.


Predicate Device (K242461)
Subject Device (K251763)

Intended to receive DICOM compliant contrast-
enhanced CT images, provide manual and machine
learning-enabled tools for image analysis and
segmentation, and creates an output file that can
be used to render a 3D model for preoperative
surgical planning and intraoperative display.

SIMILAR Indications for use:
Added MR indication for image analysis
and non-ML manual segmentation of
DICOM compliant MR images.

Comparison of Indications for Use and intended Use


Description
Predicate Device (K242461)
Subject Device (K251763)

Regulation Number
21 CFR §892.2050
21 CFR §892.2050

Product Code
Primary: QIH; Associate: LLZ
Primary: QIH; Associate: LLZ

Intended population
Adult patients (ML algorithm)
Same

Intended Users
Qualified Professionals
Same


The output file can be used to
render a 3D model for
preoperative surgical planning
and intraoperative display.
The output file is meant for
visual, non-diagnostic use and
shall be reviewed by clinicians
who are responsible for all final
patient management decisions.

Label names, label color
parameters and label name
translation for kidney CT scans

Equivalent to the predicate
device
DIFFERENCE: Updated label
names, label color parameters
and label name translation for
MR scans

Manual segmentation alone
Auto-segmentation followed by
manual segmentation.

Equivalent to the predicate
device
DIFFERENCES: Manual
segmentation only for MR scans

Graphical user interface design.
Office setting (Segmentation
Software Application running on
a general-purpose computer)

DICOM-Compliant CT scans
(Axial views, contrast enhanced)

Equivalent to the predicate
device
DIFFERENCES: addition of
DICOM compliant MR scans

Segmentation files that can be
used to render a 3D model for
preoperative surgical planning
and intraoperative display

Kidney CT structures:
• ML auto-segmentation -
Parenchyma, Artery, Vein and
Collecting System
• Manual segmentation -
Parenchyma, Artery, Vein,
Collecting System and Mass

Same: Kidney CT structures
(Manual and ML auto-
segmentation)
DIFFERENCES (MR structures
for manual segmentation):
-Kidney MR structures
-Prostate MR structures
-Rectal MR structures


ML Auto-Segmentation
Structures and
performance

4 kidney CT structures:
Parenchyma, Artery, Vein and
Collecting System
Performance: Machine Learning
Auto-Segmentation Testing of
kidney CT scans.

Manual Tools and
manual segmentation
performance

Various segmentation and
selection tools for manual
segmentation.
Performance: Manual
segmentation testing of kidney
CT scans

Same
Same (kidney CT scans)
Equivalent manual
segmentation performance
for MR scans

Volume rendering, 3D model
visualization, 2D slice
visualization.


Training/Tuning Datasets
Validation Dataset

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


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

Tumor Volume (n=218)
cubic centimeters (cc)
5.2 ± 12.5

Tumor-to-breast volume ratio (n=218)
%
0.4 ± 1.2

Tumor longest dimension (n=242)
centimeters (cm)
1.32 ± 1.65

Tumor-to-nipple distance (n=241)
centimeters (cm)
1.17 ± 1.55

Tumor-to-skin distance (n=242)
centimeters (cm)
0.60 ± 0.52

Tumor-to-chest distance (n=242)
centimeters (cm)
0.86 ± 1.22

Tumor center of mass (n=218)
centimeters (cm)
0.60 ± 1.47

between expert readers. Performance data for the automated measurements is summarized below:


Tumor segmentation (n=218)
Volumetric Dice
0.76 ± 0.26

Results of Dice and surface Dice are summarized below:


Predicate Device
(TumorSight Viz – version 1.2)

Subject Device
(TumorSight Viz – version 1.3)

Manufacturer
SimBioSys, Inc.
SimBioSys, Inc.

Medical image management and
processing system

Device Common Name
Image Processing System
Image Processing System

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

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


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

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

"-non-invasive software package
-DICOM compatible"

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


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

Interactive rotation of 3D
volume rendering


Performance N Metric Predicate/ TumorSight Predicate/ Inter-

Measurement TumorSight Viz/ Ground radiologist

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


Clinical Aspect
Submitted Device
Primary Predicate Device

RevealDx
RevealAI-Lung
(This Submission)

Optellum Ltd.
Optellum™ Software
(K202300)

§892.2060 Radiological
computer-assisted diagnostic software
for lesions suspicious of cancer.
A radiological computer-assisted
diagnostic software for lesions
suspicious of cancer is an image
processing prescription device intended
to aid in the characterization of lesions
as suspicious for cancer identified on
acquired medical images such as
magnetic resonance, mammography,
radiography, or computed tomography.
The device characterizes lesions based
on features or information extracted from
the images and provides information
about the lesion(s) to the user.
Diagnostic and patient management
decisions are made by the clinical user.

RevealAI-Lung Software is a computer
aided diagnostic (CADx) software
application intended for the
characterization of incidentally-detected
lung nodules on computed tomography
(CT) scans. When a nodule is identified,
the Software automatically compares the
nodule characteristics with a clinically
established database of lung nodules
and provides a similarity score to assist
clinicians’ assessment of patients’
cancer risk.
The mSI score is indicated for the
evaluation of incidentally-detected
pulmonary nodules of diameter 6-15mm
in patients aged 18 years or above. In
cases where multiple abnormalities are
present, the mSI score can be used to
assess each abnormality independently.
Risk should be interpreted on an
individual patient level and mSI is a

Virtual Nodule Clinic (VNC) is a
software device used in the
tracking, assessment and
characterization of incidentally
detected pulmonary nodules.
VNC includes a computer-aided
diagnosis (CADx) function,
available only to pulmonologists
and radiologists. This automatically
analyzes user-selected regions of
interest (ROI) within lung CT data
to provide volumetric and computer
analysis based on morphological
characteristics. Using only imaging
features extracted from the CT
image data, an artificial intelligence
algorithm calculates a single value,
the LCP-CNN score, which is
displayed to the user. The
LCP-CNN score is analyzed
relative to LCP-CNN scores
generated on a database of cases

Table 1 Predicate Comparison Chart - Clinical Aspects


relative risk score, not a percentage
cancer risk.
Note that mSI is not indicated for lung
cancer screening. The validation data
excluded CT images with missing slices.

with known ground-truth using a
histogram display format. The
LCP-CNN score may be useful in
the characterization of pulmonary
nodules during image interpretation
and may be used as one input to
clinical decision making when
following published clinical
guidelines.
VNC’s LCP-CNN score is indicated
for the evaluation of incidentally
detected solid and semi-solid
pulmonary nodules of diameter
5-30mm in patients aged 35 years
or above. In cases where multiple
abnormalities are present, VNC’s
LCP-CNN score can be used to
assess each abnormality
independently.
Note that LCP-CNN is not
indicated for lung cancer screening
nor is it indicated for nodules of
pure ground glass opacity. In
addition, high contrast CT images
were not used in clinical validation
(as measured as >300HU median
attenuation in the aortic arch) and
the validation data also excluded
CT images with only calcified
nodules (since these are typically
considered to be benign), with
implants, motion artifacts, missing
slices, or cases with greater than 5
nodules. Finally, the validation data
excluded patients with history of
cancer of less than 5 years to
avoid the presence of metastatic
lesions.
Users other than radiologists and
pulmonologists, e.g. clinicians,
nurses, nurse practitioners and
navigators, may use VNC to view
CT images and reports, organize
patient management workflow,
track patients, record management


decisions and organize nodule
clinics. For these users, the LCP-
CNN score is unavailable.

The targeted population inclusion criteria
are based on those where lung nodules
are found on CT scans in clinical
practice, and where product validation
has occurred, specifically where lung
nodules have been identified incidentally
on CT.

Optellum Software is a software
device used in the tracking,
assessment, and characterization
of incidentally detected pulmonary
nodules.

● Not indicated for use on modalities
other than lung CT images
● Not indicated for use on
non-lung-nodule image locations
● Not indicated for unassisted reads
(device output requires practitioner
review)
● Not indicated for use where image
quality or the presence of surgical
or implanted artifacts obscures the
lung nodule
● Not indicated for use when CT
images were acquired at a slice
spacing exceeding 5mm

● The Optellum LCP Score is not
indicated for pure
Ground-Glass Opacities
(GGO) or for calcified nodules.
● The Optellum LCP Score is not
indicated for patients with a
history of cancer less than 5
years. The Optellum LCP
Score is not indicated for
nodules detected by lung
cancer screening studies. The
Optellum LCP Score is not
indicated for patients with more
than five pulmonary nodules.
● The Optellum LCP Score is not
indicated for patients with
thoracic implants that impact
the image appearance of the
nodule.
● The Optellum LCP Score is not
indicated for patients younger
than 35 years.

RevealAI-Lung Software is designed to
be used by radiologists who are licensed
to make diagnostic or other follow-up
decisions using radiological imagery
and/or radiology reports. The information
provided by RevealAI-Lung should only
be used in conjunction with other
clinically accepted information to guide
medical decision-making at the
discretion of the clinician.

A nodule clinic is typically led by a
pulmonologist and includes a
multi-disciplinary team of
radiologists, thoracic surgeon and
sometimes pathologist and
oncologist.

The Software is used in hospitals and
healthcare provider office settings.

Virtual Nodule Clinic is intended for
use in hospitals to support these
clinics.


Technical Features
Submitted Device
Primary Predicate Device

RevealDx
RevealAI-Lung
(This Submission)

Optellum Ltd.
Optellum™ Software
(K202300)

Regulation: 21 CFR 892.2060
Product Code: POK
Device Classification Name:
Computer-Assisted Diagnostic
Software for Lesions Suspicious for
Cancer

Increases the accuracy of malignancy
assessment

Reduces variations between
individual physicians

Improves clinical decision making
Improves clinical decision making

Has been independently clinically
validated in multi-center studies

Patient scan: The CT data and any
prior scans are automatically
uploaded into the RevealAI-Lung
software.

Patient scan: The CT and any prior
scans are automatically uploaded into
the Optellum Software.

Identify nodule: Easily review any
available CT and mark the nodule(s)
of interest.

RevealAI-Lung mSI score: Within
seconds, RevealAI-Lung analyzes
the CT image data to compute the
score.

Optellum Lung Cancer Prediction
score: Within seconds, the Optellum
Lung Cancer Prediction analyzes the
3D image region around the nodule to
compute the score.

Optimal clinical decisions: With
the support of the RevealAI-Lung
mSI score, make the optimal clinical
management decision for the
patient.

Optimal clinical decisions: With the
support of the Optellum LCP score,
make the optimal clinical management
decision for the patient.

Patient Contact Materials
None. No patient contact
None. No patient contact

Provides a computer-aided
diagnosis (CADx) function to assist
radiologists who are familiar with
lung nodule management in the
assessment and characterization of

Provides a computer-aided diagnosis
(CADx) function to assist
pulmonologists and radiologists in the
assessment and characterization of
incidentally detected pulmonary
nodules using CT image data.

Table 2 Predicate Comparison Chart - Technical Features


Technical Features
Submitted Device
Primary Predicate Device

RevealDx
RevealAI-Lung
(This Submission)

Optellum Ltd.
Optellum™ Software
(K202300)

Provides a score using machine
learning referred to as mSI or
malignancy similarity index.

Provides a score using machine
learning referred to as LCP-CNN
algorithm, or Lung Cancer Prediction
Convolutional Neural Network.

The RevealAI-Lung software is
deployed on a server on-premise at
the healthcare facility or within
cloud-based systems securely
connected to the facility network.

The LCP-CNN module is deployed on
a GPU-equipped server on hospital
premises or in the cloud.

Connected to two other IT systems
in the hospital: a DICOM-compatible
Picture Archiving and
Communication System (MIMPS) for
accessing images and to the
Radiology Information System (RIS)
or reporting system for accessing
the clinical reports.

RevealAI-Lung is used in hospitals,
imaging centers and other
radiologist reader settings where
diagnostic evaluation of CT images
occur.

Pulmonary nodule clinics are typically
set up in hospitals, so that nodules can
be followed up or clinical investigations
can be prescribed to confirm the
diagnosis associated with the
presence of pulmonary nodules. A
nodule clinic is typically led by a
pulmonologist and includes a
multi-disciplinary team of radiologists,
thoracic surgeon and sometimes
pathologist and oncologist. Virtual
Nodule Clinic is intended for use in
hospitals to support these clinics.

The CADx function automatically
analyzes user-selected lung nodules
from lung CT data. This function
extracts image data from the nodule
to provide 3D analysis and computer
analytics based on morphological
characteristics. These imaging (or
radiomic) features are computed
solely from the image extract and
then synthesized by an artificial
intelligence algorithm into a single
value, the mSI score, which is
displayed to the user. The score
displayed is an integer between 0
and 1 where 0 means very likely
benign and 1 means very likely

The CADx function automatically
analyzes user-selected regions of
interest (ROI) from lung CT data. This
function extracts image data from the
ROI to provide 3D analysis and
computer analytics based on
morphological characteristics. These
imaging (or radiomic) features are
computed solely from the image
extract and then synthesized by an
artificial intelligence algorithm into a
single value, the LCP- CNN score,
which is displayed to the user. The
score displayed is an integer between
1 and 10 where 1 means very likely
benign and 10 means very likely


malignant. The mSI score is not a
probability of malignancy but is
intended to be analyzed relative to
mSI scores generated on a
database of cases with known
ground-truth, using a histogram
display format.
The mSI score, in combination with
other information may be used in the
characterization of pulmonary
nodules and may be used as one
input to clinical decision making
when following published clinical
guidelines.

malignant. The LCP-CNN score is not
a probability of malignancy but is
intended to be analyzed relative to
LCP-CNN scores generated on a
database of cases with known
ground-truth, using a histogram
display format.
The LCP-CNN score, in combination
with other information may be used in
the characterization of incidentally
detected pulmonary nodules and may
be used as one input to clinical
decision making when following
published clinical guidelines.

RevealAI-Lung is accessed via
integration with a MIMPS system for
radiologist use. Once generated,
results may be incorporated in
reporting at the discretion of the
radiologist.

Optellum Software is accessed via a
web browser on any PC or Apple
workstation in the hospital. The
software is validated with web
browsers Google Chrome, Mozilla
Firefox, Microsoft Edge, Microsoft
Internet Explorer and Apple Safari.

Input to RevealAI-Lung is a DICOM
CT image and a spatial coordinate
centered on a lung nodule.
Internally RevealAI-Lung creates 3D
views of the nodule location and
whole CT slice, and extracts
thousands of machine vision
features including layers of
transformations into frequency and
other signal processing domains.
Machine learning relates these
features to confirmed diagnoses of
input nodules.

CNN and input: The LCP-CNN
system is based on the Dense
Convolutional Network, a widely used
type of deep learning CNN
architecture that was designed for
computer vision tasks. LCP-CNN’s
ensemble of convolutional neural
network models ends with a fully
connected binary classification layer
(malignant or benign). Input to
LCP-CNN is a 3-dimensional crop of a
CT image, centered on a lung nodule.

Image Analysis
Algorithm:
Training Dataset

Training dataset: RevealAI-Lung
was trained on radiologist-identified
lung nodules from 4-30mm in
diameter, with data augmentation to
vary the spatial coordinate. The
median age of the subjects was 63
(55-74), with 43% of the subjects
female.
Only nodules that were confidently
matched to a definitive diagnosis
were used for training, including

Training dataset: The LCP-CNN
network was trained using solid and
semi-solid nodules of at least 5mm in
diameter. The training data consisted
of 8% malignant nodules and 92%
benign nodules and included both
screening (95%) and incidentally
detected (5%) nodules. The median
age of the subjects was 62 (20-90)
and included a mix of US (95%) and
EU data (5%). The data included
females (38%) and males (62%). Only


Technical Features
Submitted Device
Primary Predicate Device

RevealDx
RevealAI-Lung
(This Submission)

Optellum Ltd.
Optellum™ Software
(K202300)

following the patients for at least 5
years.

nodules that were confidently matched
to a definitive diagnosis, as provided
with the data, were used for training.

RevealAI-Lung provides an mSI
score for relative malignancy risk
with a scale of 0 to 1, with 0
representing lowest risk and 1
highest risk. The score is shown with
contextual information to relate mSI
to the training population’s confirmed
nodule diagnoses and risk levels
across the scale.

CNN output: LCP-CNN’s output is a
continuous value between 0 and 1;
this is mapped to an integer score
between 1 and 10. This mapping was
constructed by computing the raw LCP
score on a dataset consisting of
malignant (10%) and benign nodules
(90%) but which were not used during
the model training. The mapped
integer score is shown to the user
alongside a plot detailing the cancer
prevalence in each of 10 bins for a
population with a 30% cancer
prevalence.

Design Verification &
Validation: Nonclinical
Performance Testing

Standalone testing of RevealAI-Lung
demonstrated that it performed as
expected in discriminating between
benign and malignant nodules.
Technical validation is also
described for variation in nodule
coordinate and acquisition dose.

Standalone testing of the LCP-CNN
model demonstrated that it performed
as expected in discriminating between
benign and malignant nodules,

Design Verification &
Validation: Clinical
Performance Testing

Concurrent use of RevealAI-Lung
improved radiologists accuracy for
the diagnosis of pulmonary nodules
by an average of 18 points, from
0.54 to 0.72 (p < 0.001)
Every radiologist improved their
performance when using
RevealAI-Lung, and performance
was consistent across patient,
nodule and technical parameters.

● Concurrent use of the LCP-CNN
feature in Optellum Virtual Nodule
Clinic software to read CT exams
improves radiologists’ and
pulmonologists’ accuracy for the
diagnosis of pulmonary nodules by
an average of 6.85 AUC points (p
< .001) (from 81.9 to 88.8 AUC)
● Every radiologist and
pulmonologist improved their
accuracy when using the Optellum
LCP- CNN feature to assist their
read.


Every reader improved when using the device (range 0.11 - 0.26):


4. Legally Marketed Predicate Device
and Reference Devi
ce

Classification Name:
Magnetic Resonance
Diagnostic Devic

Classification Name:
Picture Archiving and
Communications