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

In-house measurements and collaboration partners.

Equipment
0.55T[1], 1.5T and 3T MRI scanners

Representative protocols (T1, T2 and PD with and without
fat saturation) which have been altered (e.g. to increase
SNR, increase resolution or reduced acceleration).

No clinical subgroups have been defined for the datasets.

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

No confounders have been defined for the datasets.

Test Statistics
and Test
Results
Summary

The impact of the network has been characterized by several
quality metrics such as peak signal-to-noise ratio (PSNR) and
structural similarity index (SSIM). Most importantly, the
performance was evaluated by visual comparisons to evaluate for
example, aliasing artifacts, image sharpness and denoising levels.
The quality metrics evaluation was performed by conventional
reconstruction of a set of test data (gold standard). This test data

Table 1. Training and validation dataset of Deep Resolve Boost


Predicate Device
FDA Clearance Number
Product
Manufacturer

Siemens Shenzhen
Magnetic Resonance Ltd.

Table 2. Predicate devices and reference devices


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

syngo MR XA80A
syngo MR XA80A
syngo MR XA60A
syngo MR

RF System
Yes, same as predicate device
Yes

Patient Table
Yes, same as predicate device
Yes

Yes, modified compared to predicate
devices:
-new high-end host computer hardware
-new syngo Workplace
-modified MaRS hardware

Yes, new coil
compared to
predicate device:
-Dental coil

Yes, modified compared to predicate
device:
-Select&GO display (TPAN 3G)


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

software syngo
software syngo
software syngo
software syngo

Yes, with new and modified features
compared to predicate devices:
-Slice Overlapping for TSE and
TSE_DIXON
- Deep Resolve for HASTE
- SPACE improvement with MTC prep
module

GRE-
based/Steady-
State Pulse
Sequence Types

Yes, with new pulse sequences
compared to predicate devices:
-GRE_PHS
-GRE_Proj

Yes, with new pulse sequences and
modified features compared to
predicate devices:
-Deep Resolve for EP2D_DIFF
-EP_SEG_PHS
-EP_SEG_FID_PHS
-EP2D_FID_PHS

myExam AutoPilot
Yes, same as predicate device
Yes

Yes, with new and
modified features:
-myExam Dental
Assist
-myExam RT Assist

Visualization
Yes, same as predicate device
Yes

Communication
Yes, same as predicate device
Yes

Yes, with new and modified features:
-Select&GO workflow extension
-Eco Power Mode
-Extended Gradient Eco Mode
-System Startup Timer


Medical electrical equipment -
Part 1: General requirements for
basic safety and essential
performance

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

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

Medical devices - Application of
risk management to medical
devices

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

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

Acoustic Noise Measurement
Procedure for Diagnosing
Magnetic Resonance Imaging
Devices

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

Digital Imaging and
Communications in Medicine
(DICOM) Set

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


Manufacturer
Artrya Ltd.
Caristo
Artrya Ltd.

Model Name
Salix Coronary Plaque (V1.0.0)
CaRi-Plaque
Salix Central

Salix Coronary Plaque
(V1.0.0) is a web-based, non-
invasive software application
that is intended to be used for
viewing, post-processing, and
analyzing cardiac computed
tomography (CT) images
acquired from a CT scanner in
a Digital Imaging and
Communications in Medicine
(DICOM) Standard format.
This software provides
cardiologists and radiologists
with interactive tools that can
be used for viewing and
analyzing cardiac computed
tomography (CT) data for
quantification and
characterization of coronary
plaques (i.e. atherosclerosis),
stenosis and to perform
calcium scoring in non-
contrast cardiac CT
Salix Coronary Plaque
(V1.0.0) is intended to
complement standard care as
an adjunctive tool and is not
intended as a replacement to
a medical professional’s
comprehensive diagnostic
decision-making process. The
software’s semi-automated
features are intended for an

CaRi-Plaque is
intended to provide an
optimized non-invasive
application to analyze
coronary anatomy and
pathology and aid in
determining treatment
paths from a set of
Computed Tomography
(CT) Angiographic
images.
CaRi-Plaque is a web-
based image
processing application.
It is a non-invasive
diagnostic reading
software intended for
use as an interactive
tool for viewing and
analyzing cardiac CT
data for determining the
presence and extent of
coronary plaques and
luminal stenoses.
CaRi-Plaque is
intended for use by
internal operators who
have been appropriately
trained in the software's
functions, capabilities
and limitations.
Users should be aware

Salix Central is a web-
based software application
that is intended to be used
for viewing, post-
processing, and analyzing
cardiac computed
tomography (CT) images
acquired from a CT
scanner in a Digital
Imaging and
Communications in
Medicine (DICOM)
Standard format.
This software provides
tools that can be used for
the qualitative and
quantitative assessment of
physician-identified
coronary plaques and
stenosis in coronary
computed tomography
angiography (CCTA) and
to perform calcium scoring
in non-contrast cardiac CT.
Salix Central is intended to
complement standard care
as an adjunctive tool and is
not intended as a
replacement to a medical
professional’s
comprehensive diagnostic
decision-making process.
The software’s semi-


adult population and should
only be used by qualified
medical professionals
experienced in examining and
evaluating cardiac CT images.
Users should be aware that
certain views make use of
interpolated data. These
data are 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.

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 may
be present that is near
or smaller than the
scanning resolution.
The analysis results
produced by the
software and provided
to the Healthcare
Professional are not
intended to replace the
skill and judgment of a
qualified medical
practitioner. The
analysis results should
be reviewed with other
clinical information
which may include but
is not limited to: The
patient's original CT
images, clinical history,
symptoms, clinical risk
factors, results of other
diagnostic tests, and
the clinical judgement of
appropriately qualified
Healthcare
Professionals.

automated features are
intended for an adult
population and should only
be used by qualified
medical professionals
experienced in examining
and evaluating cardiac CT
images.

Cardiologists, Radiologists
and Clinical Specialists


DICOM Compliant
Yes; DICOM 3.0 or higher
DICOM 3
Yes; DICOM 3.0 or higher

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

2D measurement tools of
vessel diameter and
contour

Yes; this visualization method
not necessary for analyzing
the presence and extent of
coronary plaque and stenosis

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

Yes, MPR with oblique slicing
and variable slab thickness is
not necessary analyzing the
presence and extent of
coronary plaque and luminal
stenosis

Panning
Windowing
Zooming
Series, slices, and phases

Centerline
Wall
Signal Intensity Overlay

Measurements
Distance & Area
Area
Distance & Area

Manual and semi-automatic /
user-editable

Manual and semi-
automatic / user-editable


Manufacturer
Artrya Ltd.
Caristo
Artrya Ltd.

Model Name
Salix Coronary Plaque (V1.0.0)
CaRi-Plaque
Salix Central

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

Yes; Similar (categorical),
Manual & Editable*

Area stenosis:
No; Calculated based on
Yes
No


Semi-Automated
Yes, Editable
No
No, Manual & Editable

No, Presence/absence of
positive remodeling as a
classifier

No (categorized stenosis only
into CAD RADS)

Yes, Similar
Vessel Profile: maximum and
minimum diameter measures
from selected vessel cross
sections


Yes, Similar
Lumen Profile: maximum and
minimum diameter measures
from selected vessel cross
sections

Yes. Semi-automatic with full
option to manually edit with
output demonstrated to meet
pre-determined acceptance
criteria in clinical performance
study.

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

Deep learning based,
vessel & lumen only;
validated with ground truth
by clinician expert readers,
with full option to edit.


Salix Coronary Plaque
Statistic
Estimate
Acceptance
Result

Calcium Scoring
Pearson Correlation
0.958 [0.947, 0.966]
0.90
Pass

Centerline Extraction
Overlap score
0.8604 [0.8445, 0.8750]
0.80
Pass

Lumen Wall Segmentation
Dice Score
0.8996 [0.8938, 0.9055]
0.80
Pass

Vessel Wall Segmentation
Dice Score
0.9016 [0.8962, 0.9070]
0.80
Pass

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


Scintillator
material: LYSO
Number of
detector rings:
• 100
Axial FOV:
• 295 mm

Scintillator
material: LYSO
Number of
detector rings:
• 80
Axial FOV:
• 235mm

Scintillator
material:
LYSO
Number of
detector
rings:
• 80
Axial FOV:
235mm

CT System
uCT 780
uCT 780
uCT 780
uCT 780


Table 2 Distribution of volunteer dataset


Table 4 The demographic distribution of human subjects


Evaluation Item
Evaluation Method
Criteria
Results

Contrast recovery (CR),
background variability (BV),
and contrast-to-noise ratio
(CNR) were calculated using

The averaged CR, BV, and
CNR of the uExcel DPR images
should be superior to those of
the OSEM images.

Table 5 The performance evaluation report criteria of uExcel DPR


Table 6 Distribution of volunteer dataset


Volume relative
to no motion
correction
(∆Volume).

Calculate the volume
relative to no motion
correction images

Maximal
standardized
uptake value
relative to no
motion
correction
(∆SUVmax)

Calculate the SUVmax
relative to no motion
correction images

Table 7 The performance evaluation report criteria of OncoFocus


Table 8 Distribution of volunteer dataset


For PMMA phantom data, the
average CT value in the affected
area of the metal substance and
the same area of the control image
before and after DeepMAC was
compared.

After using DeepMAC,
the difference between
the average CT value in
the affected area of the
metal substance and the
same area of the control
image does not exceed
10HU.

Table 9 The performance evaluation report criteria of DeepMAC


Manufacturer
Techsomed Medical Technologies. LTD.

Medical Image Management and Processing System


Subject Device
BioTraceIO Vision (Ver 1.7)

The intended patient
population is patients chosen
by interventional radiologists
to undergo ablation
treatment (including patients
with soft tissue lesions).

SUBSTANTIAL EQUIVALENCE COMPARISON TABLE


processing during ablation
procedures.
BioTraceIO Vision is used to
assist physicians in planning
ablation procedures,
including identifying ablation
targets and virtual ablation
needle placement.
BioTraceIO VIsion is used to
assist physicians in
confirming ablation zones.
The software is not intended
for diagnosis. The software is
not intended to predict
ablation volumes or predict
ablation success.

processing during liver
ablation procedures.
BioTraceIO Vision is used to
assist physicians in planning
ablation procedures,
including identifying ablation
targets and virtual ablation
needle placement.
BioTraceIO Vision is used to
assist physicians in
confirming ablation zones.
The software is not intended
for diagnosis. The software is
not intended to predict
ablation volumes or predict
ablation success.

ablation
procedures.
Existing liver
registration
algorithms were
adapted for kidney
anatomy and
segmentation
algorithms were
developed for
kidney anatomy.
These
modifications
represent an
extension of
established
functionality and
do not raise new
questions of safety
or effectiveness.
Performance
testing of V1.7
supports
equivalence to the
predicate device.

The application’s use
environment is the Operation
Room and the hospital
healthcare environment such
as interventional radiology
control room

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

BioTraceIO Vision is a stand-
alone software application
with tools and features
designed to assist users in
planning ablation procedures
as well as tools for treatment
confirmation. The use
environment the device is
the Operating Room and the
hospital healthcare
environment such as

BioTraceIO VIsion is a stand-
alone software application
with tools and features
designed to assist users in
planning liver ablation
procedures as well as tools
for treatment confirmation.
The use environment the
device is the Operating Room
and the hospital healthcare
environment such as


interventional radiology
control room. BioTraceIO
Vision has six distinct
workflow steps:
• Data Import
• Anatomic Structures
Segmentation
• Instrument
Placement (Needle
Planning)
• Ablation Zone
Segmentation
• Registration of Pre-
Procedure Images
• Treatment
Confirmation
(Registration of Pre-
and Post-
Interventional
Images; Quantitative
Analysis)

Window and level, pan,
zoom, cross- hairs, slice
navigation

Tools for segmenting 3D
VOIs, including target tissues,
ice ball (for cryoablation),
ablation zones, vessels, liver
and kidney.

Tools for segmenting 3D VOIs,
including target tissues,
ablation zones, vessels and
liver.

Addition of tools
for segmenting 3D
VIOs for kidney.
These
modifications
represent an
extension of
established
functionality and
do not raise new
questions of safety
or effectiveness.
Performance
testing of V1.7
supports
equivalence to the
predicate device.


Registration of multiple
images and imaging
modalities into a single view.

Registration of the planning
scan, containing the
identified target tissue, with
the confirmation scan
showing the ablation zone.
The delineated target tissue
on the planning scan is then
projected onto the
confirmation scan and
overlaid onto the delineated
ablation zone segmentation.
This helps the user in
analysing if the ablation zone
covers the target tissue with
the desired amount of
margin.


Imaging Modality
Algorithm
Metric
Performance

CT
Kidney Segmentation****
Mean DICE
0.91

CT
Liver Ablation Target Segmentation
Mean DICE
0.82

MR
Liver Ablation Target Segmentation
Mean DICE
0.76

CT
Liver Ablation Zone Segmentation
Mean DICE
0.88

Liver Registration Pre-ablation MR –
Pre-ablation CT

Kidney Registration Diagnostic CT –
Pre-ablation CT

Liver Registration Pre-ablation CT –
Post-ablation CT

Kidney Registration Pre-ablation CT
– Post-ablation CT

Liver Registration Pre-ablation MR –
Post-ablation CT

The following table provides a summary of the validation results:


Classification Name:
Medical image Analyzer


Image Modality
Chest Radiographs
Chest Radiographs

Suspicious regions of interest (ROI)
To inform the primary diagnostic
and patient management decisions
that are made by the clinical user.

ER physicians, Family medicine
practitioners and radiologists

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

PACS system
Image annotations made on copy of
original image.

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

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


Table 4 wAFROC of reads unaided and aided by qXR-Detect


Table 5 AUROC, Sensitivity and Specificity of reads unaided and aided by qXR-Detect


Modification 1 (M1) will involve retraining the
existing model using newly acquired, high-
quality adult chest radiograph datasets to
improve device performance without altering
the underlying model architecture.

Rationale
Improve the algorithm performance

The availability of a new, high-quality dataset
obtained from different parts of US, that
improves the diversity of the training data,
offering a logical pathway to improve overall
model performance.

Retraining with newly acquired, high-quality
adult chest radiograph datasets, followed by
locked test-set evaluation and comparison to
baseline model using predefined acceptance
criteria. Performance evaluation for
Modification 1 will ensure that the retrained
CNN-based model continues to operate safely,
effectively, and within the boundaries of the
device’s cleared indications for use. The
updated model will be evaluated using a
predefined set of clinically meaningful and
statistically robust metrics to confirm that the
performance is improved without introducing
any regression across the six existing condition
categories.

Before deployment, each update is reviewed to
ensure it stays within the scope of the PCCP
with no new outputs, indications, or workflow
changes and is released only after all
verification and validation confirm it meets
predefined success criteria. If these criteria are

Table 6 Details of Modification M1 and M2


met, the update is implemented through a
controlled, manual deployment process and
made globally available to all end users.
Users will be notified of updates implemented
under the PCCP through formal communication
channels, including revised labelling, updated
user manuals, and detailed release notes.
These materials will clearly outline any changes
to the device’s performance, functionality, or
indications for use, and will be disseminated in
accordance with established quality system
procedures to ensure timely, accurate, and
comprehensive stakeholder awareness.

Benefits: Improved detection and localization
performance across six clinically relevant
categories;
Risks: Potential overfitting and unintended bias
if new data are imbalanced; risk of minor
performance variability.
Risk Mitigation: The modified model will be
tested for non-inferiority on the performance
study test dataset which will contain new
unseen data.

Modification 2 (M2) will involve a controlled
architectural change through modular
replacement of the existing Convolutional
Neural Network (CNN) encoder with a Vision
Transformer (ViT)-based encoder, while
maintaining the remaining components and the
workflow of the model pipeline.

Enhance feature extraction and global context
understanding to further improve diagnostic
performance

Given the established potential of ViT models,
Modification 2 will be explored if a superior
Vision encoder becomes available. If the
updated architecture demonstrates
improvement as described in the Acceptance
Criteria, the modification will be accepted.

For testing the M2 modification, candidate
models (trained on ViT architecture) will
undergo rigorous evaluation against the
baseline CNN model. The updated model will
be evaluated using a predefined set of clinically
meaningful and statistically robust metrics to
confirm that the performance is improved
without introducing any regression across the
six existing condition categories. Testing


includes assessment of performance metrics-
(AUC, Box level sensitivity, specificity, subgroup
analysis). This ensures that candidate model
continues to operate safely, effectively, and
within the boundaries of the device’s cleared
indications for use.

Before deployment, each update is reviewed to
ensure it stays within the scope of the PCCP
with no new outputs, indications, or workflow
changes and is released only after all
predefined verification and validation confirm it
meets predefined success criteria. If these
criteria are met, the update is implemented
through a controlled, manual deployment
process and made globally available to all end
users.
Users will be notified of updates implemented
under the PCCP through formal communication
channels, including revised labelling, updated
user manuals, and detailed release notes.
These materials will clearly outline any changes
to the device’s performance, functionality, or
indications for use, and will be disseminated in
accordance with established quality system
procedures to ensure timely, accurate, and
comprehensive stakeholder awareness.

Benefits: Improved ROI classification accuracy
and localization precision
Risks: Possibility of new failure modes due to
architectural change and model instability; risk
of decoder-compatibility errors.
Risk Mitigation: Full integration verification;
performance comparison to baseline using
identical metrics and datasets


GE Medical Systems Ultrasound and Primary care Diagnostics, LLC
3200 N Grandview Blvd
Waukesha, WI 53188 USA

GE Ultrasound Korea, Ltd.
9, Sunhwan-ro 214 beon-gil, Jungwon-gu,
Seongnam-si, Gyeonggi-do, 13204 Republic of Korea

Bryan Behn
Sr. Regulatory Affairs Director
GE HealthCare
T:(262)247-5502

Qingmeng Chen
Regulatory Affairs Leader
GE HealthCare
T: +86-18180590723

Common / Usual Name:
Diagnostic Ultrasound System

IYN (primary), IYO, ITX, QIH (secondary)
Ultrasonic Pulsed Doppler Imaging System. 21CFR 892.1550, 90-IYN;
Ultrasonic Pulsed Echo Imaging System, 21CFR 892.1560, 90-IYO;
Diagnostic Ultrasound Transducer, 21 CFR 892.1570, 90-ITX
automated radiological image processing software, 21 CFR 892.2050,
90-QIH

K231989 LOGIQ E10s, LOGIQ Fortis Diagnostic Ultrasound System

K232381 LOGIQ Totus Diagnostic Ultrasound System
K231301 Vscan Air
K201768 Voluson E10

In accordance with 21 CFR 807.92 the following summary of information is provided:


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

• The overall model detection accuracy (sensitivity and
specificity) and DICE score for the Aorta, Kidney,
Liver/Spleen/inferior vena cava (IVC), Gallbladder
(GB)/Urinary Bladder, Pancreas, and Air view is expected to
be as follows:
 Detection accuracy ≥ 80% (0.80)
 Sensitivity (True Positive Rate): ≥ 80% (0.80)
 Specificity (True Negative Rate): ≥ 80% (0.80)
 DICE Similarity Coefficient (Segmentation Accuracy): ≥
0.80
• The number of individual subjects: 49
• The number of annotation images: 1186
• The model achieved accuracy of 94.8%, with sensitivity of
0.91, specificity of 0.98, and a DICE score of 0.82, all of which
meet the predefined acceptance criteria.

Information about clinical subgroups
and confounders present in the
dataset

• Gender: Male 24.2% (8), Female 75.8% (25)
• Age: 60 ±15.7 (average and standard deviation) (24 Min, 83
Max)
• BMI: 27±5.6 (average and standard deviation) (16 Min, 38
Max)
• Ethnicity: not hispanic 96.4%(27), hispanic 3.6% (1)
• Race : Asian 12.9% (4), White 77.4% (24), Black 9.7% (3)
• Country: USA (100%)

Information about equipment and
protocols used to collect images

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


Information about clinical subgroups
and confounders present in the
dataset

Long View Aorta:
• Gender: 11 Male, 25 Female
• Country: 16 Japan, 20 USA
• Age: 60.50 ±14.24 (average and standard deviation) (23
Min – 81 Max)
• BMI: 25.11 ±5.69 (average and standard deviation)
(16.65 – 37.60)
Short View Aorta:
• Gender: 11 Male, 24 Female
• Country: 15 Japan, 20 USA
• Age: 61.11 ±14.46 (average and standard deviation) (23
Min – 81 Max)
• BMI: 25.24 ±5.97 (average and standard deviation)
(16.65 – 37.60)

Validation images were collected on LOGIQ Fortis with the C1-6
probe with a standardized protocol. Images acquired on LOGIQ
Fortis are acceptable for validation of LOGIQ E10 because the
transmit, receive, and back-end processing hardware are the
same. The acquisition software architecture is also the same.
Therefore, the image quality of the two systems is comparable.

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

• Before the process of data annotation, all information
displayed on the device is removed and performed on
information extracted purely from Ultrasound B-mode
images.
• Readers to ground truth the AP measurement of the
aorta long view and the AP and Trans measurement of
the aorta short view – Number of keystrokes measured.
• Readers to ground truth the AP measurement of the
aorta long view and the AP and Trans measurement of
the aorta short view using AI - Number of keystrokes
measured.
• Number of Keystrokes with and without AI is compared
for each reader.
• Arbitrator to select most accurate measurement among
all readers.
• Arbitrator selected measurement is compared to AI
baseline measurement with and without on
segmentation editing for accuracy.

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

The exams used for regulatory validation purpose are separated
from the ones used during model development process by exam
site origin ensuring there is no overlap between the two.


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

• The average reduction between keystrokes for
measurements (diameter) made manually and with AI is
1.62±0.375 (mean and standard deviation)
Porta Hepatis measurement accuracy without segmentation scroll
edit:
• Average accuracy is 59.85% with 95% CI of +/- 17.86% and
average absolute error of 1.66 mm and 95% CI of 1.02 mm.
• Limits of Agreement in millimeters are (-4.75,4.37) with
95% CI of (-6.17, 5.79).
Porta Hepatis measurement accuracy with segmentation scroll
edit:
• Average accuracy is 80.56% with a 95% CI of +/- 8.83% and
an average absolute error 0.91 mm and 95% CI of 0.45
mm.
• Limits of Agreement in millimeters are (-1.96, 3.25) with
95% CI of (-2.85,4.14).

• Gender: Male 44% (11), Female 56% (14)

• Age: 62 ±16.03 (average and standard deviation) (23 Min –

Information about clinical subgroups and

• BMI: 23.48 ± 4.94 (average and standard deviation) (16.65

Validation images were collected on LOGIQ Fortis with the C1-6
probe with a standardized protocol. Images acquired on LOGIQ
Fortis are acceptable for validation of LOGIQ E10 because the
transmit, receive, and back-end processing hardware are the same.
The acquisition software architecture is also the same. Therefore,
the image quality of the two systems is comparable.

• Before the process of data annotation, all information

displayed on the device is removed and performed on

information extracted purely from Ultrasound B-mode

• Readers to ground truth the diameter of the CBD in the

Porta Hepatis – Number of keystrokes measured.

Porta Hepatis using AI - Number of keystrokes measured.

• Number of Keystrokes with and without AI is compared for

• Arbitrator to select most accurate measurement among all

• Arbitrator selected measurement is compared to AI

baseline measurement with and without on segmentation


study triage within existing patient pathways. It

does not replace any part of the current standard of care. It is designed to assist in prioritization of studies for

reading within a worklist, in addition to any other pre-existing formal or informal methods of study prioritization in

place. Specifically, it does not remove cases from a reading queue and operates in parallel to the standard of care.

This device is not intended to replace the usual methods of communication and transfer of information in the


Metrics
21 < Age < 50
50 ≤ Age < 70
Age ≥ 70

thickness and scanner manufacturer subgroups. Additional subgroup analysis is reported in the labelling.


Metrics
GE MEDICAL SYSTEM
Philips
SIEMENS


Metrics
SIEMENS
GE MEDICAL SYSTEMS
Philips


Characteristic/
Brainomix 360 Triage Stroke
Brainomix 360 Triage Stroke

Parameter
Predicate Device (K232496)
Subject Device

Brainomix 360 Triage Stroke is a
radiological computer aided triage and
notification software indicated for use in
the analysis of non-contrast head CT
(NCCT) images to assist hospital networks
and trained clinicians in workflow triage by
flagging and communicating suspected
positive findings of head NCCT images for
large vessel occlusion (LVO) of the
intracranial ICA and M1 and intracranial
hemorrhage (ICH). Specifically, the device
is intended to be used for the triage of
images acquired from adult patients in the
acute setting, within 24 hours of the onset
of the acute symptoms, or where this is
unclear, since last known well (LKW) time.
It is not intended to detect isolated

Brainomix 360 Triage Stroke is a radiological
computer aided triage and notification
software indicated for use in the analysis of
non-contrast head CT (NCCT) images to
assist hospital networks and trained
clinicians in workflow triage by flagging and
communicating suspected positive findings
of head NCCT images for large vessel
occlusion (LVO) of the intracranial ICA and
M1 or intracranial hemorrhage (ICH).
Specifically, the device is intended to be
used for the triage of images acquired from
adult patients in the acute setting, within 24
hours of the onset of the acute symptoms,
or where this is unclear, since last known
well (LKW) time. It is not intended to detect

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


• Brainomix 360 Triage Stroke has been
validated and is intended to be used on
Siemens, GE and Philips scanners.
• Brainomix 360 Triage Stroke is not
intended to be used on patients with
recent (within 6 weeks) neurosurgery or
endovascular neurointervention or
recent (within 4 weeks) previous
diagnosis of stroke.
• Brainomix 360 Triage Stroke is not
intended to detect isolated
subarachnoid hemorrhage and
symmetrical bilateral MCA occlusions.
Contraindications:
Brainomix 360 Triage Stroke is not suitable
for use with scan data containing image
features associated with:
• tumors or abscesses
• coils, shunts, embolization or
movement artifacts
• Brainomix 360 Triage Stroke is not
intended to be used for analyzing CT
images in intracranial vascular
pathologies such as arterial aneurysms,
arteriovenous malformations or venous
thrombosis.

workup - it provides workflow
prioritization and notification only.
3. Brainomix 360 Triage Stroke has
been validated and is intended to
be used on Siemens, GE and Philips
scanners.
4. Brainomix 360 Triage Stroke is not
intended to be used on patients
with recent (within 6 weeks)
neurosurgery or endovascular
neurointervention or recent (within
4 weeks) previous diagnosis of
stroke.
5. Brainomix 360 Triage Stroke is not
intended to detect symmetrical
bilateral MCA occlusions.
Contraindications:
Brainomix 360 Triage Stroke is not suitable
for use with scan data containing image
features associated with:
• tumours or abscesses
• coils, shunts, embolization or
movement artifacts
• intracranial vascular pathologies
such as arterial aneurysms,
arteriovenous malformations or
venous thrombosis.

Environment of Use
Clinical/Hospital environment
Clinical/Hospital environment

Input Data
NCCT (ICH and LVO)
NCCT (ICH and LVO)

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

Notification Display
Web user interface and mobile device
Web user interface and mobile device

Presentation of a preview of the study for
initial assessment not meant for diagnostic
purposes.
The device operates in parallel with the
standard of care.

Device does not mark, highlight, or direct
users’ attention to a specific location in the
original image


Two cascaded functions (ICH then LVO)
using three integrated algorithms

Triage Stroke Configuration: two cascaded
functions (ICH then LVO) using three
integrated algorithms
Triage ICH Configuration: ICH analysis
function using one algorithm
NCCT LVO Configuration: LVO analysis
function using three integrated algorithms


GE Medical Systems Ultrasound and Primary Care Diagnostics, LLC
3200 N Grandview Blvd
Waukesha, WI 53188 USA

Bryan Behn
Sr. Regulatory Affairs Director
GE HealthCare
T:(262)247-5502

Beth Wentworth
Regulatory Affairs Leader
GE HealthCare
T: 2627883816

Common / Usual Name:
Diagnostic Ultrasound System

IYN (primary), IYO, ITX, QIH (secondary)
Ultrasonic Pulsed Doppler Imaging System. 21CFR 892.1550, 90-IYN;
Ultrasonic Pulsed Echo Imaging System, 21CFR 892.1560, 90-IYO;
Diagnostic Ultrasound Transducer, 21 CFR 892.1570, 90-ITX
automated radiological image processing software, 21 CFR 892.2050,
90-QIH

Primary Predicate Device:
K231966 LOGIQ E10

K232381 LOGIQ Totus Diagnostic Ultrasound System
K201768 Voluson E10


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

• The overall model detection accuracy (sensitivity and
specificity) and DICE score for the Aorta, Kidney,
Liver/Spleen/inferior vena cava (IVC), Gallbladder
(GB)/Urinary Bladder, Pancreas, and Air view is expected to
be as follows:
 Detection accuracy ≥ 80% (0.80)
 Sensitivity (True Positive Rate): ≥ 80% (0.80)
 Specificity (True Negative Rate): ≥ 80% (0.80)
 DICE Similarity Coefficient (Segmentation Accuracy): ≥
0.80
• The number of individual subjects: 49
• The number of annotation images: 1186
• The model achieved accuracy of 94.8%, with sensitivity of
0.91, specificity of 0.98, and a DICE score of 0.82, all of which
meet the predefined acceptance criteria.

Information about clinical subgroups
and confounders present in the
dataset

• Gender: Male 24.2% (8), Female 75.8% (25)
• Age: 60 ±15.7 (average and standard deviation) (24 Min, 83
Max)
• BMI: 27±5.6 (average and standard deviation) (16 Min, 38
Max)
• Ethnicity: not hispanic 96.4%(27), hispanic 3.6% (1)
• Race : Asian 12.9% (4), White 77.4% (24), Black 9.7% (3)
• Country: USA (100%)

Information about equipment and
protocols used to collect images

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

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

• Before the process of data annotation, all information
displayed on the device is removed and performed on
information extracted purely from Ultrasound B-mode
images.


Long View Aorta:
• The average keystrokes to obtain the Anteroposterior
(AP) measurement (diameter) of the aorta in the long
view is 4.132 +/- 0.291 without AI and 1.236 +/-0.340
with AI.
Short View Aorta:
• The average keystrokes to obtain the AP and Trans
measurement (diameter) of the aorta in the short view is
7.05 +/-0.158 without AI and 2.307 +/- 1.0678 with AI.
Long View AP Measurement Accuracy:
• Average accuracy is 87.2% with 95% CI of +/- 1.98% and
average absolute error of 0.253 cm and 95% CI of 0.049
cm.
• Limits of Agreement in centimeters are (-0.15, 0.60) with
95% CI of (-0.26, 0.71).
Short View AP Measurement Accuracy:
• Average accuracy is 92.9% with a 95% CI of +/- 2.02%
and an average absolute error of 0.128 cm and 95% CI of
0.037 cm.
• Limits of Agreement in centimeters are (-0.21, 0.36) with
95% CI of (-0.29, 0.45).
Short View Trans Measurement Accuracy:
• Average accuracy is 86.9% with 95% CI of +/- 6.25% and
average absolute error of 0.235 cm and 95% CI of 0.110
cm.
• Limits of agreement in centimeters are (-0.86, 0.69) with
95% CI (-1.06, 0.92).

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

Long View Aorta:
• Gender: 11 Male, 25 Female
• Country: 16 Japan, 20 USA
• Age: 60.50 ±14.24 (average and standard deviation) (23
Min – 81 Max)


• BMI: 25.11 ±5.69 (average and standard deviation)
(16.65 – 37.60)
Short View Aorta:
• Gender: 11 Male, 24 Female
• Country: 15 Japan, 20 USA
• Age: 61.11 ±14.46 (average and standard deviation) (23
Min – 81 Max)
• BMI: 25.24 ±5.97 (average and standard deviation)
(16.65 – 37.60)

• Validation images were collected on LOGIQ Fortis with
the C1-6 probe with a standardized protocol. Images
acquired on LOGIQ Fortis are acceptable for validation of
LOGIQ E10 because the transmit, receive, and back-end
processing hardware are the same. The acquisition
software architecture is also the same. Therefore, the
image quality of the two systems is comparable.

• Before the process of data annotation, all information
displayed on the device is removed and performed on
information extracted purely from Ultrasound B-mode
images.
• Readers to ground truth the AP measurement of the
aorta long view and the AP and Trans measurement of
the aorta short view – Number of keystrokes measured.
• Readers to ground truth the AP measurement of the
aorta long view and the AP and Trans measurement of
the aorta short view using AI - Number of keystrokes
measured.
• Number of Keystrokes with and without AI is compared
for each reader.
• Arbitrator to select most accurate measurement among
all readers.
• Arbitrator selected measurement is compared to AI
baseline measurement with and without on
segmentation editing for accuracy.

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

The exams used for regulatory validation purpose are separated
from the ones used during model development process by exam
site origin ensuring there is no overlap between the two.


Porta Hepatis measurement accuracy without segmentation scroll
edit:
• Average accuracy is 59.85% with 95% CI of +/- 17.86% and
average absolute error of 1.66 mm and 95% CI of 1.02 mm.
• Limits of Agreement in millimeters are (-4.75,4.37) with
95% CI of (-6.17, 5.79).
Porta Hepatis measurement accuracy with segmentation scroll
edit:
• Average accuracy is 80.56% with a 95% CI of +/- 8.83% and
an average absolute error 0.91 mm and 95% CI of 0.45
mm.
• Limits of Agreement in millimeters are (-1.96, 3.25) with
95% CI of (-2.85,4.14).

• Gender: Male 44% (11), Female 56% (14)

• Age: 62 ±16.03 (average and standard deviation) (23 Min –

Information about clinical subgroups and

• BMI: 23.48 ± 4.94 (average and standard deviation) (16.65

Information about equipment and
protocols used to collect images

Validation images were collected on LOGIQ Fortis with the C1-6
probe with a standardized protocol. Images acquired on LOGIQ
Fortis are acceptable for validation of LOGIQ E10 because the
transmit, receive, and back-end processing hardware are the same.
The acquisition software architecture is also the same. Therefore,
the image quality of the two systems is comparable.

• Before the process of data annotation, all information

displayed on the device is removed and performed on

information extracted purely from Ultrasound B-mode

• Readers to ground truth the diameter of the CBD in the

Porta Hepatis – Number of keystrokes measured.

Porta Hepatis using AI - Number of keystrokes measured.

• Number of Keystrokes with and without AI is compared for

• Arbitrator to select most accurate measurement among all

• Arbitrator selected measurement is compared to AI

baseline measurement with and without on segmentation

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

The exams used for regulatory validation purpose are separated
from the ones used during model development process by exam
site origin ensuring there is no overlap between the two.


DHT8B: Division of Radiological Imaging Devices and

Office of Product Evaluation and Quality


Rapid Aortic Measurements (AM) (subject
device)

Rapid Neuro3D (Marketed as Lumina3D)
(K243350)

21 CFR 892.2050; Medical image management
and processing system

Intended
Use/
Indications for
Use
Statement

Rapid Aortic Measurements (AM) is an image
analysis and measurement device to evaluate
aortic and iliac arteries in contrast enhanced and
non-contrast CT imaging datasets acquired of
the chest, abdomen, and/or pelvis. The module
segments the aorta, iliacs, and major branching
vessels and provides 2D and 3D visualizations
of the segmented vessels.
Outputs of the device include: Centerline
measurements of the aorta and iliacs, Aortic
Zone Measurements (Maximum Oblique
Diameter), Fixed Measurements of the aorta
and left and right iliacs, 3D Volume
Renderings, Rotations, Curved Planar
Reformations (CPRs) of the isolated left and
right iliacs, aortic oblique Multiplanar
Reconstructions (MPRs), and Longitudinal
Tracking visualizations.
Rapid Aortic Measurements is an aid to
physician decision making. Its results are not
intended to be used on a stand-alone basis for
clinical decision-making or otherwise preclude
clinical assessment.
Rapid Aortic Measurements is indicated for
adults.

Rapid Neuro3D (RN3D) is an image analysis
software for imaging datasets acquired with
conventional CT Angiography (CTA) from the
aortic arch to the vertex of the head). The module
removes bone, tissue, and venous vessels,
providing a 3D and 2D visualization of the
neurovasculature supplying arterial blood to the
brain.
Outputs of the device include 3D rotational
maximum intensity projections (MIPS), volume
renders (VR), along with the curved planar
reformation (CPR) of the isolated left and right
internal carotid and vertebral arteries.
Rapid Neuro3D is designed to support the
physician in confirming the presence or absence
of physician-identified lesions and evaluation,
documentation, and follow-up of any such lesion
and treatment planning.
Its results are not intended to be used on a stand-
alone basis for clinical decision-making or
otherwise preclude clinical assessment.
RN3D is indicated for adults.
Precautions/Exclusions:

Aortic Measurements software is substantially equivalent.


Precautions/Exclusions:
o Series containing excessive patient motion or
metal implants may impact module output
quality.
o The AM module will not process series that
meet the following module exclusion criteria:
• Series acquired w/cone-beam CT scanners (c-
arm CT)
• Series that are non-axial or axial oblique
greater than 5 degrees
• Series containing improperly ordered or
missing slices where the gap is larger than 3
times the median inter-slice distance (e.g., as a
result of manual correction by an imaging
technician)
• Series with less than 3cm of target anatomical
zones (e.g. aorta or right/left iliac artery)
• NCCT, CECT, CTA, or CTPA datasets with:
1) in-plane X and Y FOV < 160mm
2) Z FOV (cranio-caudal transverse
anatomical coverage) < 144 mm.
3) in-plane pixel spacing (X & Y resolution)
< 0.3 mm or > 1.0 mm.
4) inter-slice distance of < 0.3 mm or > 3 mm.
5) slice thickness > 3 mm.
6) data acquired at x-ray tube voltage <
70kVp or > 150kVp, including single energy,
dual energy, or virtual monochromatic datasets

o Series containing excessive patient motion
or metal implants may impact module output
quality.
o The RN3D module will not process series
that meet the following module exclusion
criteria:
• Series containing inadequate
contrast agent (<0.3 mL of right-
hemisphere intracranial arterial
contrast media or <0.3 mL of left-
hemisphere intracranial arterial
contrast media, above 120 HU)
• Series acquired w/cone-beam CT
scanners (c-arm CT)
• Series that are non-axial
• Series with a non-supine patient
position
• Series containing missing or
improperly ordered slices (e.g., as a
result of manual correction by an
imaging technician)
• CTA datasets with:
1) in-plane X and Y FOV < 160mm or > 400mm.
2) Z FOV (cranio-caudal transverse anatomical
coverage) < 90 mm.
3) in-plane pixel spacing (X & Y resolution) < 0.2
mm or > 1.0 mm.
4) Z slice spacing of < 0.2 mm or > 1.25 mm.
5) slice thickness > 1.5mm.
6) data acquired at x-ray tube voltage < 70 kVp
or >150 kVp.

Radiologists, cardiothoracic surgeons, vascular
surgeons, endovascular specialists, ED
physicians, or users with similar training.

Radiologists, neurovascular and neurosurgical
specialists, such as vascular neurosurgeons,
neuro-interventional specialists, ED physicians,
or users with similar training.

Software package (SaMD) which interfaces to a
PACS/Viewer or allows viewing within the
application.

Standard off-the-shelf Hardware: Cloud Hybrid

Standard off-the-shelf Hardware: On-Premises
or Cloud Hybrid

Chest, Abdomen, and/or Pelvis (aorta and iliacs)

Neurological (aortic arch to the vertex of the
head)


• 2D/3D vessel visualization (VRs, CPRs,
MPRs)
• Automated segmentation and removal of
obstructive bones and/or vessels
• Automated vessel centerlines and
measurements
• Comparison/longitudinal tracking

• 2D/3D vessel visualization (Thins, MIPS, VR,
CPR)
• Automated segmentation and removal of
obstructive bones and/or vessels


The Monaco system is used
to make treatment plans for
patients with prescriptions for
external beam radiation
therapy. The system
calculates dose for photon,
electron and proton
treatment plans and
displays, on screen and in
hard-copy, two- or three-
dimensional radiation dose
distributions inside the

The Monaco system is
used to make treatment
plans for patients with
prescriptions for external
beam radiation therapy.
The system calculates
dose for photon,
electron and proton
treatment plans and
displays, on screen and
in hard-copy, two- or
three-dimensional
radiation dose


distributions inside the
patients for given
treatment plan set-ups.
The Monaco product line
is intended for use in
radiation treatment
planning. It uses
generally accepted
methods for:
•Contouring
•Image manipulation
•Simulation
•Image fusion
•Plan optimization
•QA and plan review

Monte Carlo - electron &
photon
Collapsed Cone (photon)
Pencil Beam (only used
when optimization)
GPUMCD for photon (MR
linac)
GPUMCD for proton
Proton Pencil Beam

Monte Carlo - electron &
photon
Collapsed Cone
(photon)
Pencil Beam (only used
when optimization)
GPUMCD for photon
(both MR linac and
conventional linac)
GPUMCD for proton
Proton Pencil Beam

Substantially Equivalent
GPUMCD extends from
MR linac to
conventional linac.

Dose
calculation for
MR-Linac
(including
magnetic field,
coils &
cryostat)

Adaptive therapy
planning for MR linac &
conventional linac

Substantially Equivalent
Adaptive Therapy
Planning functionalities
are extended from MR
linac (UNITY) to
conventional Linac
(EMLA). Enables offline
adaptive planning on
EMLA.

Substantially Equivalent
Auto planning feature
for iterative modification
of optimization cost
function parameters
introduced in subject
device.

Yes
(with traditional and
machine learning
algorithm)

Substantially Equivalent
The Segmentation
Component utilizes
machine-learning based
models to automatically
segment MR image
sets introduced in
subject device.


Hypertion Optimizer
(Constrained Optimization)

Next Generation
Optimizer (GPU-
accelerated Hypertion
Optimizer)

Substantially Equivalent
Next Generation
Optimizer in the subject
device is the GPU
based optimizer which
uses Pseudo-Gradient
Descent algorithm.

Beam modeling is performed
by Elekta personnel.
Standardized beam models
are provided for some Elekta
linac energy options.

Both UI based and non-
UI based scripting

• EMLA
• MR-linac Unity
• MOSAIQ
• Smart flow

• EMLA
• MR-linac Unity
• MOSAIQ
• Third party
contouring tools
• Smart flow


Testing Performed to establish substantial equivalence

Segmentation component for invoking
MR auto-segmentation algorithms

For the AI-based segmentation component, performance
testing was conducted for the models – Female Pelvis
Intact & Hysterectomy (trained on a joint image set of 529
images), Male Pelvis (trained on an image set of 250
images) and Head& Neck trained on an image set of 1862
images).
The primary metric for evaluating the model’s
performance is the Average Hausdorff Distance (AVD).
The acceptance threshold is set at 3 mm. A DICE or AUC
value of 0.7 is also taken to represent a value of interest
that might indicate model performance with respect to a
structure or set of structures, however the DICE and AUC
results were not explicitly used as the pass/fail metric.
Quantitative performance evaluation demonstrated that,
for all evaluated structures across all models, the mean
Absolute Volume Difference (AVD) was less than 3 mm.
Structure specific statistical analyses supported this
conclusion. In addition, for all structures and models, the
patterns of failure for any structure that did not meet the
Dice Similarity Coefficient (DICE) confidence value of
interest of 0.7 were investigated and any findings were
included as "Limitations".
Sub-group analysis is carried on the assessment of
performance in the patient size, pixel size, slice spacing
and number of slices subgroups.
Additionally, qualitative analysis has also been executed
based on a 5-point Likert scale with a conclusion that the
automatically generated structures provided a valuable
starting point for clinical delineation.

Verification testing was performed to evaluate the
AutoPlanning functionality, including workflow
performance, protocol management, plan creation,
intero‑perability, and error handling.
Validation testing demonstrated that the Auto Planning
functionality supports the creation of clinically acceptable
treatment plans for the intended use. Treatme‑nt plans
generated using AutoPlanning were reviewed within the
clinical workflow and determined to be suitable for
clinical use, without in‑troducing new safety or


effectiveness concerns. All testing met pre-defined
acceptance criteria.

Extending the adaptive planning
capabilities to EMLA for offline adaptive
planning

Verification testing was performed to evaluate the offline
adaptive planning functionality using Monaco. Testing
assessed correct system behavior during image
registration, structure propagation, dose recalculation/re-
optimization, offline adaptive plan generation, and
workflow execution under representative clinical
scenarios.
The purpose of testing was to confirm that offline
adaptive planning functions operate as intended and
support the creation of an updated treatment plan based
on CBCT imaging without compromising data integrity or
workflow performance. No defects, unexpected behavior,
or data integrity issues were identified during testing.
Validation testing demonstrated that offline adaptive
planning using CT toCBCT supports creation of
clinically acceptable treatment plans for the intended
use. The offline ad‑ap‑tive plans generated using CBCT
imaging were reviewed within the clinical workflow and
determined to be suitable for use. Verification and
validation testing met pre-defined acceptance criteria.

Interoperability with 3rd party software
for image management and contouring

The device incorporates or interfaces with third party
contouring functionality intended to support radiotherapy
‑
treatment planning.
Verification and validation testing were conducted to
confirm that the thirdparty contouring performs as
intended and does not adversely impact the safety or
‑
effectiveness of the overall system.
Verification testing was conducted to confirm correct
DICOM export functionality, preservation of data
integrity, and successful creation of an offline adaptive
plan when using the third party contouring functionality.
‑
Validation testing was conducted to verify the treatment
planning workflows in a Treatment Planning System
(TPS) and treatment preparation in Record and Verify
(R&V) system.
Acceptance criteria were defined to ensure that
thirdparty contouring outputs are clinically acceptable
and comparable to reference contours produced by
‑
qualified users.
All verification and validation testing met the predefined
acceptance criteria. All planned Solution Interoperability
test cases have been successfully executed and passed.


stics with the Predicate Device ...........................


Director, Digital Health and Ima
ging Center
Lead

Classification Name:
Automated Radiological Image
Processing
Software

Name of Device:
Lumify Diagnostic Ultrasound S
ystem

Classification Name:
Ultrasonic Pulsed Doppler Imag
ing System


Reference
Device
Lumify
Diagnostic
Ultrasound
System
(K223771)

The device is
intended for
noninvasive
processing of
ultrasound
images to
detect, measure,
and calculate

The device is
intended for
diagnostic
ultrasound
imaging in B
(2D), Pulsed
Wave, Color
Doppler,

Equivalent to
predicate. The
subject device is
intended for use
in patients aged
18 years and
older. The
predicate device


relevant medical
parameters of
structures and
function of
patients aged 18
years or older
with suspected
disease.

relevant medical
parameters of
structures and
function of adult
patients with
suspected
disease.

is intended for
adult patients
aged 22 years
and older. This
difference in
minimum patient
age does not
impact the
intended use or
raise new
questions of
safety and
effectiveness.
Comparisons
with the
reference device
are focused on
its merged B line
artifact detection
software feature.

Ultrasound image
processing software
implementing artificial
intelligence including
non-adaptive machine
learning algorithms trained
with clinical data intended
for non-invasive analysis of
ultrasound data

Equivalent. The
change in input
format does not
change the
Intended Use.
Both formats
store the same
ultrasound
imaging data
required for
clinical
interpretation.
While DICOM
includes
additional
metadata, this is
not critical to the
proposed
device's


Single and multi-frame ultrasound
images

Deep convolutional neural networks for segmentation
or landmark detection

Yes. A table
summarizing the
device outputs
for each
uploaded cine is
displayed to the
user.

Equivalent to
predicate.
Restricting the
anatomical site
to the lungs
does not change
the Intended
Use. Identical to
reference
device.

Detects the
presence or
absence of B
lines

Counts B lines
and detects the
presence or
absence of
merged B lines

Equivalent. The
updated task
formulation does
not change the
Intended Use.
The provided
performance
assessment for
the B line artifact
detection
module
demonstrates
that this
difference does
not raise any


Indications for Use
Please type in the marketing application/submission number, if it is known. This
K252018 ?
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.

HERA Z20 Diagnostic Ultrasound System; HERA Z20e Diagnostic Ultrasound System; HERA Z20s
Diagnostic Ultrasound System; R20 Diagnostic Ultrasound System; HERA Z30 Diagnostic Ultrasound
System· R30 Diagnostic Ultrasound System

Please provide your Indications for Use below.

HERA Z20 Diagnostic Ultrasound System; HERA Z20e Diagnostic Ultrasound System; HERA Z20s
Diagnostic Ultrasound System; R20 Diagnostic Ultrasound System; HERA Z30 Diagnostic Ultrasound
System; R30 Diagnostic Ultrasound System and probes are designed to obtain ultrasound images and
analyze body fluids.
The clinical applications include: Fetal/Obstetrics, Abdominal, Gynecology, Intra-operative, Pediatric, Small
Organ, Neonatal Cephalic, Adult Cephalic, Trans-rectal, Trans-vaginal, Muscular-Skeletal (Conventional,
Superficial), Urology, Cardiac Adult, Cardiac Pediatric, Thoracic, Trans-esophageal (Cardiac), Peripheral
vessel and Ophthalmic.
It is intended for use by, or by the order of, and under the supervision of, an appropriately trained healthcare
professional who is qualified for direct use of medical devices. It can be used in hospitals, private practices,
clinics and similar care environment for clinical diagnosis of patients.
Modes of Operation: 2D mode, M mode, Color Doppler mode, Pulsed Wave (PW) Doppler mode,
Continuous Wave (CW) Doppler mode, Tissue Doppler Imaging (TOI) mode, Tissue Doppler Wave (TOW)
mode, Power Doppler (PD) mode, ElastoScan Mode, MV-Flow Mode, Multi Image mode(Dual, Quad),
Combined modes, 3D/4D mode.

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)


AAMI ANSI ES60601-1:2005/(R)2012 & A1:2012 C1:2009/(R)2012 &
A2:2010/(R)2012 (Cons. Text) [Incl. AMD2:2021], Medical electrical
equipment - Part 1: General requirements for basic safety and essential
performance (IEC 60601-1:2005 MOD) [Including Amendment 2 (2021)]

ANSI AAMI IEC 60601-1-2:2014 [Including AMD 1:2021], Medical
electrical equipment - Part 1-2: General requirements for basic safety and
essential performance - Collateral Standard: Electromagnetic disturbances
- Requirements and tests [Including Amendment 1 (2021)]

IEC 60601-2-37 Edition 2.1 2015, Medical electrical equipment - Part 2-
37: Particular requirements for the basic safety and essential performance
of ultrasonic medical diagnostic and monitoring equipment

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

ANSI AAMI 10993-1 Fifth edition 2018-08, Biological evaluation of
medical devices - Part 1: Evaluation and testing within a risk management
process

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

System and their applications comply with the following FDA-recognized standards.


□ BMI: Normal or Underweight(BMI<=24.9), Overweight(25<=BMI<=29.9),

□ Ethnicity/Race : Asian, Black or African American, Hispanic or Latino, White


□ All ground truth (Measure, points, segmentation) were made by sonographer.

□ Images for validation were first classified into the correct views by participating

sonographer. Afterwards, corresponding anatomy areas were manually drawn for

□ The expert who classified views of the validation images has more than 10 years of

experience. The sonographer who made the ground truth of the validation images


□ Data used for test and training/tuning purpose are completely separated from the

ones during training process and there is no overlap between the two.

▣ Description of how the independence of test data from training data was ensured:


The feature demonstrated a localization accuracy success rate of 92.19% (95% CI:

[90.03%, 94.34%]) and a processing speed of approximately 3.98 FPS. The success

rate was defined as the percentage of frames within entire image sequences that

achieve the pre-specified frame accuracy criteria, in accordance with a reference

information supporting the appropriateness of the characterized performance.


□ At least two nerve views were used per patient, with 2D sequences consisting of at

□ The validation dataset consisted of at least 24 and up to 42 ultrasound images for

▣ The number of samples, if different from above, and the relationship between the two:


□ BMI: Normal/Underweight (BMI<25), Overweight (25≤BMI<30), Obese

□ Race/Ethnicity: Asian, African American, Hispanic, White, Unknown

□ Country: South Korea and United States


□ We divided the nerve ultrasound images into ten nerve groups depending on the

scanning region. The acquired data were further stratified by patient gender, age,

BMI, clinical site, and race/ethnicity for subgroup analysis.

▣ Information about clinical subgroups and confounders present in the dataset:


□ We acquired the data set with SAMSUNG MEDISON’s ultrasound system (R20,

RS85, V8, V7, V5) and probe (LA2-14A, LA2-9S, L3-22) in order to secure

diversity of the data set: Mix of data from retrospective data collection and

prospective data collection in clinical practice.

▣ Information about equipment and protocols used to collect images


□ The reference standard for NerveTrack was established through a rigorous, multi-

expert process to ensure accuracy and objectivity. The process was initiated by two

clinical experts, each with extensive experience in musculoskeletal ultrasound, who

independently performed manual segmentation by drawing a Region of Interest

(ROI) around the target nerve in each frame of the test data clips. A third, senior

clinical expert, also with extensive experience in the field, then acted as an

adjudicator. This senior expert reviewed the two independent segmentations and

resolved any discrepancies to establish the single, definitive ground truth ROI for

each frame used in the performance evaluation.


Estimate the volume of
cardiac chambers,
epicardial fat, aortic
valves, ascending aorta,
and pulmonary artery in
non-contrast and contrast-
enhanced CT scan images
Estimate the percentage
of liver voxels below
40HU in non-contrast CT
scan images
Estimate the percentage
of lung voxels below -950
and above -250 in non-
contrast CT scan images

Estimate the volume
of cardiac chambers
in non-contrast and
contrast-enhanced
CT scan images

Estimate bone
mineral density
within the spine

Estimate the
volume of the
heart
Estimate the
total volume of
calcium in
coronary
arteries
Estimate the
maximum
diameter of the
aorta at typical
landmarks

Estimate
pulmonary tissue
density within
the lungs

systems that raise new issues of safety and/or effectiveness. Thus, AI-CVD® is substantially equivalent to the predicate and reference devices.


Estimate the volume of
skeletal muscle,
subcutaneous fat, and
visceral fat in non-
contrast CT scan images
Estimate coronary artery
calcium score, aortic wall
and valve calcium scores,
and mitral valve calcium
score in non-contrast
ECG-gate and non-gated
chest CT scan images


Automatic
Yes, Deep Learning
Yes, Deep Learning
Yes, Deep
Yes, Deep
Yes, Deep
Yes, Deep

definition of
Learning
Learning
Learning
Learning

User
Healthcare Provider
Healthcare Provider
Healthcare
Healthcare
Healthcare
Healthcare

Operating Linux Linux Linux Windows Windows Windows

mark of the HeartLung Corporation. THIS DOC

© HEARTLUNG CORPORA
TION. ALL RIGHTS RESERVED.


510(k) Number
Device/Manufacturer
Predicate/Reference

DEN220040
Fibresolve / Imvaria, Inc
Predicate

Fibresolve (with PCCP) is substantially equivalent to the following predicate:


Age > 22 years old.
Pulmonary symptoms suggestive of possible
ILD including IPF.

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

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

CT scan
Potentially age, sex, and pulmonary function
tests (PFTs)

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

Algorithm
Machine learning pattern recognition
Machine learning pattern recognition
Same

Qualitative classification output of imaging
findings
Inputs that contribute to the diagnostic result


Modification
Rationale
Testing Methods
Impact Assessment

With advancements in
underlying model architecture
through new computer
science developments,
new model training allows for
improvements in performance
in terms of overall metrics as
well as consistency and speed.

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

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

With new training data, new
model training allows for
improvements in
generalizability, robustness,
and reductions in biases,
which
provides greater

Revised
sensitivity and specificity
metrics for
the system.
Benefit-Risk Analysis:
Benefit: Enhanced
performance;
generalizability.


clinical value. Performance
improvements may also be
achieved.

Addition of non-imaging
ancillary inputs into the model
to improve overall
performance with model
component contributions
displayed with the final result.
improvements enhance
performance and overall
clinical value.

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

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


Product
Marketed by
510(k) Number
Clearance Date

Aplio i900/i800/i700 Diagnostic
Ultrasound System, Software
V8.5


This study included representative images
from 30 patients selected from among
previously acquired data
• Gender: roughly equivalent number of
males and females
• Age: Ranging from 23-89 years old
• Ethnicity (Country): USA
• BMI: ranging from 18.5-36.3 kg/m2,
equally distributed across underweight
or healthy, overweight, and obese
categories
• Images were collected of various
representative abdominal organs and
anatomical structures, as well as
various representative focal
pathologies

Minimum 294 images from demographically
diverse patients acquired at a U.S. clinical site

Three (3) board certified U.S. radiologists
participate in a blind scoring and visibility
evaluation in accordance with Canon’s study
design


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

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

Sandra Soniec
Senior consultant
meditec Consulting GmbH, Switzerland

Email: soniec@meditec-consulting.ch
Phone: +41 31 535 3193


Automated radiological image processing software

Medical Image Management and Processing System


third-party viewer
third-party viewer
third-party viewer

Similar,
comparative
Performance
Evaluation
confirmed
equivalence

Quantitative
Analysis, Volume
in 4D Flow
Workflow

Directional/vector
display of the
blood particle
travel

Automatic
selection of the
Temporal
Landmark time
points


Model Name: 2D Flow Segmentation
Version: 1.0.0
Developer / Manufacturer: AI4CMR
Model Category: Convolutional Neural Network (U-Net architecture)
Primary Function: Automated segmentation of major cardiac vessels on 2D
flow CMR (PC-MRI) images.
Regulatory Classification: Class II, System, Image Processing, Radiological
Short Description:
The model uses a U-Net convolutional neural network to generate pixel-wise
segmentation masks of cardiac vessels (aorta, pulmonary artery) on phase-
contrast MRI images. The segmentation supports calculation of flow and
velocity profiles and quantification.

AI4CMR shall be used by qualified medical professionals, experienced in
examining and evaluating magnetic resonance images as a support tool that
provides relevant clinical information. The software is not intended to
determine or recommend a course of action or treatment for a patient.

AI4CMR supports clinical diagnostics by calculating flow measurements of
vascular structures in 2D phase-encoded cardiac MR images. Its results are
not intended to be used on a stand-alone basis for clinical decision-making.

AI4CMR uses a convolutional neural network–based segmentation model to
outline vascular structures on magnitude and phase images. A user-
selected seed point localizes the vessel, and generated contours may be
manually edited by the clinician.
The model is “locked,” meaning it does not adapt or change with new input
data.

AI-based 2D Flow
Segmentation –
Model Development

The AI-based vessel segmentation model was developed using retrospective
clinical data comprising 167 cardiac MR cases from 61 adult subjects (32%
female, 68% male; age range 17–89 years, mean age 62), obtained from a
tertiary Western European hospital. Images were acquired using standard
cardiac MR 2D phase-contrast flow imaging protocols on 1.5T and 3T
scanners. The dataset included both normal and pathological cases (e.g.,
cardiomyopathies, aortic disease, and valvular disease) and preserved real-
world variability in anatomy, motion artifacts, and image quality, with no
exclusions based solely on image quality.
An expert reader (EACVI level III) independently annotated all cases using
standard segmentation guidelines to ensure consistency and algorithm
generalizability, with intra-reader reliability maintained by following
established standards.


In total, 296 vessel samples (ascending aorta, descending aorta, and
pulmonary artery) were included. Data were stratified by vessel type and split
at the subject level into training, validation, and independent test sets
(70%/15%/15% of subjects), with all vessels and repeated acquisitions from
each subject assigned exclusively to a single dataset to prevent data leakage.
Segmentation performance was evaluated using the Dice Similarity
Coefficient (DSC), with values greater than 0.70 defined a priori as the
acceptance criterion based on established evidence of clinically usable vessel
delineation. On the independent test set, the model achieved DSC values of
0.952 for the ascending aorta, 0.957 for the descending aorta, and 0.952 for
the pulmonary artery, exceeding the predefined acceptance threshold.
Model generalizability was further supported through multi-vendor evaluation
across Siemens, GE, and Philips MRI systems and comparison with a legally
marketed reference device.

Performance was evaluated using internally validated datasets and
independent multi-center data.
Segmentation Performance:
• Dice: 0.95
• Precision: 0.96
• Recall: 0.95
Agreement With Predicate/Reference Measurements:
(In multi-vendor, multi-center evaluation)
• Total Forward Volume (TFV): ICC 0.95
• Total Backward Volume (TBV): ICC 0.82
• Maximum Velocity (Vmax): ICC 0.95

The model was evaluated on images from multiple centers and major MRI
vendors, covering a broad range of adult patient presentations. Validation
included diverse flow encoding settings and scanner configurations.

• Not validated on pediatric patients under 18 years
• Not designed for 4D flow MRI
• Not validated on congenital anomalies with extreme vessel distortion
• Performance may decrease on images with very low signal-to-noise
ratio or poor contrast.

• The segmentation results must be reviewed and, if necessary,
corrected by a qualified clinician.
• Segmentation inaccuracies may lead to incorrect flow quantification.
• Not intended to provide a diagnosis or replace expert clinical
judgment.


• The user provides a seed point to identify the target vessel.
• Segmentation contours are displayed on magnitude and phase
images.
• Manual editing tools allow correction or refinement of the AI-
generated contours.

Version History
Version 1.0, Initial Release


Indications for Use
Please type in the marketing application/submission number, if it is known. This
K252091 ?
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.

Surgical Reality Viewer is a medical imaging visualization software intended to assist trained healthcare
professionals with preoperative and intraoperative visualizations, by displaying 2D and 3D renderings of
DICOM compliant patient images and normal anatomic segmentations derived from patient images as well
as functions for manipulation of segmentations and 3D models.
Surgical Reality Viewer assists the trained healthcare professional who is responsible for making all final
patient management decisions.
The machine learning algorithms in use by Surgical Reality Viewer are intended for use on adult patients
aaed 22 years and over.
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)


510(k) submission - Surgical Reality Viewer


Proprietary / Trade name
Surgical Reality Viewer

Automated Radiological Image Processing Software


Table 1: Comparison of the Indications for Use between the subject device (Surgical Reality Viewer) and Ceevra Reveal 3


Technological characteristic
Subject Device
Predicate


Real-time inter-operative guidance,
No
No

Intra-operative viewing of 3D images Yes Yes


Segmentation work performed by Medical Professional Internal Operator


510(k)
Product Code
Trade Name
Manufacturer

K234009
QIH, LLZ
Acorn 3D Software & 3DP Model
Mighty Oak Medical

K240582
QIH, LLZ
VEA Align; spineEOS
EOS Imaging


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


X-Ray Technique
Median TRE (mm)
3rd Quartile TRE (mm)

points for each x-ray technique, as summarized below:


Device→
Acorn 3D Software
Acorn 3D Software
spineEOS / VEA Align

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

Automated Radiological
Image Processing Software

Manufacturer
Mighty Oak Medical
Mighty Oak Medical
EOS Imaging

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 3D Software is also intended for
measuring and treatment planning.
The Acorn 3D 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

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

VEA Align:
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

Substantial Equivalence Comparison Table


diagnostic purposes in the field of
musculoskeletal and
craniomaxillofacial applications.
The Acorn 3D Alignment and
Measurement module contains
registration capabilities and
measurement functionality based on
anatomical reference geometry. It is
intended to allow the user to align
anatomical structures between
datasets, perform spinopelvic
measurements on 3D models of
anatomy, and plan surgical
procedures in pediatric and adult
patients.
Acorn 3D Software and 3DP Models
should be used in conjunction with
expert clinical judgment.

field of musculoskeletal and
craniomaxillofacial applications.
Acorn Segmentation and 3DP Models
should be used in conjunction with
expert clinical judgment.

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.
spineEOS:
spineEOS is indicated for
assisting healthcare
professionals with
preoperative planning of
spine surgeries. The product
provides access to EOS
images with associated 3D
datasets and
measurements. spineEOS
includes surgical planning
tools that enable users to
define a patient specific
surgical strategy.

Acorn 3D Software is an image
processing software that allows the
user to import, visualize and segment
medical images, check and correct
the segmentations, conduct surgical
planning, and create and manipulate
digital 3D models.

Acorn Segmentation is image
processing software that allows the
user to import, visualize and segment
medical images, check and correct
the segmentations, and create digital
3D models.

Product Code
QIH and LLZ
QIH and LLZ
QIH and LLZ

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 for measuring, treatment
planning and producing an output file

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

VEA Align is a software
indicated for assisting
healthcare professionals
with global alignment
assessment through clinical
parameters computation.


Device→
Acorn 3D Software
Acorn 3D Software
spineEOS / VEA Align

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
 Image registration
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). Semi-automatic and
manual segmentation tools are
intended to be used for all
musculoskeletal anatomy.
Acorn 3DP Model is an additively
manufactured physical replica of the
virtual 3D model generated in Acorn
3D Segmentation. The output file from
Acorn 3D Segmentation is used to
additively manufacture the Acorn 3DP
Model.
The Acorn 3D Alignment and
Measurement module contains
registration capabilities and
spinopelvic measurement
functionality. It is intended to align
spinopelvic anatomical structures
between datasets. The module allows
the user to perform spinopelvic
measurements on 3D models of
anatomy, and to plan surgical
procedures.

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
Acorn Segmentation 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). Semi-automatic and
manual segmentation tools are
intended to be used for all
musculoskeletal anatomy.
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 product uses biplanar
2D X-ray images, exclusively
generated by EOS imaging's
EOS (K152788) and
EOSedge (K202394) systems
and generates an initial
placement of the patient
anatomic landmarks on the
images using a machine
learning-based algorithm.
The user may adjust the
landmarks to align with the
patient's anatomy.
Landmark locations require
user validation. The clinical
parameters communicated
to the user are inferred from
the landmarks and are
recalculated as the user
adjusts the landmarks. 3D
datasets may be exported
for use in spineEOS for
surgical planning.
The product is hosted on a
cloud infrastructure and
relies on VEA Portal for
support capabilities, such as
user access control and
data access. 2D X-ray
image transmissions from
healthcare institutions to the
cloud are managed by VEA
Portal. VEA Portal is a Class I
510(k)-exempt device
(LMD).
spineEOS is a software
indicated for assisting
healthcare professionals
with preoperative planning
of spine surgeries. EOS
images (generated from
EOS imaging’s acquisition
system) and associated 3D
datasets are used as inputs
of the software. The product
manages clinical
measurements and allows
user to access surgical
planning tools to define a
patient specific surgical
strategy. The product is
indicated for adolescent
and adult patients.

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 Alignment &
Measurement module is intended for

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.


use by medical professionals, such as
clinicians and surgeons, who are
trained in spinal procedures and the
interpretation of diagnostic imaging.

Acorn 3D Software is a standalone
modular software package. This
software package includes, but is not
limited to the following functions:
Acorn 3D - Segmentation
Image Import
 Importing medical images in
DICOM format
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
Image Export
 Exporting images and 3D models
to third-party packages
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
Acorn 3D – Alignment and
Measurement
Registration

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
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
Image Export
 Exporting images and 3D models
to third-party packages
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

spineEOS / VEA Align
includes the following
functionality:
Image Import
 Import of medical data
Visualization
 Display of data
 Switching of view
orientation
 Basic manipulation
(zoom, panning,
annotations)
Measurement
 Distance and angle
measurements on 2D
images and 3D models
 Clinical Parameters:
• Pelvic Tilt (PT)
• Sacral Slope (SS)
• Pelvic Incidence
(PI)
• Pelvic Obliquity
(PO)
• Sagittal Vertical
Axis (SVA)
• C7-CSL
• PI-LL
• T1 Pelvic Angle
(TPA)
• Cobb Angle
• Kyphosis/Lordosis
Angle
• Knee
Flexion/Extension
Angle
• Lordosis
Percentage
Distributions
• Spondylolisthesis
grade (i.e.,
Slippage
percentage)
Registration
 Initial placement of
anatomic landmarks
on images using a
machine-learning-
based algorithm
 3D reconstruction
model initialized by AI
algorithm
 Manual deformation of
resulting 3D model
through control points


 Alignment of anatomical
structures between datasets
Measurement
 Standard and generic 3D
measurements of spinopelvic
anatomy based on user-defined
reference geometry

 Verification of
deformation result by
matching accurately
the X-ray contours
Data Export
 Export of medical data

The Acorn 3D Segmentation module
automates the segmentation of
particular anatomy (listed in IFU) by
implementing a 3D U-Net machine
learning model. Segmentation of the
images is completed by generating an
input image that is preprocessed
(image processing methods) and then
is run through an analysis (encoder)
path and a synthesis (decoder) path.
When the image is going through the
analysis path, the model is learning by
focusing on new information
presented and by dynamically
learning which information from the
image is the most useful. The image
then goes through the synthesis path in
which the model recovers spatial
resolution and focus on salient features
that the analysis path coded. The
parameters of the model were
obtained through an algorithm
development pipeline.

Acorn 3D – Segmentation
 Vertebral model (T1-T12, L1-L5)
 Sacral model

Acorn Segmentation
 Vertebral model (T1-T12, L1-L5)

The Acorn 3D 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 and/or
intraoperative reference of anatomy
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 and/or
intraoperative reference of anatomy
in the field of orthopedic and
musculoskeletal applications.


Additional implementation
of Sacral model

The sacrum is important to the spinopelvic pre-surgical planning workflow; auto-segmentation
of the sacrum is intended to increase the speed and accuracy of the software.


(DSC), a well-established metric for measuring the spatial overlap of two objects.


Model
Testing Dataset
Training
Tuning
Testing

and testing for each model is provided in the table below.


Attribute
Predicate Device: K210791
Subject Device

Software package that operates
utilizing off-the-shelf hardware

Software package that
operates utilizing off-the-
shelf hardware

The software is designed for use
on a personal computer that has
received images from a
compatible PACS

Fully automated, including clip
selection

Manual editing
of automated
result by user

Input Data
Echocardiographic images
Echocardiographic images


characteristics of the cohort are summarized in the table below.


Moderately reduced LVEF (30-40%), n (%)
85 (16.22%)


Ultrasonic Pulsed Doppler Imaging System

Ultrasonic Pulsed Echo Imaging System
21 CFR 892.1560
IYO

Diagnostic Ultrasound Transducer
21 CFR 892.1570
ITX

Medical Image Management and Processing
System


Device Trade Name:
Butterfly iQ3 Ultrasound System

Primary Product Code:
IYN – 21 CFR 892.1550


Device Trade Name:
Voluson Expert Series


Medical electrical equipment - Part 1-11: General requirements
for basic safety and essential performance - Collateral Standard:
Requirements for medical electrical equipment and medical
electrical systems used in the home healthcare environment
CONSOLIDATED EDITION

Medical electrical equipment - Part 1-12: General requirements
for basic safety and essential performance - Collateral Standard:
Requirements for medical electrical equipment and medical
electrical systems intended for use in the emergency medical
services environment CONSOLIDATED EDITION

Medical electrical equipment - Part 2-5: Particular requirements
for the basic safety and essential performance of ultrasonic
physiotherapy equipment

Medical Electrical Equipment – Part 2-37. Particular
requirements for the basic safety and essential performance of
ultrasonic medical diagnostic and monitoring equipment
CONSOLIDATED EDITION

Biological evaluation of medical devices — Part 1: Evaluation
and testing within a risk management process

Biological evaluation of medical devices — Part 5: Tests for in
vitro cytotoxicity

Biological evaluation of medical devices — Part 10: Tests for
irritation and skin sensitization

Medical devices - Application of risk management to medical
devices


Site Number
Site 1
Site 2
Site 3
Site 4
Total

Table 1: Subjects demographic information


Native Hawaiian or Other Pacific
Islander


Butterfly Gestational
Age with iQ+ vs LMP in
days

Butterfly Gestational Age with iQ+ vs
Biometry Gestational Age in days

the Butterfly GA Tool with iQ+ against Biometry.


Butterfly Gestational
Age with iQ3 vs LMP
in days

Butterfly Gestational Age with iQ3 vs
Biometry Gestational Age in days

the Butterfly GA Tool with iQ3 against Biometry.


Butterfly
Gestational Age
Tool (Subject
Device)

Butterfly iQ3
Ultrasound
System
(K232808,
Predicate
Device)

Ultrasonic Pulsed
Doppler Imaging
System

Intended for use by
qualified and trained
healthcare
professionals to enable
diagnostic ultrasound
imaging and
measurement of
anatomical structures
and fluids of adult and
pediatric patients.

Hand-held portable
diagnostic ultrasound
system.

New feature -
similar feature to
manual biometry
calculation on the
predicate device.


Classification name:
Accelerator, Linear, M

Predicate Trade Name:
Elekta Synergy, Elekta


Summary of technological characteristic/
Summary of comparison

230 MU/min 230 230 MU/min MU/min MU/min MU/min MU/min MU/min

25 MV:
MU/min
25 MV:
25 MV:
MU/min
MU/min
6MV FFF:
MU/min
MU/min

MU/min
MU/min
MU/min
600 MU/min
600 MU/min
10MV FFF:
600 MU/min
600 MU/min

Each energy is delivered with the same dose

rate in every model of the predicate and the

MU/min
MU/min
MU/min
10MV FFF:
MU/min
MU/min
10MV FFF:

Elekta Harmony Pro supports electron energies,

while the predicate Elekta Harmony does not.

Elekta Evo supports the same energies as the

(22 MeV
(22 MeV with
predicate Versa HD.

The other models: the subject devices have the

not
same characteristics as the predicate

600
600 Mu/min
600 Mu/min
600 Mu/min
600 Mu/min
600 Mu/min

Electron High
4 to 10
4 to 10
Elekta Harmony Pro does not support the

Electron High Dose Rate (Total Skin Mode),

MeV:
MU/min
MU/min
4 to 10 MeV:
4 to 10 MeV:

Elekta Evo supports the Electron High Dose

Rate (Total Skin Mode), same as its predicate


Summary of technological characteristic/
Summary of comparison

The MLC forms the end of the gantry arm that

rotates around the treatment area of the

Leaves Collimator)
patient and modifies the beam shape in

accordance with the predefined treatment plan to

The predicate device Synergy supports both the

Agility BLD and MLCi2 BLD, while the subject

device Synergy supports the Agility BLD. The

performance of Agility covers the performance of

Conformal Radiation Therapy (CRT) without Image Guided adiation Therapy (IGRT),

Intensity-Modulated Radiation Therapy (IMRT) or Volumetric Modulated Arc Therapy

(IMRT) or Volumetric Modulated Arc
(VMAT) delivered using IGRT
Same

4-axis
Table
(with
iBEAM®
evo
Couchtop
EH)

Precise
Treatmen
t Table
(with
iBEAM®
evo
Couchtop
EP)
or
Precise
Treatmen
t Table
(with
HexaPOD
evo RT
System)

Precise
Treatment
Table
(with
iBEAM®
evo
Couchtop
EP)
or
Precise
Treatment
Table
(with
HexaPOD
evo RT
System)

Harmony Pro has the same Patient Support
System with couch top as Harmony.
Elekta Evo has one of the two Patient Support
System with couch top of Versa HD, i.e.
HexaPOD evo RT System, which has the
highest performance.
The other models: the subject devices have the
same characteristics as the predicate

The iViewGT™ imaging system is an Electronic Portal Imaging Device (EPID).

It provides the MV IGRT solution that uses the


Summary of technological characteristic/
Summary of comparison

Electronic Portal Imaging Device (EPID).
(Common to all models)

X-ray radiation of the digital accelerator and an

MV detector panel to capture an image of the

patient. The image shows the position of the

patient in relation to the radiation beam.

The imaging function is the same. However, the

control system of the subject device has

KV Imaging device
control system
and imaging
functions

The XVI imaging system is an electronic
imaging device for kV image acquisition.
(Common to all models)

The XVI imaging system is an electronic
imaging device for kV image acquisition.
(Common to all models. Image quality
improved)

The XVI imaging system is an electronic
imaging device for kV image acquisition.
(Common to all models. Image quality
improved.
A CBCT High Definition (HD) reconstruction
solution with further improved CBCT image
quality is available in the pelvic anatomies,
commercially referenced as Iris)

The control system of the subject devices has
improvements to cybersecurity.
The volume reconstruction function of all the
subject devices has been improved.
The volume reconstruction method used in the
predicate devices (based on the reconstruction
algorithm Feldkamp-Davis-Kress (FDK)) has
been improved and adopted by all the subject
devices. This results in improved image quality.
The subject devices Versa HD, Elekta Harmony
Pro and Elekta Evo also support a new CBCT
reconstruction method with further improved
image quality in the pelvic anatomies. It includes
an AI-ML based component to estimate the
scatter to enable its automatic removal from the
projection images acquired by the imager ahead
of the volume reconstruction.

Harmony and Harmony Pro imaging panels can
not be folded, while maintaining the retractable
functionality.

Beam Gating
interface to
external Patient
Positioning
Monitoring
Systems

Response, which is a proprietary interface.

The subject devices use the Integrated gating

interface, which has been designed to comply

with the IEC60601-2-1:2020 standard and is


Summary of technological characteristic/
Summary of comparison

between the two standards and in such cases

the requirements of the IEC60601-2-1 standard


Medical electrical equipment - Part 1: General requirements for basic
safety and essential performance

Medical electrical equipment - Part 2-1: Particular requirements for the
safety of electron accelerators in the range of 1 MeV to 50 MeV

Medical electrical equipment - Part 2-68: Particular requirements for the
basic safety and essential performance of X-ray-based image-guided
radiotherapy equipment for use with electron accelerators, light ion beam
therapy equipment and radionuclide beam therapy equipment

IEC 60601-1-3 (to the
extent required
normatively by the
IEC 60601-2-68)

Medical electrical equipment - Part 1-3: General requirements for basic
safety and essential performance - Collateral Standard: Radiation
protection in diagnostic X-ray equipment

Radiotherapy equipment - Coordinates, movements and scales

Medical electrical equipment. Medical electron accelerators. Functional
performance characteristics

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

Medical devices - Application of risk management to medical devices

Medical electrical equipment - Part 1-6: General requirements for basic
safety and essential performance - Collateral standard: Usability

Medical devices - Application of usability engineering to medical devices

Medical device software - Software life-cycle processes

Medical devices - Information to be supplied by the manufacturer

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

Guidance on the Application of ISO 14971 to Artificial Intelligence and
Machine Learning

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


Applicant Name
Canon Medical Informatics, Inc.

Applicant Address
5850 Opus Parkway, Suite 300

Jay Vaishnav, PhD, RAC, FRAPS
Director, Regulatory Affairs
Phone 952-487-9530
E-mail Jay.Vaishnav@mi.medical.canon


Classification Name
Computed tomography x-ray system

Common Name
System, X-Ray, Tomography, Computed


Predicate #
Predicate Trade Name
Product Code

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