ROIsplacedonthyroid
nodules
TI-RADSlexicondescriptors
TI-RADScategoryaccording
totheACRTI-RADSchart

ROIsandlesion
contoursplacedon
suspicioussoft
tissuelesion
BI-RADSlexicondescriptors
Aregion-basedscore
oflesionmalignancy
BI-RADScategory

Thyroid
TI-RADSlexicondescriptors
basedonuser-selectedROIs
TI-RADScategoryaccording
totheACRTI-RADSchart
Adirect,deep-learning
derivedcancerrisk
assessmentthatistranslated
intoanoptional
lexicon-specificmodifier.
Thesoftware’sdirect,
non-descriptor-basedcancer
riskassessmentispresented
astheKoios“AIAdapter”that
canbeusedinconjunction
withtheACRTI-RADSorATA
guidelinesfornodulerisk
stratification
Breast
Categoricalandcontinuous
outputs(confidencelevel
indicator)thatalignto
BI-RADScategories
Autoclassificationof
BI-RADSlexicondescriptors
(shapeandorientation)


Radiological Computer Assisted Detection And Diagnosis Software


Table: Primary endpoint results for ARIA detection performance. Results are reported for assisted, unassisted and
standalone software. Effect size is defined as the difference between assisted and unassisted AUC, and the p-value
corresponds to the hypothesis test on the AUC difference.

no ARIA-E vs. (mild,
moderate or severe)
ARIA-E

no ARIA-H vs. (mild,
moderate or severe)
ARIA-H

no ARIA-H
microhemorrhages
vs. (mild, moderate or
severe) ARIA-
H microhemorrhages

no ARIA-H superficial
siderosis vs. (mild,
moderate or severe)
ARIA-H superficial
siderosis

types, except for a specificity of 76.7% for the detection of (mild) ARIA-H microhemorrhages.


The clinical performance assessment multiple-reader multiple-case (MRMC) study demonstrated a
statistically significant improvement in reader performance in detecting ARIA-E and ARIA-H in the intended
patient population, as measured by the co-primary endpoints of the difference in Area Under the Curve for
assisted radiological reading with icobrain aria as compared to the same task without icobrain
aria according to clinical standard of care:
ARIA-E detection: AUC - AUC = 0.873 - 0.822 = 0.051 (two sided 95% confidence interval [0.020,
assisted unassisted
0.083])
ARIA-H detection: AUC - AUC = 0.825 - 0.781= 0.044 (two sided 95% confidence interval
assisted unassisted
[0.017, 0.070]).
The MRMC study also demonstrated that mean sensitivity increased significantly from 70.9% (unassisted)
to 86.5% (assisted) for ARIA-E detection, and from 68.7% to 79.0% for ARIA-H detection, while the device-
assisted specificity remained above 80% for the detection of both ARIA types.
Large diagnostic gains included an increase in assisted versus unassisted performance for mild ARIA-E and
mild ARIA-H detection (AUC - AUC = 0.071 for ARIA-E and AUC - AUC = 0.042 for
assisted unassisted assisted unassisted
ARIA-H), notably a much higher sensitivity, at no downside in performance for the easier tasks of detecting
moderate-or-severe ARIA-E and ARIA-H. Earlier detection of subtle ARIA findings will allow earlier
intervention and mitigate ARIA progression through appropriate decisions on dosing continuation or
suspension.
The inter-reader variability in ARIA severity determination was also significantly lower for assisted versus
unassisted reads (Kendall's coefficient of concordance of 0.720 for unassisted and 0.809 for assisted ARIA-E
severity assessments, and of 0.656 for unassisted and 0.799 for assisted ARIA-H severity assessments), and
readers were on average faster (median unassisted reading 2:34min; median assisted reading 2:21min) when
performing assisted reading with icobrain aria.

There are potential risks associated with use of the device, including:
• The device could provide false positive results (i.e., falsely detecting ARIA-E if there is no ARIA-E, false
detecting ARIA-H if there is no ARIA-H), which could contribute to the radiologist using this information
to make a false positive diagnosis (i.e., diagnosing ARIA-E when there is no ARIA-E, diagnosing ARIA-H
when there is no ARIA-H, overestimating mild ARIA-E or mild ARIA-H as moderate-or-severe ARIA-E or
ARIA-H, respectively). Such a false positive diagnosis can result in unnecessary additional MRI follow-up,
unnecessary suspension of amyloid beta-directed antibody treatment, delay in treatment (if false
positive findings correspond to symptoms caused by some other condition), HCP/patient concern,
inconvenience, discomfort from additional MRI, delay to potential benefit due to suspension of the
amyloid beta-directed antibody treatment, ...
• The device could provide false negative results (i.e., not detecting actual ARIA-E or ARIA-H findings),
which could contribute to the radiologist using this information to make a false negative diagnosis (e.g.,
not diagnosing ARIA-E when there is ARIA-E, not diagnosing ARIA-H when there is ARIA-H,
underestimating moderate-or-severe ARIA-E or ARIA-H as mild ARIA-E or ARIA-H, respectively). A false
negative diagnosis could lead to delayed diagnosis, delayed suspension of treatment when necessary,
symptoms exacerbating, no follow-up MRI, ...
• The device could be misused to analyze brain images from other patient populations or incompatible
image acquisition parameters, leading to inappropriate information regarding the presence/location of
imaging abnormalities provided to the end user.
• The device could fail and lead to absence of results, delay of results, or incorrect results, which can lead
to delayed or inaccurate patient diagnosis.


Trade Name
Trade Name
SMART Bun-Yo-Matic
Bonelogic

Weight-bearing plain film
X-Rays from sagittal and
transverse views

Computed tomography
DICOM Computed
tomography

Automated case report of
the 3D axes of patient
anatomy, surgical
instrument parameters, and
visualization of implant

3D model of patient anatomy,
automated case report

Perform measurements for
presurgical planning

Graphical user interface
(GUI) to a web application
used with a standard web
browser.

Graphical user interface
(GUI) that is standalone
application based.


Medical Image Management and Processing System

Automated Radiological Image Processing Software


Lung Nodule Assessment and Comparison Option (LNA)

Manufacturer:
Philips Medical Systems Nederland B.V.

Regulation Number:
21 CFR 892.2050; 21 CFR 892.1750


Table 2 Standalone Performance Testing Results for qCT LN Quant


the subject and predicate devices are medical image analyzers intended to read chest CT scans to

quantitatively characterize user identified lung nodules. The algorithms function similarly and with the

same purpose of quantitative characterization of lung nodules

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


The uMR 680 system is indicated
for use as a magnetic resonance


diagnostic device (MRDD) that
produces sagittal, transverse,
coronal, and oblique cross
sectional images, and
spectroscopic images, and that
display internal anatomical
structure and/or function of the
head, body and extremities.
These images and the physical
parameters derived from the
images when interpreted by a
trained physician yield
information that may assist the
diagnosis. Contrast agents may be
used depending on the region of
interest of the scan.

The intended use of
Breast coil -24 is
equivalent to previously
cleared Breast Coil-10.
The difference between
them is the number of
channels of the receiver
coil.
The difference did not
raise new safety and
effectiveness concerns.


The maximum temperature
of all temperature probes
shall not exceed 41℃.

Perform test according to
ANSI/AAMI ES60601-1

Perform test according to
NEMA MS 1, NEMA MS 3,
NEMA MS 6 and NEMA MS 9

SNR and Uniformity shall
fulfill with the design
specification.

Biocompatibility evaluation in
agreement with
recommendations in Use of
International Standard ISO
10993-1, "Biological evaluation
of medical devices - Part 1:
Evaluation and testing within a
risk management process"

Materials of construction
and manufacturing
materials exempt from
testing according to the
Biocompatibility guidance
(Attachment G), the 510(k)
numbers for devices where
these materials have been
previously approved, or full
biocompatibility report
(assessment of
sensitization, irritation and
cytotoxicity risks) for
components that have direct
contact with the patient.

All the materials of
patient-
contacting components for
the Breast Coil - 24 are
identical to uMR Omega
which was cleared in
K230152 in formulation,
processing, sterilization,
and geometry, and no
other chemicals have been
added (e.g., plasticizers,
fillers, additives, cleaning
agents, mold release
agents).

Perform test according to IEC
60601-1-2 and IEC 60601-4-2

Conform with IEC 60601-
1-2 and IEC 60601-4-2

Evaluate the image generated by
Breast Coil-24 with the same
method as the predicate device

Image quality is sufficient
for diagnostic use.

The U.S. Board Certified
radiologist approves that
image quality is sufficient
for diagnostic use.

Perform a phantom and
volunteer test under the
proposed device with MRE

Bias of accuracy,
repeatability,
reproducibility and
parameter sensitivity shall
fulfill with the design
specification.


#
Features/ Characteristics
Submitted Device
Predicate Device

LVivo platform is
intended for non-
invasive processing
of ultrasound
images to detect,
measure, and
calculate relevant
medical parameters of
structures and
function of patients
with suspected
disease. In addition, it
provides Quality Index
Score Feedback.

LVivo IQS for LV from 4-
Chamber View
LVivo IQS for RV from 4-
Chamber View

Ram: 8 GB
Windows 10 or higher
Processor: Intel core i3
compatible or higher

0-60% poor (red),
60-80 % medium (orange)
80-100 % good (green)


In 85% of the patients with image quality 3-5
by visual estimation it was possible to obtain
at least “Medium” quality score by LVivo IQS

92% of the above saved clips were clinically
interpretable


Manufacturer
TechsoMed Medical Technologies. LTD.

Medical Image Management and Processing System


VisAble.IO (Ver 1.4)
VisAble.IO (Ver 1.0)

SUBSTANTIAL EQUIVALENCE COMPARISON TABLE


undergo ablation treatment
(including patients with soft
tissue lesions)

VisAble.IO is a Computed
Tomography (CT) and
Magnetic Resonance (MR)
image processing software
package available for use with
liver ablation procedures.
VisAble.IO is controlled by the
user via a user interface.
VisAble.IO imports images
from CT and MR scanners and
facility PACS systems for
display and processing during
liver ablation procedures.
VisAble.IO is used to assist
physicians in planning ablation
procedures, including
identifying ablation targets and
virtual ablation needle
placement. VisAble.IO 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.

VisAble.IO is a Computed
Tomography (CT) image
processing software package
available for use with liver
ablation procedures.
VisAble.IO is controlled by the
user via a user interface.
VisAble.IO is controlled by the
user via a user interface.
VisAble.IO imports images from
CT and MR scanners and facility
PACS systems for display and
processing during liver ablation
procedures.
VisAble.IO is used to assist
physicians in planning ablation
procedures, including identifying
ablation targets and
virtual ablation needle
placement. VisAble.IO 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.

User Population
Qualified trained physicians
Qualified trained physicians

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

VisAble.IO 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


hospital healthcare
environment such as
interventional radiology control
room. VisAble.IO has five
distinct workflow steps:
• Data Import
• Anatomic Structures
Segmentation
• Instrument Placement
(Needle Planning)
• Ablation Zone
Segmentation
• 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,
ablation zones, vessels and
liver.

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.


Algorithm
N
Gender
Mean
N per
MR /CT Brand
Performance
Performance

Pre-ablation CT to
Post Ablation CT
Image Registration

Pre-ablation MR to
Post-ablation CT
Image Registration

MR: GE Medical
Systems, Philips,
Siemens
CT: GE Medical
Systems, Philips,
Siemens,
Toshiba

The following table provides a summary of the validation results:


CLASSIFICATION NAME:
Radiological Image Proc

REGULATION DESCRIPTION:
Picture archiving and co


ADAS 3D is indicated for use in clinical settings
to support the visualization and analysis of MR
and CT images of the heart for use on
individual patients with cardiovascular
disease.
ADAS 3D is indicated for patients with
myocardial scar produced by ischemic or non-
ischemic heart disease. ADAS 3D processes MR
and CT images. The quality and the resolution
of the medical images determines the
accuracy of the data produced by ADAS 3D.
ADAS 3D is indicated to be used only by
qualified medical professionals (cardiologists,
electrophysiologists, radiologists or trained
technicians) for the calculation, quantification
and visualization of cardiac images and
intended to be used for pre-planning and
during electrophysiology procedures. The data

modifications of the subject device to the predicate device.


produced by ADAS 3D must not be used as an
irrefutable basis or a source of medical advice
for clinical diagnosis or patient treatment. The
data produced by ADAS 3D is intended to be
used to support qualified medical
professionals for clinical decision making.
The clinical significance of using ADAS 3D to
identify arrhythmia substrates for the
treatment of cardiac arrhythmias (e.g.,
ventricular tachycardia) or risk stratification
has not been established.

Device
Description
(Including
Functional and
Technological
Characteristics)

ADAS 3D is a stand-alone software tool
designed for post-processing cardiovascular
enhanced Magnetic Resonance (MR) images
and Computed Tomography Angiography
(CTA) images that are formatted in the Digital
Imaging and Communication in Medicine
(DICOM) standard. ADAS 3D software aids in
the non-invasive calculation, quantification
and visualization of cardiac imaging data to
support a comprehensive diagnostic decision-
making process for understanding
cardiovascular disease.
ADAS 3D exports information to multiple
industry standard file formats suitable for
documentation and information sharing
purposes. The 3D data is exported into
industry standard file formats supported by
catheter navigation systems.
ADAS 3D analyses the enhancement of
myocardial fibrosis from DICOM MR images to
support:

ADAS 3D is a stand-alone software tool designed
for post-processing cardiovascular enhanced
Magnetic Resonance (MR) images and
Computed Tomography Angiography (CTA)
images that are formatted in the Digital Imaging
and Communication in Medicine (DICOM)
standard. ADAS 3D software aids in the non-
invasive calculation, quantification and
visualization of cardiac imaging data to support
a comprehensive diagnostic decision-making
process for understanding cardiovascular
disease.
ADAS 3D exports information to multiple
industry standard file formats suitable for
documentation and information sharing
purposes. The 3D data is exported into industry
standard file formats supported by catheter
navigation systems.
ADAS 3D analyses the enhancement of
myocardial fibrosis from DICOM MR images to
support:


● Visualization of the distribution of the
enhancement in a three-dimensional
(3D) chamber of the heart.
● Quantification of the total volume of
the enhancement within the Left
Ventricle (LV) and the visualization of
the enhancement area in multiple
layers through the cardiac structure.
● Calculation, quantification and
visualization of corridors of
intermediate signal intensity
enhancement in the LV.
● Quantification and visualization of the
total area and distribution of the
enhancement within the left Atrium
(LA).
Additionally, ADAS 3D imports DICOM CTA
images to support:
● Quantification of LV wall thickness.
● Identification and Visualization of
other 3D anatomical structures.
● Quantification and visualization of LA
wall thickness.
● Quantification and visualization of
distances from the LA epicardium to
other 3D anatomical structures.
Additionally, ADAS 3D imports DICOM Magnetic
Resonance Angiography (MRA) images to
support:
● Identification and Visualization of
other 3D anatomical structures.
It is designed to be used by qualified medical
professionals (cardiologists, radiologists or


Additionally, ADAS 3D uses the following
machine-learning-based features:
● Standard Initialization of the LV, LA,
and Aorta from CTA
● Standard Initialization of the
Coronary Arteries from CTA
● Standard Initialization of the LA from
CTA
● Standard Initialization of the LV from
2D LGE-MRI and Automatic Slice
Alignment
● Standard Initialization of the LV from
3D LGE-MRI
● Standard Initialization of the LA from
3D LGE-MRI
It is designed to be used by qualified medical
professionals (cardiologists, radiologists or
trained technicians) experienced in examining
and evaluating cardiovascular MR and CTA
images as part of the comprehensive
diagnostic decision-making process.


Subject Device
ADAS 3D
(Adas3D Medical S.L.)

Added support for
Linux RHEL 8 and
Windows 11

Semi-automatic using Machine
Learning technique:
● Left Chambers from CTA
● Left Ventricle from 2D DE-MRI
● Coronaries from CTA
● Left Ventricle from 3D DE-MRI
● Left Atrium from 3D DE-MRI
● Left Atrium Wall Thickness
from CTA

Semi-automatic using Machine
Learning technique:
● Left Chambers from CTA
● Left Ventricle from 2D DE-MRI
● Coronaries from CTA
Manual:
● Left Ventricle from 3D DE-MRI
● Left Atrium from 3D DE-MRI
● Left Atrium Wall Thickness from
CTA

Added three new
semi-automatic
segmentations using
Machine Learning
technique.
Improved the already
existing three semi-
automatic
segmentations.


Standard Initialization of
the Left Chambers and
Aorta from CTA

Standard Initialization of
the Coronaries from CTA

Toshiba (48%), Siemens (37%), GE (14%) and Philips
(1%).

GE (65%), Toshiba (32%) and Philips (3%)

Standard Initialization LV
from 2D DE-MRI

Standard Initialization of
the LV from 3D DE-MRI

Standard Initialization of
the LA from 3D DE-MRI

The details of the training dataset for each machine learning feature are summarized in the table below.


Standard Initialization
of the Left Chambers

US: SIEMENS (61%), Toshiba (32%) and GE (7%)

development, including training. The details of each testing dataset are summarized in the table below.


OUS: SIEMENS (47%), Toshiba (26%), Canon (13%), GE
(11%) and Philips (3%)

Standard Initialization
of the Coronaries from
CTA

US: SIEMENS (64%), Toshiba (26%), GE (8%) and
Phillips (2)
OUS: SIEMENS (47%), Toshiba (28%), Canon (14%), GE
(8%) and Philips (3%)

US: SIEMENS (58%), Toshiba (34%) and GE (8%)
OUS: SIEMENS (53%), Toshiba (30%), Canon (11%), GE
(3%) and Philips (3%)

Automatic Slice
Alignment for LV from
2D DE-MRI

US: SIEMENS (72%), Philips (25%) and GE (3%)
OUS: Philips (58%), SIEMENS (18%) and GE (24%)

Standard Initialization
of the LV from 2D DE-
MRI

US: SIEMENS (71%), Philips (23%) and GE (6%)
OUS: Philips (50%), SIEMENS (19%) and GE (31%)

Standard Initialization
of the LV from 3D DE-
MRI

US: SIEMENS (62%), Philips (32%) and GE (6%)
OUS: SIEMENS (49%), Philips (45%), Toshiba (3%) and
GE (3%)

Standard Initialization
of the LA from 3D DE-
MRI

US: Philips (50%), SIEMENS (45%) and GE (5%)
OUS: SIEMENS (71%), Philips (26%) and GE (3%)


Standard
Initialization of
the Left
Chambers and
Aorta from CTA

Standard
Initialization of
the Coronary
Arteries from
CTA

Standard
Initialization of
the LA from CTA

Automatic Slice
Alignment for LV
from 2D LGE-MRI


Standard
Initialization of
the LV from 3D
LGE-MRI

Standard
Initialization of
the LA from 3D
LGE-MRI


Comparison of Technology Characteristics with Predicate Device

Predicate Device
Subject Device
Remarks Discussion

· Radiologist
· Neurosurgeons and
Ortho/Spine Surgeons

Not subject to restrictions
other than above the age of
18, not pregnant, without
postoperative complication,
scoliosis, tumors, infections,
fractures

Not subject to restrictions
other than above the age of
18, not pregnant, without
postoperative complication,
tumors, infections, complex
hardware

· Similar to Predicate
Device and Reference
Device 2
· Different from Reference
Device 1

Yes, Segmentation and
Quantitative Analysis

Yes, Quantitative comparison
of structure with normative
data or user set thresholds

Yes, Quantitative comparison
of structure with normative
data.

Yes, Exploration of results
with the findings for further
reporting.

Same. Reports
radiologists of
physicians evaluate,
authorized and modify
reports

Deep Convolutional Image to
Image Neural Network

Mask Region-based
Convolutional Neural Network

Same as Predicate
device, similar to
reference devices.


Comparison of Technology Characteristics with Reference Device (1)

Patient population above the
age of 22 years old.

Yes, Quantitative comparison
of structure with normative
data or user set thresholds.


Comparison of Technology Characteristics with Reference Device (2)

Predicate Device
Subject Device
Remarks Discussion

AI-Rad Companion
Musculoskeletal
(K193267)

· Radiologist
· Primary Care Physicians
· Specialty/Urgent Care
Physicians

· Radiologist
· Neurosurgeons and
Ortho/Spine Surgeons

Not subject to restrictions
other than above the age of
22.

Not subject to restrictions
other than above the age of
18, not pregnant, without
postoperative complication,
tumors, infections, complex
hardware

· Similar to Predicate
Device and Reference
Device 2
· Different from Reference
Device 1

Yes, Segmentation and
Quantitative Analysis

Yes, Quantitative comparison
of structure with normative
data or user set thresholds

Yes, Quantitative comparison
of structure with normative
data.

Yes, Exploration of results
with the findings for further
reporting.

Same. Reports
radiologists of
physicians evaluate,
authorized and modify
reports

Mask Region-based
Convolutional Neural Network

Same as Predicate
device, similar to
reference devices.


IEC 62304:2006/AMD 1:2015 Medical device software — Software life cycle processes
— Amendment 1

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

IEC 62366-1:2015+AMD1:2020 Medical devices — Part 1: Application of usability
engineering to medical devices

ISO 15223-1:2016 Medical devices — Symbols to be used with medical device labels,
labelling and information to be supplied — Part 1: General requirements

NEMA PS 3.1 - 3.20 (2016) Digital Imaging and Communications in Medicine (DICOM)
Set

Consensus Standards listed in the table below.


Software verification and validation testing


AZmed SAS
6 rue Léonard de Vinci
53000 Laval, France
Phone: +33 6 43 31 51 38

Christelle BAILLE
Head of QARA
6 rue Léonard de Vinci
53000 Laval, France
Phone: +33 6 43 31 51 38
Mail: christelle@azmed.co


Rayvolve is a
computer-assisted
detection and diagnosis
(CAD) software device to
assist radiologists and
emergency physicians in
detecting fractures during
the review of radiographs
of the musculoskeletal
system.


pediatric
(≥ 2 years) as
well as adult
patients.

Ankle
Clavicle
Elbow
Forearm
Hip
Humerus
Knee
Pelvis
Shoulder
Tibia/fibula
Wrist
Hand
Foot

Clinical findings
Fractures
Fractures
Same

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

PACS system
Image annotations
toggled on or of

Privacy
HIPAA Compliant
HIPAA Compliant
Same

On-premise, on cloud,
secure local processing
and delivery of DICOM
images (eg:PACS)

Electromagnetic
compatibility
and electrical
safety

N/A, Rayvolve is a
standalone software and
is not subject to


electromagnetic testing.
Therefore no
electromagnetic
compatibility and
electrical safety is
required.

N/A, Rayvolve is a
standalone software and
is not subject to magnetic
resonance. Therefore no
magnetic testing is
required.

N/A, Rayvolve is a
standalone software with
no direct or indirect
patient or user contacting
components. Therefore
no biocompatibility is
required.


Classification Description
21 CFR §
Product Code

System, imaging, pulsed doppler,
ultrasonic

System, imaging, pulsed echo,
ultrasonic

Transducer, ultrasonic, diagnostic
892.1570
ITX

Automated Radiological Image
Processing Software

Diagnostic Intravascular Catheter
870.1200
OBJ*

Common Name Diagnostic Ultrasound System and Transducers


EPIQ Series Diagnostic
LVivo Seamless
LVivo Software

Feature
Features: SVS and
K212466
Feature: LVivo SG
Comparison

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

LVivo platform is
intended for non-
invasive processing of
ultrasound images to
detect, measure, and
calculate relevant
medical parameters of
structures and function
of patients with
suspected disease.

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

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


Class II
Class II
Class II
Class II
Identical to predicate

System, Imaging,
Pulsed Doppler,
Ultrasonic

Medical image
management and
processing system.

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

Reusable-
Yes
Yes
No, software-only
No, software-only
Identical to predicate

Limited (≤ 24 hours)
Limited (≤ 24 hours)
N/A, software-only
N/A, software-only
Identical to predicate

Track 3
Track 3
N/A, software-only
N/A, software-only
Identical to predicate

Smart View Select is an
automated software
feature that assists the
user in selection of
images for analysis
with the existing Philips
AutoStrain LV or 2D
Auto LV application in
Adult Echo
Transthoracic (TTE)
examination.
The SWM software
automatically evaluates
the segmental
(regional) function of
the left ventricle (LV)
from adult TTE echo
examinations.
Note: Per FDA
Guidance Technical

The predicate EPIQ
Series Diagnostic
Ultrasound System
does not currently have
a dedicated software
application containing
the functionality
introduced in the
subject submission for
(1) automatic selection
of 4CH,2CH, and 3CH
views for left ventricle
(LV) analysis or (2)
(semi-) automated
segmental wall motion
evaluation of the left
ventricle (LV).

The LVivoSeamless
software is a
standalone application
that extends the LVivo
Platform and runs
offline on a server. The
system accepts echo
examination that are
sent from the US
device using DICOM
communication. The
examinations contain
clips in DICOM format.
The LVivoSeamless
scans the entire
examination and
selects automatically
4CH,2CH and 3CH
views for EF and GLS
evaluation and runs the
LVivoEF and the
LVivoStrain. The

The LVivoSG is a
decision support
system (software) for
automated segmental
wall motion evaluation
of the left ventricle (LV).
The LVivoSG is based
on a proprietary
algorithm for LV
regional motion
parameters calculation
and wall motion
classification.
The LVivoSG uses the
LVivo platform to detect
the LV inner borders
(endocard) from which
the wall motion
parameters are
calculated. As part of
the LVivoSG
development, the LVivo

The subject EPIQ Series
Diagnostic Ultrasound
System integrates the LVivo
Seamless algorithm of the
K212466 reference device
as “Smart View Select” and
the LVivo SWM module of
the LVivo SG K161382
reference device as
“Segmental Wall Motion”.
Minor changes have been
made to the reference
LVivo Seamless and LVivo
SG software applications in
the subject SVS and SWM
applications, as described
below.


EPIQ Series Diagnostic
LVivo Seamless
LVivo Software

Feature
Features: SVS and
K212466
Feature: LVivo SG
Comparison

Performance
Assessment of
Quantitative Imaging in
Radiological Device
Premarket
Submissions, the SWM
software is a semi-
automated quantitative
imaging algorithm, as
users are generally
expected to review and
concur with the
initialization and
generated results. The
users can also edit
algorithm generated
segmental wall motion
scores for individual
segments based on
their clinical expertise.

results are sent back
using DICOM
communication to the
PACS server and
become part of the
study. The clinician that
reviews the
examinations at the
PACS workstation will
be able view the output
of the LVivoEF and
LVivoStrain as part of
the study review.

platform supports also
edge detection and
tracking in additional
plane, the apical long
(3CH). The LVivoSG
provides fully
automated
measurements of the
regional left ventricular
systolic function from
three apical views the
four chamber (4CH),
two chamber (2CH)
and long (3CH).

Not applicable – does
not contain functionality
for optimal triplet
selection

The subject SVS software
application utilizes the
identical, unchanged deep
neural network for view
identification and optimal
triplet (A4C, A2C, and A3C)
selection for subsequent LV
analysis

Yes – the subject SVS
software application
uses an additional
heuristic logic step, after
the deep neural network
identifies the views and
determines a list of 3 clip
combinations (triplets),
to preferentially select
triplets of shallower scan
depths, provided the
depth exceeds 10cm
and the variation of
depths of each clips is
1cm or less

Not applicable – scan
depth is not considered
in the selection of the
optimal triplets for the
user

The subject SVS software
application adds an
additional heuristic logic
step for preferentially
selecting certain scan
depths from the reference
LVivo Seamless. The
Neural Network of the
reference device is
unchanged, as described
above. Performance testing
has demonstrated very
strong correlation between
the LV analysis outputs
(EF, GLS) from clips
selected by the subject SVS
software application and
clips manually selected by
clinical users. Therefore,
the additional heuristic logic
step of the subject SVS
software does not raise new
or different questions of
safety or effectiveness in
comparison to the reference
device K212466.

Primary – from K161382
Secondary – from
cleared AutoStrain LV
(K190913)

Not applicable – does
not contain functionality
for segmental wall
motion

Not applicable – this
application does not
contain functionality for
segmental wall motion

Subject device
preferentially utilizes
identical border initialization
algorithm as reference
device K161382.
If the reference device’s
algorithm is unsuccessful in
the subject SWM, it will use


the border initialization of
the cleared AutoStrain LV
(K190913) as a back-up.
Performance testing has
demonstrated very strong
correlation between the
SWM scoring of the subject
software application
compared to the scoring of
the reference device on the
same clips. Therefore, there
are no new or increased
risks for the use of the
AutoStrain LV border
initialization in the subject
SWM software application
as a back-up.

Users manually edit
scores using a drop-
down selection

Users must manually
edit the borders to
attempt to edit scores

In the cleared LVivo SG,
users must manually adjust
the chamber borders to
influence the SWM scores.
In the subject SWM on
VM11, users may manually
select different SWM scores
for each segment using a
drop-down menu. Any
scores which are manually
changed from those
calculated by the software
application will be indicated
by an asterisk (*). This
change in editing does not
introduce new risks
because it is providing a
simpler way for the user to
edit their segmental wall
motion score(s) – instead of
having to edit the border
contours to influence the
SWM score as on the
cleared LVivo SG, user can
directly edit each segment’s
score with the drop-down.


Affiniti Series Diagnostic
Affiniti Series Diagnostic
LVivo Software Application

Ultrasound System
Ultrasound System
Feature: LVivo SG

Proposed Device
Predicate Device
Reference Device

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

LVivo platform is intended for
non-invasive processing of
ultrasound images to detect,
measure, and calculate relevant
medical parameters of
structures and function of
patients with suspected
disease.

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

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

Class II
Class II
Class II
Identical to predicate

System, Imaging, Pulsed
Doppler, Ultrasonic

Medical image management
and processing system.

Diagnostic ultrasonic transducer
Ultrasonic pulsed echo imaging
system
Diagnostic intravascular
catheter

Table 2: Comparison to Predicate - Affiniti


Automated Radiological Image
Processing Software

Reusable-
Yes
Yes
No, software-only
Identical to predicate

Limited (≤ 24 hours)
Limited (≤ 24 hours)
N/A, software-only
Identical to predicate

Track 3
Track 3
N/A, software-only
Identical to predicate

The SWM software
automatically evaluates the
segmental (regional) function of
the left ventricle (LV) from adult
TTE echo examinations.
Note: Per FDA Guidance
Technical Performance
Assessment of Quantitative
Imaging in Radiological Device
Premarket Submissions, the
SWM software is a semi-
automated quantitative imaging
algorithm, as users are
generally expected to review
and concur with the initialization
and generated results. The
users can also edit algorithm
generated segmental wall
motion scores for individual
segments based on their clinical
expertise.

The predicate Affiniti Series
Diagnostic Ultrasound System
does not currently have a
dedicated software application
containing the functionality
introduced in the subject
submission for (semi-)
automated segmental wall
motion evaluation of the left
ventricle (LV).

The LVivoSG is a decision
support system (software) for
automated segmental wall
motion evaluation of the left
ventricle (LV). The LVivoSG is
based on a proprietary
algorithm for LV regional motion
parameters calculation and wall
motion classification.
The LVivoSG uses the LVivo
platform to detect the LV inner
borders (endocard) from which
the wall motion parameters are
calculated. As part of the
LVivoSG development, the
LVivo platform supports also
edge detection and tracking in
additional plane, the apical long
(3CH). The LVivoSG provides
fully automated measurements
of the regional left ventricular
systolic function from three
apical views the four chamber
(4CH), two chamber (2CH) and
long (3CH).

The subject Affiniti Series
Diagnostic Ultrasound
System integrates the LVivo
SWM module of the LVivo
SG K161382 reference
device as “Segmental Wall
Motion”. The subject SWM
software application utilizes
the border initialization of
the cleared AutoStrain LV
software in the event that
the border initialization
algorithm from LVivo SG is
unsuccessful. Otherwise,
there is no difference
between the subject SWM
software application and the
reference LVivo SG’s SWM
module and no changes
have been made to its
algorithms.

Primary – from K161382
Secondary – from cleared
AutoStrain LV (K190913)

Not applicable – does not
contain functionality for
segmental wall motion

Subject device
preferentially utilizes
identical border initialization
algorithm as reference
device K161382.
If the reference device’s
algorithm is unsuccessful in
the subject SWM, it will use
the border initialization of
the cleared AutoStrain LV
(K190913) as a back-up.
Performance testing has
demonstrated very strong
correlation between the
SWM scoring of the subject
software application
compared to the scoring of
the reference device on the
same clips. Therefore, there
are no new or increased
risks for the use of the
AutoStrain LV border
initialization in the subject


Affiniti Series Diagnostic
Affiniti Series Diagnostic
LVivo Software Application

Ultrasound System
Ultrasound System
Feature: LVivo SG

Proposed Device
Predicate Device
Reference Device

Users manually edit scores
using a drop-down selection

Users must manually edit the
borders to attempt to edit scores

In the cleared LVivo SG,
users must manually adjust
the chamber borders to
influence the SWM scores.
In the subject SWM on
VM11, users may manually
select different SWM scores
for each segment using a
drop-down menu. Any
scores which are manually
changed from those
calculated by the software
application will be indicated
by an asterisk (*). This
change in editing does not
introduce new risks
because it is providing a
simpler way for the user to
edit their segmental wall
motion score(s) – instead of
having to edit the border
contours to influence the
SWM score as on the
cleared LVivo SG, user can
directly edit each segment’s
score with the drop-down.


Medical Image Management And
Processing System

PeekMed web is a system
designed to help healthcare
professionals carry out
pre-operative planning for several
surgical procedures, based on
their imported patients’ imaging
studies. Experience in usage and
a clinical assessment is necessary
for the proper use of the system in
the revision and approval of the
output of the planning. The
multi-platform system works with a
database of digital representations
related to surgical materials
supplied by their manufacturers.

Indications for
This medical device consists of a
This medical device consists of a
Yes
---

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


decision support tool for qualified
healthcare professionals to quickly
and efficiently perform the
pre-operative planning for several
surgical procedures, using medical
imaging with the additional
capability of planning the 2D or 3D
environment. The system is
designed for the medical
specialties within surgery and no
specific use environment is
mandatory, whereas the typical
use environment is a room with a
computer. The patient target group
is adult patients who have an
injury or disability diagnosed
previously. There are no other
considerations for the intended
patient population.

No contraindications specific to
this device.

PeekMed web allows the surgeon
to perform orthopedic pre-surgical
planning efficiently in the


musculoskeletal system (e.g., Hip
procedures, Knee procedures)

PeekMed web allows the surgeon
to perform the pre-surgical
planning efficiently in the following
anatomical regions:
- Hip
- Knee
- Upper limb
- Foot

Yes
The subject
device also
allows the
planning of
orthopedic
surgery in the
foot region.

The subject device allows for the surgery
planning on one more anatomical region
(foot), this does not constitute a new
intended purpose nor does it raise
questions of safety and performance,
since the development, verification,
validation, and release processes are
exactly the same between the devices.
The new variants of the ML model for foot
are considered in this Traditional 510(k)
and were developed according to what is
defined for each ML model development
and validation according to their intended
requirements and performance.
This change improves the medical device
in a way that the user can benefit from the
existing features on one more anatomical
region. This change does not interfere
with or create an effect on other
anatomical regions covered by the device.

Patient Population
Adults
Adults
Yes
---


End users
Healthcare Professionals
Healthcare Professionals
Yes
---

Software is cloud-based (not
installable) and can be displayed
on any personal device or
workstation that can run on a web
browser

Distributed system (cloud-based).
This distributed system is a
combination of software modules
placed on servers that are able to
communicate with each other.

The workflow is as follows: Import
case images, configure images,
identify the case, pre-surgical
planning, and export the case. The
workflow chart is in Annex 2.

Receives medical images from
various sources

The software processes data to
provide an overlap and


dimensioning of digital
representations of the prosthetic
material

Allows the overlap of models and
the intersection of the models

Support for digital
prosthetic
materials
provided by the
manufacturers


Human
intervention for
image
interpretation

Ability to add
additional
modules when
available

Yes
The updated
ML model
variants were
compared
against the
acceptance
criteria and,
therefore
confirmed to
be an
improvement

The subject device includes new and
updated ML model variants for the
purpose of bone segmentation. These ML
model variants were developed and
validated using the same methods and
acceptance criteria and deemed as
Compliant. The updated model variants
were also compared against the
acceptance criteria, therefore confirmed to
be an improvement over the last ML
model variants versions.


Configuration
Windows for
Oblique Planes
functionality

Yes
The updated
ML model
variants were
compared
against the
acceptance
criteria and,
therefore
confirmed to
be an
improvement
over the last
ML model
variants
versions.

The subject device includes new and
updated ML model variants for the
purpose of landmarking. These ML model
variants were developed and validated
using the same methods and acceptance
criteria and deemed as Compliant. The
updated model variants were also
compared against the acceptance criteria,
therefore confirmed to be an improvement
over the last ML model variants versions.


Predicate device: BriefCase for CSF
Triage Software (K190896)

CINA-CSpine is a radiological
computer aided triage and
notification software indicated for
use in the analysis of cervical
spine CT images.

BriefCase is a radiological computer aided
triage and notification software indicated
for use in the analysis of cervical spine CT
images.
The device is intended to assist hospital
networks and trained radiologists in
workflow triage by flagging and

Comparison of key features between CINA-CSpine and predicate device (Aidoc Medical Ltd)


The device is intended to assist
hospital networks and
appropriately trained physician
specialists by flagging and
communication of suspected
positive findings compatible with
acute cervical spine fractures
including non-displaced fracture
lines and/or displaced fracture
fragments.
CINA-CSpine uses an artificial
intelligence algorithm to analyze
images and highlight cases with
detected findings on a standalone
application in parallel to the
ongoing standard of care image
interpretation. The device is not
designed to detect vertebral
compression fractures.
The user is presented with
notifications for cases with
suspected findings. Notifications
include compressed preview
images that are meant for
informational purposes only, and
are not intended for diagnostic
use beyond notification.
The device does not alter the
original medical image, and it is
not intended to be used as a
diagnostic device.
The results of CINA-CSpine are
intended to be used in
conjunction with other patient
information and based on
professional judgment to assist
with triage/prioritization of
medical images.
Notified clinicians are ultimately
responsible for reviewing full
images per the standard of care.

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


DICOM information about the
patient, study and current image

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

Artificial intelligence algorithm
with database of images

Presentation of a compressed,
grayscale, unannotated image
that is marked “not for diagnostic
use”.

Presentation of a small, compressed,
black
and white preview image that is labeled
“Not for diagnostic use”.

No. The device operates in
parallel with the standard of care,
which remains the default option
for all cases.

-CSpine image processing
application
- Compatibility of use with the
CINA Platform (worklist and
Image Viewer) or other medical
image communications device.

- AHS module (orchestrator, image
acquisition)
- ACS module (image processing).
- Aidoc Worklist application for workflow
integration (worklist and non-diagnostic
basic Image Viewer).


Primary Predicate
K183593
Body Vision LungVision

The LungVision System is intended to enable
users to segment previously acquired 3D CT
datasets and overlay and register these 3D
segmented data sets with fluoroscopic live X-
ray images of the same anatomy in order to
support catheter/device navigation during
pulmonary procedures.

Automated radiological image processing
software
QIH, CFR 892.2050
Picture archiving and communications system
LLZ, CFR 892.2050

Anatomy access
Bronchial airways
Bronchial airways

Table 1 compares the subject device to the predicate


Subject Swoop Portable MR Imaging
Predicate Swoop Portable MR Imaging

The Swoop Portable MR Imaging System
is a portable, ultra-low field magne-c
resonance imaging device for producing
images that display the internal
structure of the head where full
diagnos-c examina-on is not clinically
prac-cal. When interpreted by a trained
physician, these images provide
informa-on that can be useful in
determining a diagnosis.

Pa-ent Popula-on:
Adult and pediatric pa-ents (≥ 0 years)
Same

At the point of care in professional
health care facili-es such as emergency
rooms, intensive/cri-cal care units,
hospitals, outpa-ent, or rehabilita-on
centers.

Energy Used and/or delivered:
Magne-c Resonance
Same

Physical Dimensions
835 mm x 630 mm x 652 mm
Same

Field Strength
63.3 mT permanent magnet
Same

Strength
X: 24 mT/m, Y: 23 mT/m, Z: 39 mT/m
Same

Rise Time
X: 2.1 ms, Y: 2.0 ms, Z: 3.8 ms
Same

Slew Rate
X: 24 T/m/s, Y: 22 T/m/s, Z: 21 T/m/s
Same

Computer Display
Hyperfine-supplied tablet
Same

The table below compares the subject device to the predicate.


Inner Dimensions (mm)
205 mm x 240 mm
Same

Pa-ent Weight Capacity
1.6kg-200 kg
Same

Noise correc-on and line noise
suppression for all sequences

T1W
• T1-Standard
• T1-Gray/White Contrast

Fast Itera-ve Shrinkage Thresholding
Algorithm (FISTA)

• Advanced Denoising
• Image orienta-on transform
• Geometric distor-on correc-on
• Receive coil intensity correc-on
• DICOM output


Test
Test Description
Applicable Standard(s)

Software verification testing in accordance with
the design requirements to ensure that the
software requirements were met.

• IEC 62304:2015
• FDA Guidance, “Content of Premarket
Submissions for Device Software
Functions”

requirements and applicable standards to support substantial equivalence.


Testing to verify the subject device meets all
image quality criteria.

• NEMA MS 1-2008 (R2020)
• NEMA MS 3-2008 (R2020)
• NEMA MS 9-2008 (R2020)
• NEMA MS 12-2016
• American College of Radiology (ACR)
Phantom Test Guidance for Use of the
Large MRI Phantom for the ACR MRI
Accreditation Program
• American College of Radiology
standards for named sequences

Testing to verify cybersecurity controls and
management.

FDA Guidance, “Cybersecurity in
Medical Devices: Quality System
Considera-ons and Content of
Premarket Submissions”

Validation to ensure the subject device meets user
needs and performs as intended.

FDA Guidance, “Content of Premarket
Submissions for Device Software
Functions”


Biocompatibility testing of patient-contacting
materials.

Cleaning and disinfection validation of patient-
contacting materials.

• FDA Guidance, “Reprocessing Medical
Devices in Health Care Settings:
Validation Methods and Labeling”
• ISO 17664:2017
• ASTM F3208-17

Electrical Safety, EMC, and Essential Performance
testing.

Characterization of the Specific Absorption Rate
for Magnetic Resonance Imaging Systems.

modifications did not introduce a new worst-case configuration or scenario for testing.


ReferenceDevice
1:
CaptionGuidance
(DEN190040)

Ultrasoundimage
processingsoftware
implementing
artificialintelligence,
including
non-adaptive
machinelearning
algorithmstrained
withclinicaldata
intendedfor
non-invasiveanalysis
ofultrasounddata


DeepConvolutional
NeuralNetworksfor
Segmentation,
LandmarkDetection
andClassification

LVEF
IVCMinimum
diameteron
inspirationand
Maximumdiameter
onexpiration
Myocardiumwall
thickness
(Interventricular
Septumand
Posteriorwall)from
Plaxview

LVEF,GLS
RVsizeand
function:Fractional
AreaChange
(FAC),FreeWall
Strain(FWS),
Tricuspidannular
planesystolic
excursion(TAPSE)

Retrospectively
recordingof
Diagnostic
qualityclip


AImodulesare
anaccessoryto
compatible
general
purpose
diagnostic
ultrasound
systems


Modification Rationale TestingMethods ImpactAssessment

(A)Modification
toarchitecture,
pre/post
processing

TheAImodelsin
AIPlatform2.0
maybemodified
inafocusedand
boundmanner,to
improveaccuracy,
efficiency,and
adaptabilitywhile
maintainingthe
safetyandefficacy
ofthedevice.

Substantialequivalence
inperformancematrices
ascomparedtotheAI
Platform2.0.
Non-inferioritymarginsin
alignmentwithFDA
recommendationswillbe
usedtoensurethenew
deviceremainssafeand
effectivewithoutany
suchchangecompared
withtheoriginaldevice
andthelastmodified
versionofthedevice.

Improvedaccuracyand/orefficiencymetricsfor
AImodelperformance.
Benefit-RiskAnalysis:
Benefit:Enhancedperformance;Improved
efficiencyandscalability.
Risk:Reductioninclinicalperformance
RiskMitigation:
Therisksofoverfittingandgeneralizabilityare
mitigatedbyconductingcross-validation,data
augmentation,andhyperparametertuningin
internaltrainingprocedures.
Thefinalvalidationisperformedusing
non-inferiorityanalysisaccordingtothe
establishedclinicalprotocoltoensurebaseline
performanceismaintainedandimprovements
arerealizedbeforemodificationsarecommitted
andreleased.

Thetraining
datasetoftheAI
modelsmaybe
augmentedwitha
broaderandmore
diverserangeof
imagingdatato
enhancemodel
robustness,
reducebias,
improve
generalizability,
andrespond
effectivelyto
real-world
feedback.

ImprovedmodelgeneralizabilityforAImodel
outputsdisplayedtousers.
Benefit-RiskAnalysis:
Benefit:Improvedmodelrobustness,reduced
bias,enhancedgeneralizability,andresponding
torealworldfeedback.
Risk:Reductioninclinicalperformance
RiskMitigation:
Therisksofoverfittingandgeneralizabilityare
mitigatedbyconductingcross-validation,data
augmentation,andhyperparametertuningin
internaltrainingprocedures.
Thefinalvalidationisperformedusing
non-inferiorityanalysisaccordingtothe
establishedclinicalprotocoltoensurebaseline


Classification Description
21 CFR §
Product Code

System, imaging, pulsed doppler,
ultrasonic

System, imaging, pulsed echo,
ultrasonic

Transducer, ultrasonic, diagnostic
892.1570
ITX

Automated Radiological Image
Processing Software

Diagnostic Intravascular Catheter
870.1200
OBJ


EPIQ Series Diagnostic
EPIQ Series Diagnostic

Feature: 4D CARDIO-
Quantification Software

Feature
VIEW
Feature: 3D Auto MV
Comparison

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

Quantification and
reporting of
cardiovascular, fetal,
and abdominal
structures and functions
of patients with
suspected disease to
support the physician in
the diagnosis.

QLAB Quantification software
is a software application
package. It is designed to view
and quantify image data
acquired on Philips ultrasound
systems

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

Licensed medical
practitioners or assistant
medical technicians

Table 1: Comparison to Predicate for introduction of 3D Auto TV onto EPIQ


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

Inside and outside of
Hospitals, Clinics, and
Physician’s offices

Class II
Class II
Class II
Class II
Identical to predicate

The 3D Auto TV
software solution is
intended for use with the
Philips EPIQ Diagnostic
Ultrasound Systems.
The software enables
semi-automated
quantification of the
tricuspid valve. At a high
level, this is
accomplished through
automatically derived
measurements from a
segmented model of the
tricuspid valve annulus
formed by the software
through model-based
segmentation of the
acquired ultrasound
images.

The predicate EPIQ
Series Diagnostic
Ultrasound System does
not currently have a
dedicated software
application for
quantification of the
tricuspid valve annulus.
The subject of this
submission is to
introduce the 3D Auto
TV software onto the
predicate device.

4D CARDIO-VIEW is an
advanced analysis tool
for 3D/4D
echocardiography data.
Anatomical structure
visualization, volume
measurements (LV
and/or generic), and
specified or manual
measurements are
possible for cardiac
structures including, but
not limited to, the
tricuspid valve. Various
tools are available for
rendering that display 2-
and 3-dimensional
morphology and
function for defined
structures.

3D Auto MV is a semi-
automated software
application intended for the
analysis of Mitral Valve (MV)
anatomy and function. This
application generates models
of anatomical structures of
interest such as the MV
annulus, leaflets, and the
closure line, which allows for
quantification of pre- and post-
operative valvular function and
a comparison of morphology.

Similar to the reference
device features.
The functionality and
workflow of the 3D Auto
TV software is very
similar to the 3D Auto
MV tool, where
measurement
parameters are derived
from models of the
mitral valve (in the case
of 3D Auto MV) and
tricuspid valve (in the
case of 3D Auto TV).
Manual measurements
are also able to be
performed on both
software applications.
Comparing 3D Auto TV
to 4D CARDIO-VIEW,
both software have
functionality for
quantifying the tricuspid
valve. The proposed 3D
Auto TV allows for semi-
automated
quantification, where the
reference device is fully
manual. As we
demonstrate high
agreement in the
measurement outputs
on the same patients
when quantified using
the proposed 3D Auto
TV software and the
reference 4D CARDIO-
VIEW application, there
are no new questions
raised of safety or
effectiveness.

3D surface model is
created semi-
automatically using
machine learning
algorithm without user
interaction. User is able
to edit, accept, or reject
the initial landmark
proposals of the mitral

No standard tricuspid
valve (TV) quantification
parameters included as
part of the system.

3D surface model is
created based on user
defined anatomical
landmarks. User is able
to edit the contour of the
surface model before
proceeding with the
workflow.

3D surface model is created
semi-automatically using
machine learning algorithm
without user interaction. User
is able to edit, accept, or reject
the initial landmark proposals
of the mitral valve anatomical
locations.

Subject device uses
identical method for
contour generation as
the reference device
K200974. The only
difference is the
algorithm is trained on
tricuspid valve images,
where the reference


EPIQ Series Diagnostic
EPIQ Series Diagnostic

Feature: 4D CARDIO-
Quantification Software

Feature
VIEW
Feature: 3D Auto MV
Comparison

device was trained using
mitral valve images.

Semi-auto annulus
results
• TV Ann Perimeter
(3D)
• TV Ann Perimeter
(2D)
• TV Ann Max Diam
(2D)
• TV Ann Min Diam
(2D)
• TV Ann Perimeter
Derived Diam (2D)
• TV Ann Height (3D)
• TV Ann Area (2D)
Manual device results
• TV Ann AP Diam (2D)
• TV Ann SL Diam (2D)
• Subvalvular 5 Plane
SL Diam
• Subvalvular 5 Plane
AP Diam
• Supravalvular C-
Shaped Perimeter
• Supravalvular AV -
AoCenter Diam

TAVR results
• Ann-Ost left diam
• Ann-Ost right diam
• Annulus Area
• Annulus dmin
• Annulus dmax
• Ao Ring diam
• Ao SV diam
• Ao STJ diam
Volume results (not
related to TV
quantification)
• EDV
• EF
• ESV
• GenVol
• Mass
• SV

Standard MV Parameters
• AP Diameter (cm)
• AL-PM Diameter (cm)
• Sphericity Index (AP / AL-
PM)
• Intertrigonal Distance (cm)
• Commissural Diameter (cm)
• D-Shaped Annulus
Perimeter (cm)
• Annulus Height (cm)
• Non-planar Angle (degrees)
• Tenting Volume (cm3)
• Coaptation Depth (mm)
• Tenting Area (cm2)
• Angle AAo-AP (degrees)
• Maximum Prolapse Height
(mm)
• Maximum Open Coaptation
Gap (mm)
• Maximum Open Coaptation
Width (mm)
• Anterior Leaflet Area (cm2)
• Posterior Leaflet Area (cm2)
• Distal Anterior Leaflet Angle
(degrees)
• Posterior Leaflet Angle
(degrees)
• Anterior Leaflet Length (cm)
• Posterior Leaflet Length
(cm)
• C-Shaped Annulus (cm)
2D MV Parameters
• D-Shaped Annulus Area
(cm2)
• Annulus Area (cm2)
• Anterior Closure Line
Length (cm)
• Posterior Closure Line
Length (cm)
3D MV Parameters
• Saddle Shaped Annulus
Area (cm2)
• Saddle Shaped Annulus
Perimeter (cm)
• Total Open Coaptation Area
(cm2)
• Anterior Closure Line
Length (cm)
• Posterior Closure Line
Length (cm)

Similar. The proposed
3D Auto TV software
allows very similar semi-
automated
measurements as the
reference software
application 3D Auto MV,
only applied to the
tricuspid valve.
The proposed 3D Auto
TV software adds
additional TV annulus
and device
measurements from
those available in 4D
CARDIO-VIEW to
further define the
tricuspid valve anatomy.
Both the proposed 3D
Auto TV and the
reference 4D CARDIO-
VIEW software allow
manual, free-form
measurements of the
tricuspid valve.


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

QLAB Quantification software is a
software application package. It
is designed to view and quantify
image data acquired on Philips
ultrasound systems

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

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

3D Auto CFQ is a new semi-
automated quantification software
which will be introduced on the
EPIQ Ultrasound Systems from
software version VM11.0. The
application provides semi-
automated quantification of Mitral
Regurgitation (MR) volume and
peak flow rate by analyzing 3D
color flow images acquired during
transesophageal
echocardiography (TEE)
examinations.

The Proximal Isovelocity Surface
Area (PISA) methodology can be
used currently on the predicate
device to quantify valvular
regurgitation. The technique
utilizes 2D/Color and Doppler
images to allow the user to make
simple, manual measurements in
a cascading fashion to allow
calculation of peak flow rate and
volumetric regurgitation.

3D Auto MV is a semi-automated
software application intended for
the analysis of Mitral Valve (MV)
anatomy and function. This
application generates models of
anatomical structures of interest
such as the MV annulus, leaflets,
and the closure line, which allows
for quantification of pre- and post-
operative valvular function and a
comparison of morphology.

Similar. The predicate device
facilitates the quantification of
mitral regurgitation volume
and peak flow rate through a
group of measurements which
are performed in a cascading
fashion manually by the user
according to the Proximal
Isovelocity Surface Area
(PISA) methodology. The
proposed 3D Auto CFQ
software application allows
the users to quantify the same
measurements for mitral
regurgitation volume and peak
flow rate but in a semi-
automated workflow.

Table 2: Comparison to Predicate for introduction of 3D Auto CFQ onto EPIQ


The reference device does not
contain functionality for
quantification of mitral
regurgitation.

3D surface model is created
semi-automatically using
machine learning algorithm
without user interaction. User is
able to edit, accept, or reject the
initial landmark proposals of the
mitral valve anatomical locations.

No standard contour generation
technology for the mitral valve,
outside of 3D Auto MV, included
as part of the system.

Subject device uses identical
method for contour generation
as the reference device
K200974

The 3D Auto CFQ algorithm
quantifies mitral regurgitation
volume and flow rate from
acquired 3D color flow images.
The greyscale information from
these images is used to generate
a 3D model of the mitral valve,
which is used as an input along
with the 3D color data into the 3D
Auto CFQ flow algorithm. The 3D
Auto CFQ algorithm uses a fluid
dynamic model of an
incompressible fluid (blood)
traveling through an irregular-
shaped (i.e., nonround) orifice. In
its initial step, the algorithm
generates a hypothetical model
of true blood flow velocities in the
proximal convergence zone
based on all measured Doppler
velocities and the underlying fluid
dynamics model. The true
velocity model is then converted
into the corresponding apparent
Doppler velocity model
(“synthetic apparent velocities”)
using ultrasound physics
(projection along the axial
dimension). These synthetic
velocities are subsequently
compared to the acquired
velocities in the Color Flow (CF)
data set. Based on the outcome
of this comparison, the model is
updated and reiterated to get
the best fit between the acquired
velocities and the generated
model. 3D Auto CFQ determines
the resulting regurgitant flow rate
for this frame. This process is
repeated for each frame included
in the analysis, which in most
cases includes the entire systolic
cycle. In each frame, the size and
shape of the
regurgitant orifice is not assumed
but is generated by this iterative
loop between the model and the
CF data.

The PISA methodology uses
sequential acquisitions and
manual measurements, which
are manually performed by the
user:
• MR Alias Velocity (from the
2D/Color)
• MR Radius (from the 2D/Color)
• MR Vmax (from the continuous
wave doppler)
• MR VTI (from the continuous
wave doppler)
The outputs of these
measurements go into the
equations for the derived
measurements including:
• Mitral Regurgitant (MR) Flow
Rate
• MR Effective Regurgitant
Orifice (ERO)
• MR Volume

N/A – does not contain
technology for mitral regurgitation
quantification

Similar. The predicate device
utilizes the PISA methodology
for quantifying MR volume
and flow rate. This
methodology utilizes
sequential measurements
performed by the user and is
based on assumptions
including there being a single,
round, constant flow orifice
during the entire systole.
3D Auto CFQ operates using
3D color to address the spatial
complexities seen in mitral
regurgitation and was
developed to evaluate the
regurgitant flow at every frame
in systole, where the PISA
methodology only assesses
one frame during systole and
assumes this frame applies
across systole.
The proposed 3D Auto CFQ
software application allows
the users to quantify the same
measurements for mitral
regurgitation volume and peak
flow rate as PISA. The
dynamic flow algorithm is the
new technology introduced in
this submission. Everything
leading up to the calculations
by the dynamic flow model are
existing and cleared, including
the transducer and imaging
modes (X8-2t transducer; 3D
Zoom, Full Volume 3D, or Live
3D; K163120), and software
for mitral valve model
generation (reference device
K200974). The dynamic flow
model of the 3D Auto CFQ
software application uses
these as inputs to arrive at the
outputs of mitral regurgitation
volume and peak flow rate.
These outputs are the same
as in the predicate, only the
method to arrive at the


EPIQ Series Diagnostic
EPIQ Series Diagnostic

measurements differs in the
subject device.

3D9-3V, C5-1, C8-5, C9-2, C10-
3V, C10-4ec, D2cwc, D2tcd,
D5cwc, eL18-4, eL18-4 EMT,
L12-3, L12-3 ERGO, L12-5 50,
L15-7io, L18-5, mC7-2, mC12-3,
mL26-8, S5-1, S7-3t, S8-3, S8-3t,
S9-2, S12-4, V6-2, V9-2, VL12-5,
X5-1, X5-1c, X6-1, X7-2, X7-2t,
X8-2t, X11-4t, XL14-3
The 3D Auto CFQ software
application can analyze
transesophageal
echocardiography (TEE) images
acquired by the cleared X8-2t
transducer (K163120), which is
already commercially available
and compatible with the EPIQ
Ultrasound System.

The 3D Auto MV software
application is compatible with the
following transducers: X7-2t, X8-
2t, X11-4t

Identical. There are no new
transducers introduced in this
510(k).
The 3D Auto CFQ software
application can analyze
transesophageal
echocardiography (TEE)
images acquired by the
cleared X8-2t transducer
(K163120), which is already
commercially available and
compatible with the EPIQ
Ultrasound System.
There are no impacts to the
indications for use or intended
use of the X8-2t transducer
when used for the purposes of
acquiring images for analysis
by the 3D Auto CFQ software
application. The 3D Auto CFQ
software application requires
images to be acquired in
either 3D Zoom, Full Volume
3D, or Live 3D imaging
modes. There are no changes
to these existing transducer
imaging modes.

Semi-automated measurements
performed by the 3D Auto CFQ
software application:
Mitral regurgitation (MR) volume
[mL];
Peak flow rate [mL/s]

Derived measurements which the
user can obtain through the PISA
methodology include:
Mitral regurgitation (MR) volume
[mL];
Peak flow rate [mL/s]

Standard MV Parameters
• AP Diameter (cm)
• AL-PM Diameter (cm)
• Sphericity Index (AP / AL-PM)
• Intertrigonal Distance (cm)
• Commissural Diameter (cm)
• D-Shaped Annulus Perimeter
(cm)
• Annulus Height (cm)
• Non-planar Angle (degrees)
• Tenting Volume (cm3)
• Coaptation Depth (mm)
• Tenting Area (cm2)
• Angle AAo-AP (degrees)
• Maximum Prolapse Height
(mm)
• Maximum Open Coaptation
Gap (mm)
• Maximum Open Coaptation
Width (mm)
• Anterior Leaflet Area (cm2)
• Posterior Leaflet Area (cm2)
• Distal Anterior Leaflet Angle
(degrees)
• Posterior Leaflet Angle
(degrees)
• Anterior Leaflet Length (cm)
• Posterior Leaflet Length (cm)
• C-Shaped Annulus (cm)
2D MV Parameters

Similar. The measurements
performed by the proposed
3D Auto CFQ software
application can also be
obtained by a user on the
predicate device.
Substantiation of the
performance of the 3D Auto
CFQ software’s regurgitant
volume output was performed
by comparison to cardiac
magnetic resonance imaging
(CMR) images with
acceptance criteria of
agreement within the limits of
agreement. While the PISA
methodology is a widely
accepted method for mitral
regurgitation quantification
and is a recommended
method by the American
Society of Echocardiography,
the outputs from 3D Auto CFQ
were compared to those from
CMR (as opposed to PISA) as
the former is considered a
gold standard for mitral
regurgitation quantification.
Acceptance criteria for 3D
Auto CFQ was based on


• D-Shaped Annulus Area (cm2)
• Annulus Area (cm2)
• Anterior Closure Line Length
(cm)
• Posterior Closure Line Length
(cm)
3D MV Parameters
• Saddle Shaped Annulus Area
(cm2)
• Saddle Shaped Annulus
Perimeter (cm)
• Total Open Coaptation Area
(cm2)
• Anterior Closure Line Length
(cm)
• Posterior Closure Line Length
(cm)

agreement with CMR being
within predefined maximum
limits of agreement.
In addition to regurgitant
volume, the peak flow rate
output of 3D Auto CFQ was
validated in comparison to
manual PISA method, where
the correlation was very high.
The reference device
K200974 facilitates
anatomical measurements of
the mitral valve from the
generated model of the mitral
valve but does not perform
measurements for quantifying
mitral regurgitation.


Clarius Ultrasound Scanner, model C3 HD3
(K213436)

Motive Software
OptiTrack Cameras
SpineUs™ Active LED Markers
Power over Ethernet (PoE) switch

The SpineUs™ system comprises the following:


Diagnostic ultrasound imaging of the spinal
region of the human body

Diagnostic ultrasound imaging and fluid flow
analysis of the human body

The SpineUs™ system is a software-based,
tracked, ultrasound imaging system and
accessories, intended for diagnostic imaging. It
is indicated for diagnostic ultrasound imaging in
the following applications: musculoskeletal
(conventional, superficial). The system is
intended for use by trained chiropractors and
radiologists in a hospital or medical clinic.
The SpineUs™ system is intended for assisting
trained chiropractors and radiologists in
acquiring, viewing, and measuring ultrasound
images of the spine in both clinic and hospital
settings. The SpineUs™ system is intended to
be used as an adjunct to conventional imaging
method that allows trained chiropractors and
radiologists to measure spine-related anatomical
components on images (e.g., intervertebral
angles and spine curvature). The system also
allows the review and management of patient
measurement data. Clinical judgment of
anatomy and experience are required to properly
use the SpineUs™ system.
Patient management decisions should not be
made based solely on the results of the
SpineUs™ computer application. The user shall
retain the ultimate responsibility of ascertaining
the measurements based on standard practices
and clinical judgement.

LOGIQ E10 is intended for use by a qualified
physician in a hospital or medical clinic, for
ultrasound evaluation of:
• Fetal / Obstetrics
• Abdominal (including Renal,
Gynecology/Pelvic)
• Pediatric
• Small Organ (Breast, Testes,
Thyroid)
• Neonatal Cephalic
• Adult Cephalic
• Cardiac (Adult and Pediatric)
• Peripheral Vascular
• Musculoskeletal Conventional and
Superficial
• Urology (including Prostate)
• Transrectal
• Transvaginal
• Transesophageal and Intraoperative
(Abdominal and Vascular)

3D Optical tracking using GPS-like technology

2D/3D GPS tracking using GPS-like
technology.

AI-based segmentation of vertebral bone tissue
in ultrasound image frames.

AI-based tools such as Breast Assistant and
Auto Lesion Segmentation powered by Koios
DS™, Auto Doppler Assistant, and OB
Measurement Assistant.

Consumer PC, provided by Verdure Imaging,
Inc.

Mobile console approximately 585 mm wide
(keyboard), 991 mm deep and 1300 mm high
that provides digital acquisition, processing, and
display capability. The user interface includes a
computer keyboard, specialized controls, 12-
inch-high resolution color touch screen and
23.8-inch High Contrast LED LCD monitor

B, M, PW Doppler, CW Doppler, Color
Doppler, Color M Doppler, Power Doppler,
Harmonic Imaging, Coded Pulse, 3D/4D
Imaging mode, Elastography, Shear Wave
Elastography, Attenuation Imaging and
Combined modes: B/M, B/Color, B/PWD,
B/Color/PWD, B/Power/PWD


age range, body mass index, and ethnicity.


Non-Clinical and Clinical Performance Testing Dataset (n = 31)

separate annotator. These annotations were used as the ground truth for the pixel-based metrics.


Name
Description
Success Criteria
Results

Average
Percentage of
Transverse
Processes
Identified*

The number of transverse processes
correctly identified relative to the
total real number of transverse
processes present in the 3D
reconstruction of the spine

The average time required to process
a single 2D ultrasound image frame
with the evaluated Segmentation AI

Segmentation AI Summary of Non-Clinical Endpoints, Success Criteria, and Results


Various pixel-based metrics which are
used to identify relative performance
of individual image segmentations
(Sensitivity, Specificity, Precision,
Dice Coefficient, Balanced Accuracy,
95th Percentile Hausdorff Distance)

Sensitivity: 41.80%.
Specificity: 99.19%.
Precision: 38.70%.
Dice Coefficient:
0.4019.
Balanced accuracy:
70.49%.
95th %ile HD: 12.91
mm.


Patient Subgroup
Number of Patients
Percentage of transverse

African American or Black
11
100.0 [100.0 – 100.0] %


Patient Subgroup
Number of Patients
Average error (mm, 95% CI)

African American or Black
11
3.15 (2.11 - 4.20)

shown in the table below for each patient subgroup.


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
Compatibility – Requirements and tests


Medical electrical equipment – Part 2-37: Particular requirements for the
basic safety and essential performance of ultrasonic medical diagnostic and
monitoring equipment

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

Medical Devices – Application of Risk Management to Medical Devices

Digital Imaging and Communications in Medicine (DICOM) Set


LV/RV cavity volume at the phase defined as the ED.

• On Screen Data Panel
• CT Function Report

LV/RV blood volume at the phase defined as the ES.

The volume of blood pumped out of the LV/RV during
each cardiac contraction; represented by the
differences of EDV and ESV.

Percentage of the total amount of blood in the LV/RV
that is pumped out in each cardiac cycle; calculated
by dividing the SV by EDV.

The amount of blood pumped by the heart in a
minute; calculated by multiplying the SV and heart
rate.

An indexed hemodynamic parameter that relates the
CO in one minute to body-surface area (calculated
from gender, height, and weight); obtained by dividing
the CO by BSA.

LV myocardial mass at the phase defined as the ED;
calculated by multiplying the myocardial volume in ED
phase with myocardial density (1.05 g/mL).

LV myocardial mass at the phase defined as the ES;
calculated by multiplying the myocardial volume in ES
phase with myocardial density (1.05 g/mL).

End Diastolic and End Systolic Mass
(EDESM)* [g]

Mean LV myocardial mass; calculated as the average
of the LV EDM and LV ESM.

Note that measurement may also be indexed to patient BSA or height.


CT Function Module (K241038)
Vitrea CT Multi-Chamber CFA (K141302)

Manufactured by Circle
Manufactured by Vital Images

The Cardiac CT Function Software Application is intended for qualitative and
quantitative evaluation of cardiovascular CT images in a DICOM Standard
format, to calculate and display cardiac function metrics (e.g., end diastolic
volume, end systolic volume, stroke volume, ejection fraction, cardiac output,
cardiac index, and LV myocardial mass).

The Vitrea CT Multi-Chamber CFA option is intended to be used with CT studies
of the heart to assist cardiologists and radiologists in assessing function when
producing a cardiac evaluation. The CT Multi-Chamber CFA option includes
semi-automatic heart segmentation including three chambers (left ventricle, right
ventricle, and left atrium) segmentation, including identification of long axis and
mitral valve boundaries across multiple phases; calculation of global metrics,
including end diastolic volume, end systolic volume, stroke volume, ejection
fraction, cardiac output, cardiac index, stroke index, and myocardial mass; and
calculation of regional metrics; including wall motion, percentage of wall
thickening, regional ejection fraction, and polar plots.

The Cardiac CT Function Software Application is indicated to be used with
multi-phase, multi-slice cardiovascular CT angiography images to assist
qualified medical professionals in assessing and evaluating cardiac function.
CT Function includes manual and semi-automatic heart segmentation of 2
chambers (LV and RV) and calculation of cardiac function metrics including
end diastolic volume, end systolic volume, stroke volume, ejection fraction,
cardiac output, cardiac index, and LV myocardial mass.

Table 2. Intended use and indications comparison.


Medical image management and processing system

Picture Archiving and Communications System

Regulation Number
21 CFR 892.2050
21 CFR 892.2050

Multi-phase, multi-slice cardiac CT angiography images
(vendor independent)

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

Semi-automatic determination of epicardial and endocardial
contours, via segmentation of LV chamber and myocardium, RV
chamber, and LA chamber.

• Provides manual contour editing tools if adjustments are needed
• Default views automatically aligned to contours to aid typical image
review process
• Report

• Provides image editing tools if adjustments are needed
• Visualization presets and automated steps for typical image
review procedures
• Report

• 2D image viewing with real-time Window/Level, Zoom, and Pan
• 3D and Multi-Planar Reformatting (MPR) view
• Automatic industry standard oblique views of the heart (SAX, 2
chamber, and 4 chamber)
• Volume Rendering
• Phase Navigator
• Review Heart in Motion

• 2D image viewing with real-time Window/Level, Zoom, and Pan
• 3D and Multi-Planar Reformatting (MPR) view
• Maximum Intensity Projection
• Automatic industry standard Oblique views of the heart
• Volume Rendering
• Phase Navigator
• Review Heart in Motion

Identification of cardiac phases
and landmarks in loaded DICOM
datasets

• Automatic determination of ED and ES phases by volume
• Manually select/correct ED
• Manually select/correct ES
• Automatically identifies/orients to long axis view across multiple phases
• Automatically identifies/orients basal cut plane close to the mitral valve

• Automatic determination of ED and ES phases by volume
• Provides ability to select Diastolic phase
• Provides ability to select Systolic phase
• Automatically identifies the long axis across multiple phases
• Automatically identifies the plane of the Mitral valve

Automated calculation and
display of cardiac parameters

• End Diastolic Volume (EDV)
• End Systolic Volume (ESV)
• Stroke Volume (SV)
• Ejection Fraction (EF)
• Cardiac Output (CO)
• Myocardial Mass (EDM, ESM, EDESM) [in the LV Only]

• End Diastolic Volume (EDV)
• End Systolic Volume (ESV)
• Stroke Volume (SV)
• Stroke Index (SI)
• Ejection Fraction (EF)
• Cardiac Output (CO)

Table 3. Regulatory and technological features comparison.


CT Function Module (K241038)
Vitrea CT Multi-Chamber CFA (K141302)

Manufactured by Circle
Manufactured by Vital Images

• Myocardial Mass
• Wall Motion
• Percent Wall Thickening
• Myocardial Volume (MV)
• Myocardial Index
• Myocardial Mass Index
• Regional Ejection Fraction (EF)
• Outputs a Polar Map
• Time/Volume Graph
• Cardiac Index (CI)
• Regurgitation Fraction
• Cyclic Volume Change
• Reservoir Volume


Proposed device
uCT 780 with uWS-CT-Dual
Energy Analysis

Predict device
uCT 760/780 with uWS-
CT-Dual Energy Analysis
(K230162)

AI-based deep learning
reconstruction algorithm.

head, chest, abdomen, cardiac and
vascular CT applications for adults

The user interface
increased the choice for the
noise reduction level. This
will not introduce safety or
effectiveness issues.

The new dataset was increased to
127 cases compared with the
original dataset.

Dataset was increased .to
improve robustness of the
algorithm. The difference
will not introduce safety or
effectiveness issues.

Select recon type and strength
(noise index level).

between the proposed and predicate devices.


 Phantom test for dose
reduction, LCD, noise and
spatial resolution improving
test.
 Basic IQ performance test
including HU value on water
phantom, thickness section
test.
 Additional HU value test on
tissue-mimicking phantom,
noise spectrum power test.

Adding more phantom
tests, including HU value
test on tissue-mimicking
phantom and noise
spectrum power test to give
a more comprehensive tests
results. The difference will
not introduce safety and
effectiveness issues.

DELTA is able to generate CT
images at lower dose in the low
dose simulation validation and
generate equivalent or better
image quality compared with FBP
in the clinical scenarios of real
scan under a variety of dose levels.

DELTA is able to generate
CT images with lower image
noise and improve low
contrast detectability and
also to reduce the dose
required for diagnostic CT
imaging.

Substantially Equivalent.
The function of DELTA is
optimized in the proposed
device, which improved the
algorithm framework. The
difference will not
introduce safety and
effectiveness issues.


Body Part
Number
Age
Sex
Dose Distribution

The distribution of validation and test data was shown below:


Common Name:
M
edical Image Processing S

Classification Name:
S
ystem, Image Processing,

Regulation Description:
M
edical image management


5060 Shoreham Place, Ste 240
San Diego, CA 92122


Common Name:
Medical Image Processing Software

Classification Name:
System, Image Processing, Radiological

Medical image management and processing system


Device Name
NeuroQuant (Predicate, K170981)
NeuroQuant (Current Submission)

Automatic labeling, visualization and volumetric
quantification of segmentable brain structures
and lesions from a set of MR images.
Volumetric data may be compared to reference
percentile data

• Automated measurement of brain tissue
volumes and structures and lesions
• Automatic segmentation and quantification of
brain structures using a dynamic probabilistic
neuroanatomical atlas, with age and gender
specificity, based on the MR image intensity

• Automated measurement of brain tissue
volumes and structures and lesions
• Automatic segmentation and quantification of
brain structures and lesions using a dynamic
probabilistic neuroanatomical atlas, with age
and gender specificity, based on the MR image
intensity and static deep-learning technologies

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

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

• MRI scanner: 3D T1 and T2 FLAIR MRI scans
acquired with specified protocols
• NeuroQuant Supports DICOM format as input

• MRI scanner: 3D T1 and T2 FLAIR and T2*
GRE / SWI MRI scans acquired with specified
protocols
• NeuroQuant Supports DICOM format as input

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

Automated quality control functions:
-Tissue contrast check
-Scan protocol verification
-Atlas alignment check
Results must be reviewed by a trained physician

Summary Comparison Table for the device and predicate device (K170981):


Radiologist and neuro- and spine-
surgeons

Patients aged 18 years or older,
undergoing clinical lumbar spine MR
exams.
CoLumbo does not support DICOM
images of patients that are pregnant,
undergo MRI scan with contrast media,
or have post-operational complications,
scoliosis, tumors, infections, fractures.

Patients aged 18 years or older,
undergoing clinical lumbar spine MR
exams.
RAI does not support DICOM images
of patients that are pregnant, undergo
MRI scan with contrast media, or have
post-operational complications,
scoliosis, tumors, infections, fractures.

Software automatically generates
segmentations of anatomical features of
interest

Software automatically generates
measurements of interest

Software automatically highlights and
reports out-of-range measurements
based on predetermined thresholds

Software does not automatically
highlight or report any out-of-range
measurements

Exportation of measurement results to a
written report for user's review, revise
and approval

Exportation of measurement results in
both a DICOM Structured Report and
a .docx file for users to review and to
use full or partial list of software-
generated measurements to prepare
their own radiology report.

Deep Convolutional Image-to-Image
Neural Network

Comparison of Technological Characteristics with Predicate Device


IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION, Medical device software
— Software life cycle processes

ISO 14971 Third Edition 2019-12, Medical devices — Application of risk management
to medical devices

IEC 62366-1 Edition 1.1 2020-06 CONSOLIDATED VERSION, Medical devices —
Part 1: Application of usability engineering to medical devices

ISO 15223-1 Fourth edition 2021-07, Medical devices – Symbols to be used with
information to be supplied by the manufacturer – Part 1: General requirements

NEMA PS 3.1 – 3.20 2022d, Digital Imaging and Communications in Medicine
(DICOM) Set


The 200 MR studies were acquired on MRI imaging systems made by five (5) manufacturers.


RAI 510(k) Premarket Notification Remedy Logic Inc.


limit) for each measurement listed below.


limit (MDC Limit) for each segmentation listed below.


Anterior Vertebral Body Height (Sagittal)

Dural Sac Anterior-Posterior Diameter
(Axial)

Dural Sac Transverse Diameter (Axial)
2 mm

Middle Vertebral Body Height (Sagittal)
2 mm

Posterior Vertebral Body Height (Sagittal)

Spinal Canal Anterior-Posterior Diameter
(Axial)


Anterior
Vertebral Body
Height (Sagittal)

Dural Sac
Anterior-
Posterior
Diameter
(Axial)

Posterior
Vertebral Body
Height (Sagittal)

Spinal Canal
Anterior-
Posterior
Diameter
(Axial)

Spinal Canal
Transverse
Diameter
(Axial)


BriefCase-Quantification is a
radiological image
management and
processing system software
indicated for use in the
analysis of CT exams with
contrast, that include the
abdominal aorta, in adults or
transitional adolescents
aged 18 and older.
The device is intended to
assist appropriately trained
medical specialists by
providing the user with the
maximum abdominal aortic
axial diameter measurement
of cases that include the
abdominal aorta (M-AbdAo).
BriefCase-Quantification is
indicated to evaluate normal
and aneurysmal abdominal
aortas and is not intended to
evaluate post-operative
aortas.
The BriefCase-
Quantification results are not
intended to be used on a
stand-alone basis for clinical
decision-making or
otherwise preclude clinical
assessment of cases. These
measurements are
unofficial, are not final, and
are subject to change after
review by a radiologist. For
final clinically approved
measurements, please refer
to the official radiology
report. Clinicians are
responsible for viewing full
images per the standard of
care.

BriefCase-Quantification is a
radiological image
management and
processing system software
indicated for use in the
analysis of CT exams with
contrast, that include the
abdominal aorta, in adults or
transitional adolescents
aged 18 and older.
The device is intended to
assist appropriately trained
medical specialists by
providing the user with the
maximum abdominal aortic
diameter measurement of
cases that include the
abdominal aorta (M-AbdAo).
BriefCase-Quantification is
indicated to evaluate normal
and aneurysmal abdominal
aortas and is not intended to
evaluate post-operative
aortas.
The BriefCase-
Quantification results are not
intended to be used on a
stand-alone basis for clinical
decision-making or
otherwise preclude clinical
assessment of cases. These
measurements are
unofficial, are not final, and
are subject to change after
review by a radiologist. For
final clinically approved
measurements, please refer
to the official radiology
report. Clinicians are
responsible for viewing full
images per the standard of
care.

Appropriately trained
medical specialists.

Table 1: Comparison between Predicate and Subject Devices


CT exams with contrast that
include the abdominal aorta

Presentation of a
compressed, grayscale
preview image that is
captioned “Not for diagnostic
use”.

Maximum abdominal aortic
diameter with preview image
that contains the
measurement and key slice
with the long and short axis
of the aorta in the slice with
the maximum measurement.

Maximum abdominal aortic
diameter with preview image
that contains the
measurement and key slice
with the long and short axis
of the aorta in the
reformatted slice composed
of the plane that is
orthogonal to the blood flow
and not on the original axial
slice.

Artificial intelligence
algorithm with database of
images

- BriefCase-Quantification is
hosted on a cloud server,
analyzes applicable CT
images that are forwarded to
BriefCase-Quantification.
- The results of the analysis
are exported in DICOM
format, and are sent to a
PACS destination for review
by medical specialists, to
assist in the measurement of
the abdominal aorta.


Table 3. Frequency Distribution of Gender


Table 4. Frequency Distribution of Manufacturer


Applicant Name
Microstructure Imaging, Inc.

370 Jay St, 7th Floor
Brooklyn NY 11201 United States

Applicant Contact
Gregory Lemberskiy, PhD

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

Correspondent Contact
Michelle Rubin-Onur, PhD


Medical image management and processing system

Classification Name
System, Image Processing, Radiological


Predicate #
Predicate Trade Name
Product Code

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


Predicate Device (K200016)
uCT 530, uCT 550

 Rotation speed: up to
0.5s/rotation;
 70cm bore

 22mm Detector
 Material: Solid-state GOS
 40 rows,
 864 channels/row
 Size of detector element in Z-
plane: 0.55mm


 70, 80, 100, 120, 140 kV
 mA range: 10 mA-420 mA
 Anode heat capacity: 5.3MHU
 Maximum anode heat
dissipation:
9.6kW(815kHU/min)
 Focal spot size:
0.5mm × 1.0mm
1.0mm × 1.0mm

Substantially
Equivalent,
The difference will
not raise safety and
effectiveness
concerns.


Yes
It is an AI-based deep learning
algorithm to effectively reduce
image noise, enhance high-contrast
spatial resolution, improve low-
contrast detectability, and
correspondingly decrease the dose
required for diagnostic CT images.

Yes
It is an AI-based deep learning
algorithm to automatically
recommend the scan range.


It is an AI-based deep learning
algorithm to reduce the artifacts
caused by head motion.

Low Dose CT Lung
Cancer Screening
Protocol (LDCTLCS)


Technology
AI-based deep learning algorithm
AI-based deep learning algorithm
Same

Head, chest, abdomen and vascular
CT applications for adults

Head, chest, abdomen, cardiac
and vascular CT applications for
adults

The proposed device does not support
cardiac. This difference will not introduce
safety and effectiveness issues.

Increased noise reduction. This difference
will not introduce safety and effectiveness
issues.

Compared with DELTA (Deep Recon) algorithm cleared by K230162, detailed discussion about the difference are as follows:


The dataset was increased to 127
cases compared with the original
dataset.

Dataset was increased to improve algorithm
robustness. This difference will not
introduce safety and effectiveness issues.

Select recon type, strength (noise
index level)

Select recon type and strength
(noise index level)

Bench test for dose reduction,
LCD, noise reduction and spatial
resolution improving test. Basic IQ
performance test including HU
value on water phantom, thickness
section test.

DELTA supplies diagnostic image
quality at lower dose in the low
dose simulation validation and
equivalent or better image quality
compared with FBP in the
evaluation of clinical cases covering a
variety of dose level.

DELTA is able to generate CT
images with lower image noise
and improved low contrast
detectability, also reduce the dose
required for diagnostic CT
imaging.

Substantially Equivalent
The function of DELTA has been
optimized in the uCT 550. The difference
will not raise new safety and effectiveness
concerns.


Body Part
Number
Age
Sex
Dose Distribution

validation and test data was shown below:


Proposed devices –
Primary Predicate devices –
Discussion

Low Dose CT Lung Cancer Screening (LD LCS) Option

The Cleared Arineta’s SpotLight / SpotLight Duo

(K230370) CT X-ray System (with DLIR option)

The SpotLight / SpotLight Duo is intended to produce cross-
sectional images of the body by computer reconstruction of
X-ray transmission projection data taken at different angles.
The system has the capability to image cardiovascular and
thoracic anatomies, including the heart, in a single rotation.
The system may acquire data using Axial, Cine and Cardiac
scan techniques from patients of all ages (DLIR is limited for
patient use above the age of 2 years). These images may be
obtained either with or without contrast. This device may
include signal analysis and display equipment, patient and
equipment supports, components and accessories.
This device may include data and image processing to
produce images in a variety of trans-axial and reformatted
planes.
The system is indicated for x-ray Computed Tomography
imaging of cardiovascular and thoracic anatomies that fit in
the scan field-of-view.
The Low Dose CT Lung Cancer Screening Option for
SpotLight / SpotLight Duo is indicated for using low dose CT
for lung cancer screening. The screening must be conducted
with the established program criteria and protocols (for
medium and large patients) that have been approved and
published by a governmental body or a professional medical
society. Information from professional societies related to
lung cancer screening can be found but is not limited to:
American College of Radiology® (ACR) – resources and
technical specification; accreditation American Association of
Physicists in Medicine (AAPM) – Lung Cancer Screening
Protocols; radiation management. Please refer to clinical
literature, including the results of the National Lung
Screening Trial (N Engl J Med 2011; 365:395-409) and
subsequent literature, for further information.
The DLIR and ASIR-CV options are not compatible with the
Low Dose Lung Cancer Screening option.
The device output is useful for diagnosis of disease or
abnormality and for planning of therapy procedures.

The SpotLight / SpotLight Duo (with DLIR option) is intended
to produce cross-sectional images of the body by computer
reconstruction of x-ray transmission projection data taken at
different angles. The system has the capability to image
cardiovascular and thoracic anatomies, including the heart,
in a single rotation. The system may acquire data using
Axial, Cine, and Cardiac scan techniques from patients of all
ages (DLIR is limited for patient use above the age of 2
years). These images may be obtained either with or without
contrast. This device may include signal analysis and display
equipment, patient and equipment supports, components
and accessories.
This device may include data and image processing to
produce images in a variety of trans-axial and reformatted
planes.
The system is indicated for X-ray Computed Tomography
imaging of cardiovascular and thoracic anatomies that fit in
the scan field of view.
The device output is useful for diagnosis of disease or
abnormality and for planning of therapy procedures.

The indications for use are
similar, with the same
indications for the entire
device, except for minor
differences; the indications
for use for the proposed
SpotLight/SpotLight Duo
devices include Low Dose
Lung Cancer Screening
Option with additional
references to ACR and
AAPM guidelines, and
specific DLIR and ASIR-CV
limitation for that option. LCS
indication for use does not
constitute a new intended
use for the CT.