The evaluation on the test dataset confirmed very similar metrics in
terms of peak signal-to-noise ratio (PSNR), structural similarity index
(SSIM) and learned perceptual image patch similarity metrics (LPIPS) for
the predicate and the modified network with both outperforming
conventional GRAPPA as the reference. Visual evaluations confirmed
statistically significant reduction of banding artifacts with no significant
changes in sharpness and detail visibility. In addition, the radiologist
evaluation revealed no difference in suitability for clinical diagnostics
between updated and cleared predicate network.
The function as on the predicate devices was modified to the subject
devices but the training and testing from the predicate devices still fits.

Equipment: 0.55T, 1.5T and 3T MRI scanners
Protocols: representative protocols (T1, T2 and PD with and without fat
saturation)
Body regions: broad range of different body regions
Used coils: broad range of coils to cover the dedicated body regions
Testing of the Deep Resolve Boost network has been described in the
reference and predicate device submissions. Additional tests have been
performed to evaluate the banding artifact reduction capabilities of the
updated network.

Dataset split: Training: more than 23250 slices (93%)


Validation: more than 1750 slices (7%)
Additional test dataset for banding artifact reduction:
more than 2000 slices
For training and validation of the network, the identical data was used as
for the initial device submission (K213693).
Note: Data split maintained similar data distribution (e.g., contrast,
orientation, field strength, …) in both training and validation datasets.

Sample source: in-house measurements and collaboration partners

Due to reasons of data privacy, gender, age and ethnicity during data
collection have not been recorded. Due to the network architecture,
attributes like gender, age and ethnicity are not relevant to the training
data.

No clinical subgroups have been defined for the collected dataset

The acquired training/validation datasets (identical to the initial
submission K213693) represent the ground truth for the training and
validation. Input data was retrospectively created from the ground truth
by data manipulation and augmentation. This process includes further
undersampling of the data by discarding k-space lines, lowering of the
SNR level by addition of noise and mirroring of k-space data.

Training and validation datasets were kept independent from each other
during training and validation. The acquired datasets (one dataset
consists of a group of multiple slices) were split into 93% training and 7%
validation data prior to the training. A similar distribution was
maintained for training and validation data. The test dataset for banding
artifact reduction was acquired after the release of the predicate
network and is therefore independent of the training/validation data.


Medical electrical equipment - Part
1: General requirements for basic
safety and essential performance
(IEC 60601-1:2005, MOD)

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

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

Medical devices - Application of risk
management to medical devices

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

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

Acoustic Noise Measurement
Procedure for Diagnosing Magnetic
Resonance Imaging Devices

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

Digital Imaging and
Communications in Medicine
(DICOM)

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


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 are
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.
This medical device consists of a 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

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


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.

The PeekMed web allows the surgeon to
perform orthopedic pre-surgical planning
efficiently in the musculoskeletal system
(e.g., Hip procedures, Knee procedures)

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

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 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.

Internet connection
Required
Required
Yes
---

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


Human
intervention for
image
interpretation

Ability to add
additional modules
when available

Yes
- Hip (X-ray and CT scan)
- Knee (X-ray, CT scan, and MRI)
- Upper limb (CT scan)
- Foot (X-ray and CT scan)

Yes
- Hip (X-ray and CT scan)
- Knee (X-ray - AP & LAT view, CT scan,
and MRI)
- Upper limb (CT scan)
- Foot (X-ray and CT scan)

The subject device includes new ML
model variants for the segmentation of
the Knee in a Lateral view.
Both devices allow planning for the
knee region with X-rays in an AP view,
but the subject device also offers a
new image perspective (Lateral view).


This does not constitute an intended
purpose update, nor does it raise
questions of safety and performance,
since the development, verification,
validation, and deployment processes
are the same for both devices.
Compared to the predicate device, the
subject device includes updates to
existing ML variants and the introduction
of new ML variants, such as the
segmentation and landmarking for the
knee region. In addition, certain
previously cleared variants have
undergone updates to improve
performance while maintaining the same
intended use. These modifications do
not alter the overall intended use of the
device

Automatic or Manual
- Hip (X-ray and CT scan)
- Knee (X-ray, CT scan, and MRI)

Automatic or Manual
- Hip (X-ray and CT scan)
- Knee (X-ray - AP & LAT view, CT scan,

The subject device includes new ML
model variants for landmarking for the
Knee in a Lateral view.


and MRI)
- Upper limb (CT scan)
- Foot (X-ray and CT scan)

Both devices allow planning for the
knee region with X-rays in an AP view,
but the subject device also offers a
new image perspective (Lateral view).
This does not constitute an intended
purpose update, nor does it raise
questions of safety and performance,
since the development, verification,
validation, and deployment processes
are the same for both devices.
Compared to the predicate device, the
subject device includes updates to
existing ML variants and the introduction
of new ML variants, such as the
segmentation and landmarking for the
knee region. In addition, certain
previously cleared variants have
undergone updates to improve
performance while maintaining the same
intended use. These modifications do
not alter the overall intended use of the
device


Yes – Reconstruction of 3D anatomical
representation from 3D imaging modalities
(e.g., CT, MRI).

Yes
Reconstruction of 3D anatomical
representation from 3D imaging modalities
(e.g., CT, MRI) and additionally from 2D
imaging modalities (X-ray).

Both devices provide reconstruction
capabilities for generating an anatomical
representation. The subject device
extends the input modalities to include 2D
X-rays, enabling reconstruction of 3D
models where only planar images are
available. This does not alter the intended
use or indications for use, as the function
remains within pre-surgical planning for
the same anatomical regions. The
reconstructed models can be integrated
into planning and analysis workflows. The
capability is decision-support only, with
outputs subject to review and approval by
healthcare professionals. The
development, verification, validation, and
deployment processes follow the same
methodology, and no new safety or
effectiveness concerns are raised.


Automated radiological image processing software


Medical image management and
processing system.


All devices fall under the same regulation 21 CFR
892.2050.
The subject device also falls under the same
product codes as the predicate and reference
devices.

The Neosoma
software uses an
artificial intelligence
algorithm (i.e., deep
learning neural
networks) to contour
(segment) known or
previously diagnosed
brain tumors on MRI
images for qualified
and trained medical
professionals.
The technology is
meant for
informational
purposes only and not
intended to replace
the clinician’s current
standard practice of
manual contouring.
The software does not

VBrain is a software
device intended to
assist trained medical
professionals, during
their clinical workflows
of radiation therapy
treatment planning, by
providing initial object
contours of known
(diagnosed) brain
tumors (i.e., region of
interest, ROI) on axial
Tl contrast enhanced
brain MRI images.
VBrain uses an
artificial intelligence
algorithm (i.e., deep
learning neural
networks) to contour
(segment) brain
tumors on MRI images

NS-HGlio is intended
for the semi-automatic
labeling, visualization,
and volumetric
quantification of
high-grade brain
glioma (WHO grade 3
astrocytoma, WHO
grade 4
astrocytoma and WHO
grade 4 glioblastoma)
from a set of standard
MRI images of male or
female patients 18
years of age or older
who are known to
have pathologically
proven
high-grade glioma.
Volumetric
measurements may be

The indications for use of Neosoma software
and VBrain are highly similar in scope, intended
user, and clinical application. Both devices are
software tools designed to assist trained medical
professionals, particularly in the context of
radiation therapy treatment planning and
related clinical workflows, by providing
semi-automatic contouring of known or
previously diagnosed brain tumors on MRI
images. Both employ artificial intelligence (deep
learning neural networks) to generate Gross
Tumor Volume (GTV) contours, are intended for
informational purposes only, do not replace
standard manual contouring practices, do not
alter the original MRI image, and are not
intended for tumor detection or diagnosis.


MRI:
T1 precontrast, T1
postcontrast, T2 and
T2-FLAIR

Anatomical Target
Brain
Brain
Brain
Same.

Image Review
2D and 3D
2D and 3D
2D and 3D
Same.

ML based
semi-automatic
segmentation of brain
metastasis

ML based
semi-automatic and
manual segmentation
of brain
metastasis

ML based
semi-automatic
segmentation of
brain high grade
glioma

Volumetric
measurement of the
brain metastasis

Volumetric
measurement of
the brain high
grade glioma

Provides volumetric
measurements of
brain metastasis.
Includes segmented
color overlays of
brain metastasis
and reports.

Provides
volumetric
measurements of brain
high grade
glioma. Includes
segmented color
overlays.

The subject device uses the same pre and post


Metric
Acceptance Criteria
Performance Results

between device and reference standard surfaces.


0.57 lesions per MRI with
95% CI of 0.35 - 0.80


Criteria
Diagnocat (Subject Device)
Overjet Periapical Radiolucency

Classification
21 CFR 892.2070 (Medical Image
21 CFR 892.2070 (Medical Image

Overjet Periapical Radiolucency
(PARL) Assist is a radiological,
automated, concurrent read computer-
assisted detection software intended to
aid in the detection of periapical
radiolucency on permanent teeth
captured on periapical radiographs.
The device provides additional aid for
the dentist to use in their identification
of periapical radiolucency. The device
is not intended as a replacement for a
complete dentist’s review or their
clinical judgment that considers other
relevant information from the image or
patient history. The system is to be
used by professionally trained and
licensed dentists.
The Overjet Periapical Radiolucency
Assist software is indicated for use on
patients 12 years of age or older.

Use
automated, concurrent read computer-
(PARL) Assist is a radiological,

assisted detection software intended to
automated, concurrent read computer-

images, using scans that were previously

acquired for clinically justified purposes

independent of Diagnocat. Diagnocat
of periapical radiolucency. The device

may be used only when a dental
is not intended as a replacement for a

professional has independently
complete dentist’s review or their

determined that CBCT imaging is
clinical judgment that considers other

necessary for further evaluation of the
relevant information from the image or

aid for the dental professional to use in

images or patient history. The system is

including at least two years of clinical

Diagnocat is indicated for use by dental

Detection Outputs
CBCT visualization
Localization: CADe of

2) Pathology Detection with
suspected dental findings per

Dental Findings
Periapical radiolucency
Periapical radiolucency

Table 1: Comparison of Diagnocat and Predicate Device


Criteria
Diagnocat (Subject Device)
Overjet Periapical Radiolucency

Reader Workflow
Concurrent Reading
Concurrent Reading

Algorithm
Supervised machine learning
Supervised machine learning

Image Format
DICOM , JPEG, TIFF, PNG
JPG, PNG, EOP, JIF, DICOM

Configuration
Web and Desktop application
Desktop application


Periapical Radiolucency Segmentation
Cohort 2
0.804


increased by 0.027 compared to unaided interpretation.


Subject Device
Aidoc BriefCase-Triage: CARE Multi-triage
CT Body

Predicate Device
Aidoc Briefcase-Triage for AD (K251406)

BriefCase-Triage: CARE (Clinical AI
Reasoning Engine) Multi-Triage CT Body is a
radiological computer aided triage and
notification software indicated for use in
the analysis of contrast and non-contrast CT
images of the chest, abdomen, and/or
pelvis, in adults or transitional adolescents
aged 18 and older. The device is intended to
assist hospital networks and appropriately
trained medical specialists in workflow
triage by flagging and communicating
suspected positive findings, per study, of:
1. Diverticulitis;
2. Abdominal-pelvic abscess;
3. Appendicitis;
4. Intestinal ischemia and/or
pneumatosis;
5. Obstructive renal stone;
6. Small bowel obstruction;
7. Large bowel obstruction;
8. Spleen injury;
9. Liver injury;
10. Kidney injury;
11. Pelvic fracture.
The device flags cases with at least one
suspected finding to assist with
triage/prioritization of medical images. The
device will provide a flag for each suspected
finding within this study. A preview image
will be provided for each distinct suspected
finding.
BriefCase-Triage uses a foundation
model-based artificial intelligence (AI)

BriefCase-Triage is a radiological
computer aided triage and notification
software indicated for use in the analysis
of CT chest, abdomen, or chest/abdomen
exams with contrast (CTA and CT with
contrast) in adults or transitional
adolescents aged 18 and older. The
device is intended to assist hospital
networks and appropriately trained
medical specialists in workflow triage by
flagging and communication of suspected
positive findings of Aortic Dissection (AD)
pathology.
BriefCase-Triage uses an artificial
intelligence algorithm to analyze images
and highlight cases with detected findings
on a standalone desktop application in
parallel to the ongoing standard of care
image interpretation. The user is
presented with notifications for cases
with suspected findings. Notifications
include compressed preview images that
are meant for informational purposes
only and not intended for diagnostic use
beyond notification. The device does not
alter the original medical image and is
not intended to be used as a diagnostic
device.
The results of BriefCase-Triage are
intended to be used in conjunction with
other patient information and based on
their professional judgment, to assist
with triage/ prioritization.


system to analyze images and highlight
cases with detected findings in parallel to
the ongoing standard of care image
interpretation. The user is presented with
notifications for cases with suspected
findings. Notifications include compressed
preview images for each suspected finding
that are meant for informational purposes
only and not intended for diagnostic use
beyond notification. The device does not
alter the original medical images and is not
intended to be used as a diagnostic device.
The results of BriefCase-Triage are intended
to be used in conjunction with other patient
information and based on their professional
judgment to assist with triage/prioritization
of medical images. Notified clinicians are
responsible for viewing full images per the
standard of care.

Hospital networks and appropriately trained
medical specialists

1. Diverticulitis;
2. Abdominal-pelvic abscess;
3. Appendicitis;
4. Intestinal ischemia and/or
pneumatosis;
5. Obstructive renal stone;
6. Small bowel obstruction;
7. Large bowel obstruction;
8. Spleen injury;
9. Liver injury;
10. Kidney injury;


Notificatio
n-only
(/notificati
on alerts),
parallel
workflow
tool

No. No cases are removed from
desktop app or deprioritized


Inclusion/
Exclusion
criteria for
clinical
performa
nce
testing

Inclusion Criteria:
● Patient population: CT scans
performed on adults/transitional
adults ≥ 18 years of age
● Slice thickness: 0.5 mm - 5.0 mm
axial
● Contrast-enhanced and
non-contrast CT images*
Exclusion Criteria
● All studies that have an inadequate
field of view.
*Contrast and non-contrast CT images of
the chest, abdomen, and/or pelvis, as
applicable to indication-specific inclusion
criteria.

Inclusion Criteria
● Scans performed on
adults/transitional adolescents ≥
18 years of age.
● CT exams with contrast (CTA and
CT with contrast) that include at
least part of the aorta
● Slice thickness 0.5 mm - 5.0 mm
Exclusion Criteria
● All studies that have an
inadequate field of view.

Multi-triage module, locked artificial
intelligence algorithm fine tuned from a
foundation model.

Single-triage module, locked, artificial
intelligence algorithm fine tuned from a
foundation model..

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


AUC
Sensitivity (Se)
Specificity (Sp)
Case Count


Indication 4: Intestinal ischemia and/or pneumatosis


BriefCase-Triage and
compatible image
communication
platform
Time-to-notification

Table 3. Time-to- notification comparison for Briefcase-Triage devices (Seconds)


Table 5. Descriptive Statistics for Gender


Indication 8: Spleen injury
* 4 cases were unknown for gender, all positive.

Indication 9: Liver injury
* 7 cases were unknown for gender, all positive.


Indication 11: Pelvic fracture
* 9 cases were unknown for gender, all positive.


Indication 4: Intestinal ischemia and/or
pneumatosis

Table 6. Frequency Distribution of Manufacturer


Table 7. Frequency Distribution of Slice Thickness


Table 8. Sensitivity, Specificity for AOP1-AOP4


Tiffany Zhang
Regulatory Affairs Manager
Phone: +86-0-18911415886
E-mail: tiffany.zhang@philips.com

Carmit Shmuel
Regulatory Affairs Manager and Site Lead
Phone: (972) 54-2109054
E-mail: Carmit.shmuel@philips.com


Commerical/Trade name:
CT Rembra RT
CT Areta RT
CT Rembra

Product Name
CT 5400 RT
CT 5200 RT
CT 5400

Legal Manufacturer:
Philips Healthcare (Suzhou) Co., Ltd.

Classification Name:
Computed tomography x-ray system

Classification Regulation:
21 CFR 892.1750


Manufacturer:
Philips Healthcare (Suzhou) Co., Ltd.


Classification Name:
Computed tomography x-ray system


Manufacturer:
Philips Medical Systems Nederland B.V.


Medical image management and processing system
Computed tomography x-ray system


Whole-body CT x-ray system
with continuous rotation

Use environment,
users, and patient
population

Hospital use; trained
operators; patients of all ages

Diagnostic imaging including
oncology and radiation
therapy planning

CT5300, Philips
CT Big Bore,
Spectral CT
7500 RT

Continuous rotation; axial and
helical scan modes

Image quality, noise, slice
thickness, dose management

Complies with applicable
NEMA and regulatory
standards

Incisive Host Platform
(updated version)

Modified; supported
by verification and
validation testing

Comparable design
supported by
predicate

Modified; supported
by predicate
technology and
testing

Detector
Updated detector elements
CT5300
Modified; supported

Table 1. Summary of Technological Comparison and Basis for Substantial Equivalence


CT 5300,
Spectral CT
7500 RT,
Philips CT Big
Bore

Pulmonary gating (4D CT)
Extended field of view
(EFOV), CT Collaboration
Live, Precise Position, Precise
Intervention

MM Sim auto-launch,
CANOpen injection, Body
perfusion

New features
supported by
verification and
validation testing


Medical Electrical Equipment – Part 1:
General Requirements for Basic Safety and
Essential Performance (IEC 60601-1:2005,
MOD)


Medical Electrical Equipment –
Electromagnetic disturbances (EMC)
requirements and tests

Radiation protection in diagnostic X-ray
equipment

Usability engineering for medical electrical
equipment

Particular requirements for CT equipment
safety and performance

Medical device software – Software lifecycle
processes

Application of usability engineering to
medical devices

Application of risk management to medical
devices

Biological evaluation of medical devices –
Part 1

Access Controls for CT: Identification,
Interlocks, and Logs

User information and system function
related to dose in CT

Standard attributes related to dose
optimization and management


Guidance for the Content of Premarket Submissions for Device Software
Functions

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

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

Pediatric Information for X-ray Imaging Device Premarket Notifications

Applying Human Factors and Usability Engineering to Medical Devices

Medical X-Ray Imaging Devices Conformance with IEC Standards

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

Electromagnetic Compatibility (EMC) of Medical Devices

Performance Standards for CT Equipment and Laser Products (21 CFR
1020.33 and 21 CFR 1040.10)

Use of International Standard ISO 10993-1


Feature
Testing Performed
Summary of Results

Phantom testing, system
V&V, external image
quality review

Demonstrated acceptable image quality
and reduced motion artifacts for
intended use

Demonstrated acceptable geometric
and quantitative accuracy for RT
planning applications

Body Perfusion, MM
Sim auto-launch,
OnPlan

Demonstrated acceptable performance
for intended use

Table 4. Summary of Non-Clinical Performance Testing


Operating point
Group
Sensitivity1
95% confi-dence
Specificity
95% confi-

1 Result of a representative standalone study with 306 CBCTs

included lesion size, age, device, gender, and endodontic treatment status, with results as follows:


The MRMC Tooth Level AUC results are as follows:


Biograph Vision PET/CT with
PETsyngo VG85A

Siemens
Shenzhen
Magnetic
Resonance
Ltd.


improvements compared to conventional parallel imaging. An
inspection of the test images did not reveal any negative
impact to the image quality. The function has been used either
to acquire images faster or to improve image quality.

Equipment: 0.55T, 1.5T and 3T scanners
Protocols: representative measurement protocols (T1, T2 and
PD with and without fat saturation) which have been altered
for training (e.g. to increase SNR, increase resolution or
reduce acceleration).
Body regions: broad range of body regions
Used coils: broad range of coils to cover the dedicated body
regions

Sample size: 27,679 3D patches from 1265 measurements

Dataset split: Training: 81% of the 1265 measurements
Validation: 19% of the 1265 measurements
Note: Data split maintained similar data distribution (e.g.,
contrast, orientation, field strength, …) in both training and
validation datasets.

Sample source: in-house measurements (training and
validation) and collaboration partners (testing)

Gender distribution:
- Male: 53%
- Female 47%
Age: for training and validation.
- 19 - 45: 14%
- 46 - 65: 43%
- 66 - 89: 43%
Clinical subgroups: No clinical subgroups have been defined
for the datasets.

The acquired datasets (as described above) represent the
ground truth for the training and validation. Input data was
retrospectively created from the ground truth by data
manipulation and augmentation. This process includes further
undersampling of the data by discarding k-space lines as well
as creating sub-volumes of the acquired data.

Datasets determined for training and validation were split prior
to training along individual acquisitions to ensure that there is
no mixture of sub-volumes stemming from the same
acquisition.


tests show increased edge sharpness and reduced Gibb’s
artifacts.

Equipment: 0.55T, 1.5T and 3T MRI scanners
Protocols: representative measurement protocols (T1, T2 and
PD with and without fat saturation) which have been altered
(e.g. to increase SNR, increase resolution or reduce
acceleration)
Body regions: broad range of different body regions.
Used coils: broad range of coils to cover the dedicated body
regions

Sample size: approx. 13,000 high resolution 3D patches from
500 measurements.

Dataset split: Training: 70% of the 500 measurements.
Validation: 30% of the 500 measurements
Note: Data split maintained similar data distribution (e.g.,
contrast, orientation, field strength, …) in both training and
validation datasets.

Gender distribution:
- Male: 66.6%
- Female 33.4%
Age: for training and validation.
- 19 - 45: 8.4%
- 46 - 65: 40.2%
- 66 - 89: 51.4%
Clinical subgroups: No clinical subgroups have been defined
for the datasets.

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

The high-resolution datasets were split to 70% training and
30% validation datasets before training to ensure
independence of them. The input and output variables of the
network have been derived from the same dataset so that no
confounders exist for the training methodology.


statistically significant reduction of banding artifacts with no
significant changes in sharpness and detail visibility. In
addition, the radiologist evaluation revealed no difference in
suitability for clinical diagnostics between updated and cleared
predicate network.
The function as on the predicate devices was modified to the
subject devices but the training and testing from the predicate
devices still fits.

Equipment: 0.55T, 1.5T and 3T MRI scanners
Protocols: representative protocols (T1, T2 and PD with and
without fat saturation)
Body regions: broad range of different body regions
Used coils: broad range of coils to cover the dedicated body
regions
Testing of the Deep Resolve Boost network has been
described in the reference and predicate device submissions.
Additional tests have been performed to evaluate the banding
artifact reduction capabilities of the updated network.

Dataset split: Training: more than 23250 slices (93%)
Validation: more than 1750 slices (7%)
Additional test dataset for banding artifact
reduction: more than 2000 slices
For training and validation of the network, the identical data
was used as for the initial device submission (K213693).
Note: Data split maintained similar data distribution (e.g.,
contrast, orientation, field strength, …) in both training and
validation datasets.

Sample source: in-house measurements and collaboration
partners

Due to reasons of data privacy, gender, age and ethnicity
during data collection have not been recorded. Due to the
network architecture, attributes like gender, age and ethnicity
are not relevant to the training data.

No clinical subgroups have been defined for the collected
dataset

The acquired training/validation datasets (identical to the initial
submission K213693) represent the ground truth for the
training and validation. Input data was retrospectively created
from the ground truth by data manipulation and augmentation.
This process includes further undersampling of the data by
discarding k-space lines, lowering of the SNR level by addition
of noise and mirroring of k-space data.

Training and validation datasets were kept independent from
each other during training and validation. The acquired
datasets (one dataset consists of a group of multiple slices)
were split into 93% training and 7% validation data prior to the


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

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

Medical devices - Application
of risk management to
medical devices

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

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

Acoustic Noise Measurement
Procedure for Diagnosing
Magnetic Resonance Imaging
Devices

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

Digital Imaging and
Communications in Medicine
(DICOM)

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

Performance Measurements
of Positron Emission
Tomographs


Subject Device:
AI-Rad Companion
Brain MR VA60

Pre-processing
functionality for automatic
segmentation and
volumetry of MPRAGE
data.

Image
processing for
automatic
segmentation
and volumetry
of MPRAGE
data.

Calculation of label maps
(display of brain
segmentation) and partially
combined label maps
(fused with the processed
MPRAGE data).

Normalized and
unnormalized
volume and
volume changes
of different
brain structures.

Calculation of deviation
map (representation of
brain status in relation to
reference data) and
partially combined
deviation maps (fused with
the processed MPRAGE
data) User customizable
color labels for the overlay
map.

Automatic calculation of
the atrophy range in


percentage for each
segmented brain structure

Brain Morphometry
Follow-Up Time
Between Studies

Configurable time interval
between the current and the
prior scan should be ≥ 180
days and < the
retention period.

Brain White Matter
Hyperintensities
Segmentation

Pre-processing
functionality for automatic
segmentation and
volumetry of MPRAGE
and FLAIR data.

Image
processing for
automatic
segmentation
and volumetry
of FLAIR data.

Brain White Matter
Hyperintensities
Quantification

Calculation of white
matter hyperintensities
count and volume as per 4
brain regions.

Unnormalized
volume and
volume changes
of FLAIR white
matter
hyperintensities
as per 4 brain
regions

Calculation of white
matter hyperintensities
map fused with the
processed FLAIR data
User customizable color
labels for the overlay map.

Calculation of
white matter
hyperintensities
map overlaid
with the FLAIR
data

Brain White Matter
Hyperintensities
Input Data

T1-weighted MPRAGE
and T2-weighted 3D
FLAIR image series


MR images from two time
points to identify new or
enlarged areas. The results
of follow-up scan are then
refined by using
morphological operations
to generate WMH changed
areas between baseline and
follow-up scan

Assessment of
New/Enlarging
Lesion count

White Matter
Hyperintensities
Follow Up Input
Data

WMH follow-up supports
1.5T and 3T input data in
both prior and current
study

WMH follow-up is only
supported for 3T input data
in both prior and
current study.

White Matter
Hyperintensities
Follow-Up Data
Criteria

Not mandatory to have the
same Slice Thickness,
Pixel Spacing, and
Magnetic Field Strength
consistent between prior
and current studies.
*In case if the prior and
current Magnetic Field
Strength (0018, 0087) are
not same, the system will
process the input data with
an information message.

Mandatory to have the
same Slice Thickness,
Pixel Spacing, and
Magnetic Field Strength
consistent between prior
and current studies.

White Matter
Hyperintensities &
Follow-Up
Multi-Vendor
Support

Validated with data from
Siemens Healthineers, GE
and Philips

Only supports data
acquired on 3 Tesla
Siemens Healthineers MR
scanners

Creation of an image series
for a morphometry report.
Automatic transfer of


generated maps and
morphometry report to a
PACS system.

Confirmation UI with
basic visualization
functionality

Simplified layout
dedicated for confirmation
of results

Cloud solution and Edge
components deployed on
customer premise.

Cloud only
solution with no
components
deployed on
customer
premise.

DICOM structured report
representation of a natural
language report


Medical Devices – Application
of usability engineering to
medical devices [including
Corrigendum 1 (2016)]

IEC 62366-1
Edition 1.1
2020-06
CONSOLIDATED
VERSION

Medical Devices – application
of risk management to
medical devices

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

IEC 62304
Edition 1.1
2015-06
CONSOLIDATED
VERSION

Digital Imaging and
Communications in Medicine
(DICOM) Set

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

Health software – Part 1:
General requirements for
product safety

FDA recognized Consensus Standards listed in Table 2.


Device Trade
Regulation
Common name
Device
Product
Classification

Medical charged-particle
radiation therapy system

MUJ
Associated
Product
Code(s):
QKB, LLZ

System,
Planning,
Radiation
Therapy
Treatment


Predicate trade name
(primary predicate is listed
first)


Property
Proposed Device
Primary Predicate ART-
Reference Device n°2
Comment

ART-Plan+ v3.1.0
Plan+ v3.0.0
ART-Plan v2.2.0

Standalone software
application accessed via
a compliant browser
(Chrome, Mozilla Firefox
and Edge) on a personal
computer, tablet or
phone (In case of
connection to the
platform with a screen of
a phone or a tablet, the
user must choose the
option for the desktop
site of his communication
device. The platform is
optimally used with 17
inches and up screen.
Facilitates display and
visualization of data by
user.

The proposed device, the primary
predicate and the reference
devices are standalone software.

The proposed device, primary
predicate and the reference
devices share an AI delineation
method.

Generation of CT density
image series out of
CBCT images

Generation of CT density
image series out of multiple
MR-image series and CBCT
images

The proposed device and
reference device share the same
module named AdaptBox that can
generate synthetic-CT from CBCT
images.

Dose computation on CT
and/or synthetic-CT
images for external beam
irradiation with photon
beams

The proposed device and the
reference device share the same
module named AdaptBox that can
perform dose computation.

reference device that represent an additional claim for the proposed device.


Assisted CBCT-based
off-line adaptation
decision-making for
supported anatomies

Assisted CBCT-based off-
line adaptation decision-
making for supported
anatomies

The proposed device and the
reference device share the same
module named AdaptBox that can
assist off-line adaptation decision-
making.

Segmentation:
CT (injected or not), MR
images, DICOM
RTSTRUCT, synthetic-
CT from CBCT

Segmentation:
CT (injected or not), MR
images, DICOM RTSTRUCT,
synthetic-CT from CBCT

The proposed device, the primary
predicate and the reference
devices propose segmentation on
medical images of different
modalities.

Distribution of DICOM
compliant Images into
other DICOM compliant
systems.

The proposed device, the primary
predicate and the reference
devices have identical data export
capabilities with DICOM format.

Compatible with data
from any DICOM
compliant systems for the
applicable modalities.

Compatible with data from
any DICOM compliant
scanners for the applicable
modalities.

The proposed device, the primary
predicate and the reference
devices have identical
compatibility (DICOM format)


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

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

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

The proposed device, the
primary predicate and also
the reference device (when
it comes to segmentation on
synthetic-CT from CBCT
image used within the
shared module, AdaptBox)
propose segmentation on
the same localizations with
the same medical images of
different modalities using
AI.

applicable modalities. modalities. compatibility (DICOM format)


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

Predicate Device
uCT 780 with uWS-CT-
Dual Energy Analysis
(K241079)

 Model: uXG 100 for
mobile,
CT140N80X4889 for
non-mobile;
 Maximum power:
100kW
 kV settings: 70kV,
80kV, 100kV, 120kV,
140kV

 Model:
CT140N80X4889
 Maximum power:
100kW
 kV settings: 70kV,
80kV, 100kV, 120kV,
140kV

The proposed device adds
mobile configuration and
introduces a new high voltage
generator (uXG 100) for
mobile configuration. uXG 100
has consistent performance
parameters as
CT140N80X4889.
The difference did not raise
new safety and effectiveness
concerns.

 40mm Detector
 Rotation speed: up to
0.3s/rotation
 70cm bore

Gantry tilt lock is added for
mobile configuration.
Performance parameters of
gantry for mobile configuration
is consistent as non-mobile
configuration.
The difference did not raise
new safety and effectiveness
concerns.


 Mobile: Standard
config patient table
 Non-mobile: Standard
config patient table
and High config
patient table

 Standard config
patient table
 High config patient
table

Only standard config patient
table is used for mobile
configuration. Patient table
lock is added for mobile
configuration. Patient table
lock device does not change
the performance of the
standard config patient table.
The difference did not raise
new safety and effectiveness
concerns.

 Mobile: Wireless
VSM
 Non-mobile: Wireless
VSM and Wired VSM

Only Wireless VSM is used for
mobile configuration.
The difference did not raise
new safety and effectiveness
concerns.

• Power voltage:
380VAC/400VAC/41
5VAC/440VAC/460V
AC/480VAC
• Power frequency:
50/60 Hz

PSC is strengthened for mobile
configuration and a shock-
absorbing base is added. Also,
the fixing method of PSC is
optimized. Performance
parameters of PSC for mobile
configuration are consistent as
non-mobile configuration.
The difference did not raise
new safety and effectiveness
concerns.


Before reliability test, a comprehensive test is conducted on
the mobile configuration of uCT 780, including device
performance and image performance.
The test results indicate that all test items have been passed.

Vibration and shock tests are conducted on key components
of the above sample device such as gantry, patient table, and

Table 2 Result for environmental Testing


PSC respectively according to IEC 60068-2-6, IEC 60068-2-
27 and IEC 60068-2-64 .
Reliability test conditions:
 Sine sweep：2Hz~200Hz~2Hz as one cycle, 6 cycles in
X/Y/Z direction, respectively.
 Random vibration: 36h in X/Y/Z direction, respectively.
 Shock: ±2.5g in X/Y direction, ±4g in Z direction; 400
cycles in each direction.
During reliability condition test, the appearance of the system
is in good condition, and there is no loose, damage and
fracture.

After reliability test, the system is re-integrated and the same
test as Pre-test is re-conducted, including device performance
and image performance.
The test results indicate that all test items have been passed.
The vibration and shock don’t impact the device performance
metrics and image quality.


Subject Device
Aidoc Briefcase-Triage for IFG

Predicate Device
Aidoc Briefcase-Triage for IFG (K193298)

BriefCase-Triage is a radiological computer
aided triage and notification software
indicated for use in the analysis of abdominal
CT images in adults or transitional
adolescents aged 18 and older. The device is
intended to assist hospital networks and
appropriately trained medical specialists in
workflow triage by flagging and
communication of suspected positive findings
of Intra-abdominal free gas (IFG) pathologies.
BriefCase-Triage uses an artificial intelligence
algorithm to analyze images and highlight
cases with the detected findings in parallel to
the ongoing standard of care image
interpretation. The user is presented
with notifications for cases with suspected
findings. Notifications include compressed
preview images that are
meant for informational purposes only and
not intended for diagnostic use
beyond notification. The device does not
alter the original medical image and is not
intended to be used as a diagnostic device.
The results of BriefCase-Triage are intended
to be used in conjunction with other
patient information and based on their

BriefCase is a radiological computer aided
triage and notification software indicated for
use in the analysis of abdominal CT images. The
device is intended to assist hospital networks
and trained radiologists in workflow triage by
flagging and communication of suspected
positive findings of Intra-abdominal free gas
(IFG) pathologies.
BriefCase uses an artificial intelligence
algorithm to analyze images and highlight cases
with detected findings on a standalone desktop
application in parallel to the ongoing standard
of care image interpretation. The user is
presented with notifications for cases with
suspected findings. Notifications include
compressed preview images that are meant for
informational purposes only and not intended
for diagnostic use beyond notification. The
device does not alter the original medical
image and is not intended to be used as a
diagnostic device.
The results of BriefCase are intended to be used
in conjunction with other patient information
and based on their professional judgment, to
assist with triage/prioritization of medical


professional judgment, to assist with
triage/prioritization of medical images.
Notified clinicians are responsible for viewing
full images per the standard of care.

Hospital networks and appropriately trained
medical specialists

Notification-o
nly
(/notification
alerts),
parallel
workflow tool

No. No cases are removed from
desktop app or deprioritized


Inclusion/
Exclusion
criteria for
clinical
performance
testing

Inclusion criteria
● CT studies of the abdomen.
● Scans performed on
adults/transitional adults ≥ 18 years
of age.
● Slice thickness 0.6 mm-5 mm.
Exclusion Criteria
● All studies that have an inadequate
field of view

Inclusion criteria
● Scans performed on
adults/transitional adults ≥ 18 years of
age.
● Scans performed on CT scanners
with 64 or greater number of detectors.
● CT studies of the abdomen.
● Slice thickness 0.625 mm-5 mm
axial.
Exclusion Criteria
● All scans that are technically
inadequate, including motion artifacts,
severe metal artifacts, or an inadequate
field of view.

Artificial intelligence algorithm with database
of images.

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

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


Table 3. Frequency Distribution of Gender


Table 4. Frequency Distribution of Manufacturer


Table 5. Frequency Distribution of Slice Thickness


Operating Point
Parameter
N
Estimate
95% Lower CL
95% Upper CL

Table 16. Sensitivity and Specificity with Associated Two-sided 95% Confidence Limits


Predicate Device
Subjected Device
Discussion of Differences

21cm Amorphous Silicon (a-
Si) Flat Panel Detector

Substantially Equivalent.
The change of the image
receptor is to enhance the
device performance.
Both detector technology is
widely used in mobile
fluoroscopic C-arm imaging
system.
Also see comparation with
the cleared reference device
OEC Elite (K172550) that
using the identical CMOS
detectors in Table 2 in the
following section
This change did not raise
any new safety and
effectiveness concerns.

Table 1 Comparison between the Subject Device and Predicate Device


Substantially Equivalent.
Slightly changed for 21cm
Flat Panel Detector (FPD).
The image matrix size is
determined by the physical
dimensions of the detector.
31cm FPD is larger than the
21 cm FPD, therefore it is
different with OEC One
ASD.
This change did not raise
any new safety and
e ffectiveness concerns.

Type: Stationary Anode Focal
Spot (IEC60336):
-Small Focal: 0.6 x 1.4
-Large Focal: 1.4
Housing Heat Capacity:
900,000 HU
Housing Cooling Rate: 12,500
HU/min

Type: Stationary Anode Focal
Spot (IEC60336):
-Small Focal: 0.6
-Large Focal: 1.2
Housing Heat Capacity:
1,200,000 HU
Housing Cooling Rate: 20,000
HU/min

Substantial Equivalent.
The focal spot size, heat
capacity and cooling rate
have been changed to
enhance monoblock
performance.
The system verification and
validation have been
executed and passed.
These changes did not raise
any new safety and
e ffectiveness concern.

40 kHz High Frequency
Max Power 2.5 kW
Peak Tube Potential: 40-110
kVp
Fluoroscopy: 0.1-8.0 mA
High Level Fluoro: 0.2-25.0
mA
Digital Spot:2-10mA
(for 100-120V system)

20 kHz High Frequency
Max Power 4.0 kW
Peak Tube Potential: 40-120
kVp
Fluoroscopy: 0.1-12.0 mA
High Level Fluoro: 0.2-40.0
mA
Digital Spot:2-15mA
(for 100-120V system)

Substantial Equivalent.
The maximum power, kVp
and mA updated to get the
full capability of the tube-
insert to enhance the system
performance.
The frequency of the X-ray
generator is adjusted to fit
the 4kW max power.
These changes are
performance enhancement,
and the X-ray generation

510(k) Premarket Notification Submission – OEC One CFD


control mechanism is not
changed.
The system verification and
validation have been
executed and passed.
These changes did not raise
any new safety and
e ffectiveness concern.

Auto Mode
Manual Mode
(Normal Fluoro, Low Dose
Fluoro, High Level Fluoro)

Continuous – Fluoroscopy
-Normal Dose
-High Level Dose
-Low Dose
Pulsed Fluoroscopy
-Normal Dose
-High Level Dose
-Low Dose
Digital Spot
-Normal Dose
Subtraction
-Normal Dose
-Low Dose
Roadmap
-Normal Dose
-Low Dose

Substantially Equivalent.
Roadmap provides a
modified subtracted image
on the left area of the
monitor showing the
difference between the
current fluoroscopic image
and a roadmap mask image.
It is useful for providing the
anatomical location of
pathology for proper
placement of a catheter,
balloon, or stent.
Roadmap function is the
same as the cleared device
OEC One (K182626).
This change did not raise
any new safety and
effectiveness concerns.

Auto X-Ray technique control
Noise and motion reduction
(TNR)
Auto/Manual Brightness and
Contrast Control
Negate
Swap and auto-swap
Save and auto-save
Last image hold


Edge enhancement
Zoom (Live Zoom) & Roam
Image rotation
Image flip/ invert
Manual/Auto Smart Metal
AutoTrak
Patient Annotation
Markers
Measurement Functions
Peak Opacification
Cine Recording/Playback
· Cine Automatic Image
Playback
· Cine Frame-by-Frame Review
· Fluorostore
Re-Registration
Variable Landmarking
Mask save/Recall
Reference Image Hold
Digital Pen

Iris, Tungsten Dual Leaf
Shutter
Collimator Iris Preview
Collimator Shutter Preview
Squircle shape secondary
collimator

For 21cm detector:
- Line Rate: 74L/cm
- Ratio: 14:1
- Focal Distance: 100 cm
For 31cm detector:
- Line Rate: 60L/cm
- Ratio: 10:1
- Focal Distance: 100 cm

Substantially Equivalent.
Identical for 21cm grid.
Adding 31cm grid due to the
introduction of 31cm flat
panel detector.
This change did not raise
any new safety and
effectiveness concerns.


Predicate Device
Subjected Device
Discussion of Differences

Colored 27” monitor
Resolution: 3840 x 2160
Brightness: 600cd/m2
Touch screen
10 bit image display on
monitor

180 degree swivel
5 degrees tilt up
25 degrees tilt down
Monitor viewable from all 4
sides
Horizontal and Vertical
viewing angle 178 degrees
20cm up vertical travel
20cm down vertical travel
Extension arm rotation 210
degrees
Spring arm rotation 180
degrees
Position monitor above C-arm
by folding articulation arm
within system footprint

Substantially Equivalent.
This change was driven by
IT technology advancement
by using a more state-of-the-
art technology which
achieves the same or better
functionality.
This change did not raise
any new safety and
effectiveness concerns.


Free Space in Arc: 30.7” (78
cm)
Depth of Arc: 26.0” (66 cm)
Source Image Distance: 39.4”
(100 cm)
Lateral Rotation: 410° (+205°
/ -205°)
Wig/Wag: 25° (+12.5°/-12.5°)
Orbital Rotation: 150° (95°
underscan /55° overscan)
Horizontal Movement: 7.9”
(20cm)
Vertical Travel: 17.5” (44 cm)

Free Space in Arc: 30.7” (78
cm)
Depth of Arc: 27.2” (69 cm)
Source Image Distance: 39.4”
(100 cm)
Lateral Rotation: 410° (+205°
/ -205°)
Wig/Wag: 25° (+12.5°/-12.5°)
Orbital Rotation: 180° (95°
underscan /90° overscan)
Horizontal Movement: 7.9”
（20cm）
V ertical Travel: 17.5” (44 cm)

Substantial Equivalent.
The depth of arc and the
orbital rotation angle are
changed to a larger range to
make it easier for the user to
use the system.
These changes did not raise
any new safety and
effectiveness concern.

Green Laser
Flat panel detector side/Tube
side Laser aimer:
- CLASS 2 laser product
- Wavelength: 510nm-
530nm
Optical output power: 1mW

Noise and motion artifact
reduction, Autotrak, ABS
(based on CPU)
Smart window, smart metal,
ADRO (based on GPU)

Noise and motion artifact
reduction, Autotrak, ABS
(based on CPU)
Smart window, smart metal,
A DRO (based on GPU)

Substantially Equivalent.
The added optional OEC
One Touch Tableside


provided identical
function/feature that had
already existed on the tablet
on the system except for the
ability to adjust the
collimator during live X-
rays.
This change did not raise
any new safety and
effectiveness concerns.

Wireless video
- Transmitter
- Receiver
-5.1-5.9 GHz

Substantially Equivalent.
This feature is to introduce
an optional wireless video
module which enables the
user to cast the system
displays wirelessly to the
selected external displays
simultaneously. It functions
in the same way as the
cleared device OEC Elite
(K172550).
This change did not raise
any new safety and
e ffectiveness concerns.

27" monitor
Resolution: 1920X1080
Non-touch Screen

27" monitor
Resolution: 1920X1080
Touch Screen

Substantially Equivalent.
The added touch screen
feature provided identical
function/feature that had
already existed on the
system monitor. It cannot
alter or control X-ray
radiation of the system.
This change did not raise
any new safety and
effectiveness concerns.

External
Video Input
(Picture in
Picture)

Substantially Equivalent.
This feature is to display the
external video input on the
right side of the C-arm
system monitor while the


Predicate Device
Subjected Device
Discussion of Differences

left side of monitor is the
live image from the X-ray
mobile C-arm system. This
feature makes user more
convenient to view the
images from other devices
such as endoscope etc.
It functions in the same way
as the cleared device OEC
Elite (K172550).
This change did not raise
any new safety and
effectiveness concerns.

Substantially Equivalent.
Image Stitching is to create a
composite view from
multiple image acquisitions
to aid in surgical procedures
of the spine, long bone, or
pelvis.
The verification and
validation test to meet
design inputs and user needs
for this feature have been
tested and the result was
passed.
This change did not raise
any new safety and
effectiveness concerns.

Substantially Equivalent.
Trajectory Pointer is a
feature utilizing deep
learning technology to
segment the Kirschner-wire
then utilize a traditional
algorithm to present a visual
guideline to aid in
positioning Kirschner-wires
or similar straight, solid
metallic devices.


The verification and
validation test to meet
design inputs and user needs
for this feature have been
tested and the result was
passed.
This change did not raise
any new safety and
effectiveness concerns.


Modification
Description
Per-Structure Testing Method

The modification adds new
models to K253270, providing a
more comprehensive set of
anatomical contours to support
clinicians in creating contours

Only structures that pass two or more of the following three
tests could be included in the final models:
1. MDA Metric Testing
2. Dice Metric Testing
3. User Beta Testing

The modification adds new CBCT
models to K253270, providing
anatomical contours to support
clinicians in creating contour on
IRIS imaging CBCT data

This modification includes
routine improvements to the
current device by utilizing
higher-quality or more diverse
ground truth training data to
improve overall segmentation
accuracy or improve
performance on subgroups with
challenging anatomy

Only structures that pass two or more of the following three
tests could be included in the final models:
1. MDA Metric Testing
AND
MDA Non-inferiority Testing with Contour ProtégéAI+
2. Dice Metric Testing
AND
Dice Non-inferiority Testing with Contour ProtégéAI+
3. User Beta Testing

This modification includes
routine improvements to the
current device by re-training on
previously cleared
architectures to improve overall
segmentation accuracy or
improve performance on
subgroups with challenging
anatomy and/or decrease
computational time

Table 4 – Traceability of PCCP modifications and per-structure testing protocol


This modification entails
releasing previously released
CT models, that are retested
and renamed to provide
anatomical contours to support
clinicians in creating contours on
IRIS imaging CBCT data


Table 5 – Contour size categorization and associate Dice acceptance criteria threshold


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

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

Lee Bush
Regulatory Affairs Director
GE HealthCare
T:(262)309-9429

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

Common / Usual Name:
Diagnostic Ultrasound System

IYN (primary), IYO, ITX, QIH (secondary)
Ultrasonic Pulsed Doppler Imaging System. 21CFR 892.1550, 90-IYN;
Ultrasonic Pulsed Echo Imaging System, 21CFR 892.1560, 90-IYO;
Diagnostic Ultrasound Transducer, 21 CFR 892.1570, 90-ITX;
Medical Image Management and Processing System, 21 CFR 892.2050,
90-QIH

K231989 LOGIQ E10s, LOGIQ Fortis Diagnostic Ultrasound System

K232381 LOGIQ Totus Diagnostic Ultrasound System
K231301 Vscan Air
K201768 Voluson E10
K220882 Vivid E80, Vivid E90, Vivid E95
K183575 Siemens Acuson S3000/S2000

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


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

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

Information about clinical subgroups
and confounders present in the
dataset

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

Information about equipment and
protocols used to collect images

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

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

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


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

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

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


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

Validation images were collected on LOGIQ Fortis with the C1-6
probe with a standardized protocol.

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

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

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

510(k) Premarket Notification Submission


95% CI of (-6.17, 5.79).
Porta Hepatis measurement accuracy with segmentation scroll
edit:
• Average accuracy is 80.56% with a 95% CI of +/- 8.83% and
an average absolute error 0.91 mm and 95% CI of 0.45
mm.
• Limits of Agreement in millimeters are (-1.96, 3.25) with
95% CI of (-2.85,4.14).

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

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

Information about clinical subgroups and

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

Information about equipment and
protocols used to collect images

• Before the process of data annotation, all information

displayed on the device is removed and performed on

information extracted purely from Ultrasound B-mode

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

Porta Hepatis – Number of keystrokes measured.

Porta Hepatis using AI - Number of keystrokes measured.

• Number of Keystrokes with and without AI is compared for

• Arbitrator to select most accurate measurement among all

• Arbitrator selected measurement is compared to AI

baseline measurement with and without on segmentation

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

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


Indications for Use
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textbox will be left blank for original applications/submissions.
Please provide the device trade name(s). ?

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

Please provide your Indications for Use below.

LOGIQ Totus is intended for use by a qualified physician 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, Musculo-skeletal Conventional
and Superficial; Urology(including Prostate); Transrectal; Transvaginal; Transesophageal and
lntraoperative(Abdominal and Vascular).
Modes of operation includes: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power
Doppler, Harmonic Imaging, Coded Pulse, 30/40 Imaging mode, Elastography, Shear Wave Elastography,
Attenuation Imaging and Combined modes: B/M, B/Color, B/PWD, B/Color/PWD, B/Power/PWD.
The LOGIQ Totus is intended to be used in a hospital or medical clinic.

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


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

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

Lee Bush
Regulatory Affairs Director
GE HealthCare
T:(262)309-9429

Chae-Rin, Song
Regulatory Affairs Leader
GE Ultrasound Korea, Ltd.
T: +82-31-740-6310

Common / Usual Name:
Diagnostic Ultrasound System

IYN (primary), IYO, ITX, QIH (secondary)
Ultrasonic Pulsed Doppler Imaging System. 21CFR 892.1550,
90-IYN;
Ultrasonic Pulsed Echo Imaging System, 21CFR 892.1560, 90-
IYO;
Diagnostic Ultrasound Transducer, 21 CFR 892.1570, 90-ITX;
Medical Image Management and Processing System, 21 CFR
892.2050, 90-QIH

K232381 LOGIQ Totus Diagnostic Ultrasound System

K231989 LOGIQ E10s Diagnostic Ultrasound System
K231301 Vscan Air
K201768 Voluson E10
K220882 Vivid E80, Vivid E90, Vivid E95
K183575 Siemens Acuson S3000/S2000


Legal Manufacturer
Perspectum Ltd.
Perspectum Ltd.

Device Trade Name
LiverMultiScan (v6.0)
LiverMultiScan (v5.0)


Attributes
Subject Device
Predicate Device
Comments

Device trade name
LiverMultiScan v6.0
LiverMultiScan v5.0

Manufacturer
Perspectum Ltd
Perspectum Ltd

The device is indicated for
use as a magnetic
resonance diagnostic
device software
application for non-
invasive assessment of
liver health and the
generation, display and
review of magnetic
resonance medical image
data.
The device produces
quantified metrics and
composite images from
magnetic resonance
medical image data
which, when interpreted

LiverMultiScan v5 (LMSv5)
is indicated for use as a
magnetic resonance
diagnostic device software
application for non-invasive
liver evaluation that
enables the generation,
display and review of 2D
magnetic resonance
medical image data and
pixel maps for MR
relaxation times.
LMSv5 is designed to
utilize DICOM 3.0
compliant magnetic
resonance image datasets,
acquired from compatible

Rephrased the
indications for use for
better readability without
any change to its intent.

demonstrate substantial equivalence. Differences between the devices are commented.


by a trained healthcare
professional, yield
information on liver tissue
characteristics that
may assist in clinical
d ecisions.

MR Systems, to display the
internal structure of the
abdomen including the
liver. Other physical
parameters derived from
the images may also be
produced.
LMSv5 provides several
tools, such as automated
liver segmentation and
region of interest (ROI)
placements, to be used for
the assessment of selected
regions of an image.
Quantitative assessment of
selected regions includes
the determination of
triglyceride fat fraction in
the liver (PDFF), T2*, LIC
(Liver Iron Concentration)
and iron corrected T1 (cT1)
measurements.
These images and the
physical parameters
derived from the images,
when interpreted by a
trained clinician, yield
information that may assist
i n diagnosis.

• Perspectum
trained analysts use
the device
to analyse MRI data
and produce output.
• Trained healthcare
professionals interpret
the device
output for their clinical
decision pathways.

Liver
Iron Corrected T1 (cT1)
Proton Density Fat
Fraction (PDFF)
Liver Iron Concentration
(LIC)

No change to the list of
measurement output.
However, the equation
used to calculate the LIC
value has changed in the
subject device to align
with the reference
article. And, the cT1
calculation in the subject
device is updated to
account for T1 signal
variations due to elevated
fat in addition to what


already existed in the
predicate device.

Target population
General population
General population
No change

Contraindications
None, Software only
None, Software only
No change

DICOM 3.0 compliant MR
image datasets from
compatible MR scanners

MRI Scanners
GE, Siemens and Philips
GE, Siemens and Philips
No change

MR field strength
1.5T and 3T
1.5T and 3T
No change

Subject device is
modified to read MR scan
data acquired through
NOLLI (non-MOLLI) in
additional to MOLLI
method.


is at least as safe and effective as the predicate device.


Swoop Portable MR Imaging
Imaging System
Imaging System

The Swoop Portable MR Imaging
System is a portable, ultra-low
field magnetic resonance imaging
device for producing images that
display the internal structure of
the head where full diagnostic
examination is not clinically
practical. When interpreted by a
trained physician, these images
provide information that can be
useful in determining a diagnosis.

Adult and pediatric patients (≥ 0
years)

At the point of care in professional
health care facilities such as
emergency rooms,
intensive/critical care units,
hospitals, outpatient, or
rehabilitation centers.

The table below compares the subject device to the predicate.


Model 1 Swoop System
63.3 ± 2.0 mT
Model 2 Swoop System
64.9 mT (nominal)

Model 1 Swoop System
24.0 in. width, 12.4 in. height
Model 2 Swoop System
36.0 in. width, 13.4 in. height

Model 1 Swoop System
705 lbs
Model 2 Swoop System
712 lbs

Model 1 Swoop System
X: 24 mT/m, Y: 23 mT/m, Z: 39
mT/m
Model 2 Swoop System
X: 33.9 mT/m, Y: 33.2 mT/m, Z:
66.2 mT/m

Model 1 Swoop System
X: 2.1 ms, Y: 2.0 ms, Z: 3.8 ms
Model 2 Swoop System
X: 1.8 ms, Y: 1.8 ms, Z: 5.1 ms

Model 1 Swoop System
X: 24 T/m/s, Y: 22 T/m/s, Z: 21
T/m/s
Model 2 Swoop System
X: 18.8 T/m/s, Y: 18.4 T/m/s, Z:
13.0 T/m/s

Patient Weight Capacity
1.6kg-200 kg
Same
Same


• T1 (Standard), T1
(Gray/White)
• Advanced Gridding
reconstruction

• T2, T2 (Fast)
• Advanced Gridding
reconstruction

Model 1 Swoop System
• FLAIR
• Advanced Gridding
reconstruction
Model 2 Swoop System
• FLAIR, FLAIR (Fast)
• Advanced Gridding
reconstruction

Model 1 Swoop System
• Single Direction DWI/ADC
• Multi-direction DWI/ADC
• Advanced Gridding + FISTA
Model 2 Swoop System
• Single Direction DWI/ADC,
• Multi-direction DWI/ADC
• Advanced Gridding + FISTA

• Single Direction
DWI/ADC, Single
Direction DWI/ADC
(Fast)
• Advanced Gridding +
FISTA

• Single Direction
DWI/ADC
• Advanced Gridding +
FISTA

• Advanced Denoising
• Image orientation transform
• Geometric distortion
correction
• Receive coil intensity
correction
• Advanced Interpolation
• ADC/Trace output (DWI)
• DICOM output

• Advanced Denoising
• Image orientation
transform
• Geometric distortion
correction
• Receive coil intensity
correction
• Advanced
Interpolation
• ADC output (DWI)
• 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 62304:2016
• FDA Guidance, “Content of Premarket
Submissions for Device Software
Functions”

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
standards for named sequences

Testing to verify cybersecurity controls and
management.

• FDA Guidance, “Cybersecurity in
Medical Devices: Quality System
Considerations and Content of
Premarket Submissions”

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

requirements and applicable standards to support substantial equivalence.


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.

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


Gender:
Female Male Unknown
13% 87% 0%
Age:
0-2 2-18 18-35 35-60 60+ Unknown
0% 0% 25% 0% 75% 0%
Ethnicity data not recorded.
Number of sites: 6
Equipment:
Model 1 Swoop System
Model 2 Swoop System
(V1.9)
13% 87%
Included pathology: Post-resection Tumor, ICH, Stroke, TBI, Post-
Craniotomy.


Gender:
Female Male Unknown
25% 42% 33%
Age:
0-2 2-18 18-35 35-60 60+ Unknown
0% 0% 8% 42% 33% 17%
Ethnicity data not recorded.
Number of sites: 5
Equipment type:
Model 1 Swoop System Model 1 Swoop System
Model 2 Swoop System
(V1.8) (V1.9)
22% 22% 56%
Included pathology: Acute/Subacute Stroke Stroke, Tumor, Post-operative Tumor, Cerebellar
metastatic disease, white matter disease, Acute/Subacute Infarct


Gender:
Female Male Unknown
41% 35% 24%
Age:
0-2 2-18 18-35 35-60 60+ unknown*
0% 0% 15% 26% 32% 26%
*anonymized
Ethnicity data not recorded.
Number of sites: 8
Equipment type:
Model 1 Swoop System Model 1 Swoop System Model 2 Swoop System
(V1.8) (V1.9)
20% 10% 70%
Included pathology: Acute Stroke, Subacute Stroke, Multiple Sclerosis, White matter disease,
Metastatic disease, Post-operative glioma, Tumor, Hydrocephalus, ICH, IVH,

DWI (trace-weighted or single direction), and ADC.


Specification
Predicate Device
Proposed Device

Revolution Apex (K213715)
Photonova Spectra

Patient Population
Patients of all ages
Same

• 80 cm patient bore
• Rotation Speeds: 0.23, 0.28, 0.35,
0.5, 0.6, 0.7, 0.8, 0.9, and 1.0
seconds per rotation

• 80 cm patient bore
• Rotation Speeds: 0.23, 0.28, 0.35,
0.5, 0.6, 0.7, 0.8, 0.9, 1.0 & 2.0
seconds per rotation

• Up to 160 mm in Z-direction with
up to 50cm Scan field of view

• Up to 80 mm in Z-direction with up
to 50 cm scan field of view


• 256 rows, 0.625 mm pixel pitch
• Energy Integrating
• Gemstone Scintillator Material
• Detector Thermal System (DTS)
with high RPM fans

(40 mm option)
• 192 rows, 0.2 mm pixel pitch in XY
and 0.4 mm pixel pitch in Z
• Photon Counting with 8 discrete
energy bins
• Silicon Semiconductor Material
• Detector Thermal System (DTS) with
higher RPM fans

Quantix X-Ray Tube
• 70, 80, 100, 120, 140 kVp
• Focal Spot sizes: Extra Large,
Large, Small

Quantix X-Ray Tube
• 120 kVp
• Focal Spot sizes: Extra Large, Large,
Small, Extra Small

Wolverine 2 Collimator
• Diagnostic Bowties: Small,
Medium, Large
• Scout: SmartScout Filter
• Calibration: No dedicated filter.

Badger Collimator
• Diagnostic Bowties: Small, Large
• Scout: SmartScout Filter
• Calibration: Tungsten Filter

• Tables with scannable range of
2000 mm & 1700mm
• Load capacity 675 lbs

• Scout
• Axial
• Helical
• Cine
• Cardiac
• Gated
• High Definition
• Fluoro (axial)
• GSI
• SmartScout
• ECG-Less cardiac (K233750)

• Scout
• Axial
• Helical
• Cardiac
• Gated

510(k) Premarket Notification Submission – Photonova Spectra


Indications for Use
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1<253532 ?
textbox will be left blank for original applications/submissions.
Please provide the device trade name(s). ?

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textbox will be left blank for original applications/submissions.

Please provide your Indications for Use below.

TruSPECT is intended for acceptance, transfer, display, storage, and processing of images for detection of
radioisotope tracer uptakes in the patient's body. The device using various processing modes supported by
the various clinical applications and various features designed to enhance image quality. The emission
computerized tomography data can be coupled with registered and/or fused CT/MR scans and with
physiological signals in order to depict, localize, and/or quantify the distribution of radionuclide tracers and
anatomical structures in scanned body tissue for clinical diagnostic purposes. The acquired tomographic
image may undergo emission-based attenuation correction.
Visualization tools include segmentation, colour coding, and polar maps. Analysis tools include Quantitative
Perfusion SPECT (QPS), Quantitative Gated SPECT (QGS) and Quantitative Blood Pool Gated SPECT
(QBS) measurements, Multi Gated Acquisition (MUGA) and Heart-to-Mediastinum activity ratio (H/M).
The system also includes reporting tools for formatting findings and user selected areas of interest. It is
capable of processing and displaying the acquired information in traditional formats, as well as in three-
dimensional renderings, and in various forms of animated sequences, showing kinetic attributes of the
imaged organs.
TruSPECT is based on Windows operating system. Due to special customer requirements and the clinical
focus the TruSPECT can be configured with different combinations of Windows OS based software options
and clinical applications which are intended to assist the physician in diagnosis and/or treatment planning.
This includes commercially available post-processing software packages.
TruSPECT is a processing workstation primarily intended for, but not limited to cardiac applications. The
workstation can be integrated with the D-SPECT cardiac scanner system or used as a standalone post-
processing station.

Please select the types of uses (select one or both, as [gJ Prescription Use (21 CFR 801 Subpart 0)
D
applicable). Over-The-Counter Use (21 CFR 801 Subpart C)


Characteristic
Predicate device
Proposed device

Manual and automated processes, pre-
trained neural networks in iterative
reconstruction, traditional algorithms

Non-adaptive machine learning algorithms
trained with clinical data

Preinstalled on Spectrum Dynamics
workstation or loaded onto customer’s
workstation meeting specifications


Clinical Validation Acceptance Criteria (Prespecified)


DHT8B: Division of Radiological Imaging Devices and

Office of Product Evaluation and Quality


Applicant Address
Loughbeg Ringaskiddy Ireland

Applicant Contact Telephone
+1 574-404-8348

Correspondent Address
Loughbeg Ringaskiddy Ireland

Correspondent Contact Telephone
+1 267-889-5354

Correspondent Contact
Ms. Anuradha Moholkar

Medical image management and processing system

Classification Name
System, Image Processing, Radiological


Medical Specialty
Radiology
Radiology
Identical

21 CFR 892.2050 Picture archiving and
communications system

LLZ System, image processing,
radiological
HAW Neurological stereotaxic instrument

QIH Automated radiological image
processing software
LLZ System, image processing,
radiological

Equivalent. Both devices use
anatomic landmarks identified from
radiology images to assist in
positioning total hip replacement
components intraoperatively, and to
support preoperative planning in
total hip and total knee procedures

JointPoint is a non-invasive software
(Software as a Medical Device) intended
to provide preoperative templating for
orthopedic procedures and intraoperative
data for total hip arthroplasties (THA).

VELYS™ Hip Navigation is a non-
invasive software (Software as a Medical
Device) intended to provide preoperative
templating for orthopedic procedures and
intraoperative data for total hip
arthroplasties (THA).

device support that there are no new questions of safety or effectiveness.


JointPoint is an image-processing
software indicated to assist in the
positioning of total hip replacement
components. It is intended to assist in
precisely positioning total hip
replacement components intra-
operatively by measuring their positions
relative to the bone structures of interest
provided that the points of interest can
be identified from radiology images.
JointPoint is also indicated for assisting
healthcare professionals in preoperative
planning and postoperative analysis of
orthopedic surgery in Total Hip
Replacement, Total Knee Replacement,
and Intertrochanteric Fracture
Reduction. The device allows for
overlaying of prosthesis templates on
radiological images and includes tools
for performing measurements on the
image and for positioning the template.
Clinical judgment and experience are
required to properly use the software.
The software is not for primary image
interpretation. The software is not for
use on mobile phones.

VELYS™ Hip Navigation is an image-
processing software indicated to assist in
the positioning of total hip replacement
components. It is intended to assist in
precisely positioning total hip replacement
components intra-operatively by
measuring their positions relative to the
bone structures of interest provided that
the points of interest can be identified
from radiology images.
VELYS Hip Navigation is also indicated
for assisting healthcare professionals in
preoperative planning and postoperative
analysis of orthopedic surgery in Total
Hip Replacement and Total Knee
Replacement. The device allows for
overlaying of prosthesis templates on
radiological images and includes tools for
performing measurements on the image
and for positioning the template. Clinical
judgment and experience are required to
properly use the software. The software is
not for primary image interpretation. The
software is not for use on mobile phones.

Identical. The indications for use
of the subject device fall within the
intended use of the predicate
device.

Patients who are candidates for
orthopedic procedures and total hip
replacement

Patients who are candidates for total hip
replacement and total knee replacement

Identical. The target population of
the subject device is within that of
the predicate device. There are no
new questions of safety or
efficacy.


Preoperative and intraoperative
radiographic patient images of the pelvis

Preoperative templating:
• Image import
• Image calibration
• User selected templates of
implants for THA or TKA
• Digital annotations
• Leg length analysis tool (THA)
• Manual landmark selections

• User places anatomical landmarks
on patient images using digital
tools (manual landmark selection)
• Surgeon confirms placement
of landmarks

• When enabled, an ML model
determines the default landmark
position. The user confirms and/or
adjusts the landmark position using
digital tools. This feature is available
in Windows Version only.
• Manual landmark selection is
available as an option
• Surgeon confirms placement of
landmarks

Equivalent. With the subject device,
when enabled, the default landmark
location is determined by an ML
model. In both the subject device and
the predicate device, the user can
move the landmark selections using
digital tools, and the surgeon must
confirm placement of the landmarks.
Performance testing did not raise new
issues of safety or efficacy.

• Software provides leg length and
offset data for selected implant
constructs and alternatives
• Software provides inclination and
anteversion of the trial or final
acetabular implant
• Manual landmark selection or
AI assisted landmarking


• Contralateral overlay allows an
anterior-posterior (AP) image of
the non-operative side to be
compared to the operative side.
• OneTrial® Manual
landmark selection

Identical. With the subject device,
the AI assisted landmark placement
is only available in the Windows
version. The iOS version of the
subject device is identical to the
predicate device.


• mAP@0.75: Indicates precision at a higher threshold of localization accuracy.


Model
uMI Panvivo
uMI Panvivo S
uMI Panvivo ES
uMI Panvivo EX
uMI Panvivo
uMI Panvivo S

Detector
Scintillator material
LYSO
LYSO
LYSO
LYSO
LYSO
LYSO
Same

Axial field of view
295 mm
235 mm
534mm
712mm
295 mm
235 mm


Contrast
Recovery
10 mm Sphere
＞45.0%
13 mm Sphere
＞55.0%
17 mm Sphere
＞65.0%


Shanghai United Imaging Healthcare Co., Ltd.

22 mm Sphere
22 mm Sphere
22 mm Sphere
22 mm Sphere
22 mm Sphere
22 mm Sphe

28 mm Sphere
28 mm Sphere
28 mm Sphere
28 mm Sphere
28 mm Sphere
28 mm Sphe

37 mm Sphere
37 mm Sphere
37 mm Sphere
37 mm Sphere
37 mm Sphere
37 mm Sphe

Lung Residual
Lung Residual
Lung Residual
Lung Residual
Lung Residual
Lung Resid

error ＜8.0%
error ＜8.0%
error ＜8.0%
error ＜8.0%
error ＜8.0%
error ＜8.0%

Background Background Background Background Background Background

variability variability variability variability variability variability

10mm Sphere
10mm Sphere
10mm Sphere
＜
10mm Sphere
＜
10mm Sphere
10mm Sphe

13mm Sphere
13mm Sphere
13mm Sphere
＜
13mm Sphere
＜
13mm Sphere
13mm Sphe

17mm Sphere
17mm Sphere
17mm Sphere
＜
17mm Sphere
＜
17mm Sphere
17mm Sphe

22mm Sphere
22mm Sphere
22mm Sphere
＜
22mm Sphere
＜
22mm Sphere
22mm Sphe

28mm Sphere
28mm Sphere
28mm Sphere
＜
28mm Sphere
＜
28mm Sphere
28mm Sphe

37mm Sphere
37mm Sphere
37mm Sphere
＜
37mm Sphere
＜
37mm Sphere
37mm Sphe


Time-
of-flight Resolution
＜ 245 ps
＜ 245 ps
＜ 245 ps
＜ 245 ps
＜ 245 ps
＜ 245 ps
Same

PET-C
T Coregistration Accuracy
＜ 3.0 mm
＜ 3.0 mm
＜ 3.0 mm
＜ 3.0 mm
＜ 3.0 mm
＜ 3.0 mm
Same

Digital Gating Yes Yes Yes Yes Yes Yes Same

Neuro Focus.Brain No No Yes Yes No No Note

HYPE
R Iterative
Yes
Yes
Yes
Yes
Yes
Yes
Same

Intellig
ent Assistant
No
No
Yes
Yes
No
No
Note8


The number of detector rings and the axial Field of View (aFOV) of the poposed devices are
larger than that of the predicate devices. A longer aFOV can increase the scanning range per bed
position, thereby reducing the number of bed positions required for a whole-body scan and
shortening the total scanning time.
The differences do not affect the clinical effectiveness and safety.

For systems with a longer aFOV, the coincidence timing window was increased based on
simulation analyses to accommodate the detection of more LORs (Line of Response). This
adjustment is moderate and well-controlled, and does not affect the system's Image Quality (IQ)
performance.
The difference does not affect the clinical effectiveness and safety.

The Sensitivities of the proposed devices are larger than that of the predicate devices. Improved
system sensitivity enables the acquisition of more counts under identical scan duration and
radiotracer activity, which in turn leads to better image quality.
The difference does not raise new safety and effectiveness concerns.

The proposed devices provide larger NECR peak value and Peak True Count Rate to the Predicate
devices. The higher NECR Peak Value and Peak True Count Rate will let system acquire more
effective data even in high activity concentration.
The difference does not raise new safety and effectiveness concerns.

Following system tests that confirmed the table's ability to support a 280 kg load, the maximum
load specification has been increased. This revision is justified, as the original specifications
included an excessive safety margin, with no changes to the bed's design.
The difference does not raise new safety and effectiveness concerns.

NeuroFocus.Brain is a brain-artifact elimination solution that employs a statistics-based motion
detection method to automatically select an optimal motion-free subset of counts for
reconstruction.
The long-axial PET systems (uMI Panvivo EX and uMI Panvivo ES), unlike the previously
cleared short-axial uMI Panvivo and uMI Panvivo S, provide inherently higher native sensitivity
that compensates for the discarded counts. The exclusive use of the optimal subset improves
reconstruction efficiency.
Performance and clinical image evaluation were conducted on the proposed device. It is shown
that the difference did not raise new safety and effectiveness concerns.

The uExcel DPR implemented on the uMI Panvivo EX and uMI Panvivo ES systems is
algorithmically identical to the previously cleared uExcel DPR feature on the uMI Panvivo and
uMI Panvivo S systems. The sole distinction lies in the declared intended use, uMI Panvivo ES
and uMI Panvivo EX only support FDG (¹⁸F-FDG) while uMI Panvivo and uMI Panvivo S
supports several imaging agents.
Performance and clinical image evaluation were conducted on the proposed device. It is shown
that the difference did not raise new safety and effectiveness concerns.

The Intelligent Assistant is a local auxiliary query tool that responds to PETCT operation-related
questions based on validated knowledge (including user manuals and common issues). It does not
control or modify the system's hardware, core software, scanning parameters, or reconstruction
processes, nor does it alter the original operational workflow.
The addition of this feature does not raise new safety and effectiveness concerns.


Table 2 Distribution of volunteer dataset


Evaluation Item
Evaluation Method
Criteria
Results

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

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

Table 3 The performance evaluation report criteria of DeepMAC


Table 4 The demographic distribution of human subjects


Contrast recovery (CR),
background variability
(BV), and contrast-to-noise
ratio (CNR) were
calculated using NEMA IQ
phantom data reconstructed
with uExcel DPR and
OSEM under acquisition
conditions of 1 to 5
minutes per bed.

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

Table 5 The performance evaluation report criteria of uExcel DPR


Table 6 Distribution of volunteer dataset


Volume relative to no
respiratory motion
correction (∆Volume).

Calculating the OncoFocus volume
change relative to no respiratory
motion correction images

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

Calculating the SUVmax obtained
from the OncoFocus with that from
the corresponding non-corrected
image

Table 7 The performance evaluation report criteria of OncoFocus


Table 8 Distribution of volunteer dataset


Calculate ∆SUVmean in the high-
uptake region for two MCS cases:
one with motion introduced during
simulation and reconstructed
using NeuroFocus.Brain, and one
stationary reconstructed without
NeuroFocus.Brain.

Calculate ∆SUVmean in the high-
uptake region of the prefrontal
cortex, relative to reconstruction
without NeuroFocus.Brain for the
same clinical scan with head
motion.

Table 9 The performance evaluation report criteria of NeuroFocus.Brain


For phantom study, the water
phantom outside of CT scan-
FOV was tested to compare the
AIEFOV algorithm with EFOV
algorithm.
For patient study, the SUV of
some ROIs in PET image with
attenuation correction
performed with CT generated
with EFOV and AIEFOV
algorithm will be compared.

Compared to the ground truth, the
uniformity and SUV deviation of
PET image obtained by using
AIEFOV for attenuation
correction should be less than
5%.
And when the scanned object
does not exceed the CT field of
view, attenuation correction using
CT generated either with
AIEFOV or EFOV should result
in consistent PET image SUV.

Table 11 The performance evaluation report criteria of AIEFOV


This feature is modified from the predicate devices

This feature is currently supported by other cleared Siemens CT systems or
cleared Siemens stand-alone software applications. This feature will be
supported for the subject device with software version SOMARIS/10 syngo CT
VB20 and is substantially equivalent compared to the cleared version of the
predicate devices.


SOMATOM X.
Platform
with syngo CT
VB10
(K233650)

Interfaces for
respiratory gating
systems – Open
Online Interface

FAST 3D Camera/
FAST Integrated
Workflow

6.
DirectSetup Notes
SW
enabled
n.a.
cleared

(SOMATOM X. Platform) with software version SOMARIS/10 syngo CT VB20 compared to the predicate devices.


myExam
Companion –
myExam
Compass/myExam
Cockpit

CT guided
intervention –
myAblation Guide
interface

modified
(same as in
K233650, with
exception of the
modifications
regarding scan
range and cycle
time which are


Subject device
SOMATOM X. Platform
with SOMARIS/10 syngo CT
VB20

Primary predicate device
SOMATOM X. Platform s
with SOMARIS/10 syngo CT
VB10
(K233650)
SOMATOM SOMATOM
X.cite X.ceed

Whole body Computed
Tomography (CT) Scanner
System

Whole body Computed S
Tomography (CT) Scanner
System

with software version syngo CT VB20 and the primary predicate devices with software version syngo CT VB10 (K233650).


Primary predicate device As
SOMATOM X. Platform su
eq
with SOMARIS/10 syngo CT
VB10
(K233650)
SOMATOM SOMATOM
X.cite X.ceed

br
th
in
co
pa
ga
of
In
O
in
O
in
fo
pr
SO
Pl
CT

eathing signal of
e patient,
terfaces for
nnecting of 3rd
rty respiratory
ting devices are
fered.
troduction of new
pen Online
terface.
pen Online
terface is cleared
r the secondary
edicate devices
MATOM go.
atform with syngo
VB20 in K250822.


Assessment of
the substantial
equivalency
(SE)

SOMATOM X.cite
SOMATOM X.ceed
with SOMARIS/10
syngo CT VB20

Windows based
SOMARIS/10 syngo
CT VB20
Note: the short
version syngo CT
VB20 is also used as
labeling information

Windows based
SOMARIS/10 syngo
CT VB10
Note: the short
version syngo CT
VB10 is also used as
labeling information

Software
version
upgraded due
to new
functionalities.
Same as in the
secondary
predicate
device


respiratory gating
system
• Protocol
supporting
contrast bolus-
triggered data
acquisition
• Contrast media
protocols
(including
coronary CTA)
• Pediatric
Protocols
• Flex Dose Profile
• Turbo Flash Spiral
• Dual Energy
acquisition
(TwinBeam DE
and TwinSpiral
DE)
• Dynamic imaging
(Flex 4D Spiral)
• Protocols
supporting CT
Intervention (scan
modes: 2D i-
sequence, 3D i-
sequence, i-spiral,
i-Fluoro)
• Protocols
supporting
Cardiac Scanning
• Protocols for
DirectBreathhold

Recon&GO:
- Spectral Recon
(Dual Energy
Reconstruction
including Virtual
Unenhanced,
Monoenergetic plus

Same as the
primary and
secondary
predicate
devices


SOMATOM go.
Platform
with SOMARIS/10
syngo CT VB20
(K250822)

- Inline Results DE
SPP (Spectral Post-
Processing with
photon-counting
image data)
- Inline Anatomical
ranges
(Parallel/Radial) incl.
Virtual Unenhanced,
Monoenergetic plus
- Inline Spine and Rib
Ranges
- Inline table and
bone removal

CT View&GO offers:
- basic post-
processing viewer (CT
View&GO)
- 2D and 3D (MPR,
VRT, MIP and minIP)
- Evaluation tools,
Filming, Printing
- Interactive Spectral
Imaging (ISI)
- Basic visualization
tools: Endo View
- Basic manipulation
tools: DE ROI, ROI
HU, Average

• Recon&GO Inline
Results
Software interface to
post-processing
algorithms which are
unmodified when
loaded onto the CT
scanners and 510(k)
cleared as medical

Same as the
secondary
predicate
devices
In software
version syngo
CT VB20, the
subject devices


Same as the
secondary
predicate
devices
In addition to
HD FoV 4.0, the
subject devices
SOMATOM X.
Platform with
syngo CT VB20
support a new
generation of
extended field
of view
reconstruction
algorithm: HD
FoV 5.0.

FAST technologies
CARE technologies
GO technologies

256 x 256 pixels
512 x 512 pixels
768 x 768 pixels
1024 x 1024 pixels
(Precision Matrix)


relative mass density, relative mass density relative mass density, predicate


Feature/Non-clinical
Bench Testing performed

FAST 3D Camera/
FAST Integrated
Workflow

The FAST 3D camera sub-features FAST Isocentering, FAST Range, and
FAST Direction have been optimized using additional data from adults
and adolescence patients. The bench test evaluates and compares the
accuracy of the three sub-features in software version syngo CT VB20 to
the accuracy of the predicate devices with syngo CT VB10.
The objectives of the bench tests are to demonstrate that the FAST 3D
camera achieves comparable or better results for both, adults and
adolescents, as the predicate device for adults.
Overall, the subject devices with syngo CT VB20 delivers comparable or
improved accuracy to the predicate devices with syngo CT VB10
predicate device for adults and extends the support to adolescents.

The purpose of the test is to provide a clear reporting on the applied
algorithm, its product development, validation, and verification on
patient data, which enable the claims.
Objective of the test is to assess the fraction (percentage) of ranges
calculated by the FAST Planning algorithm that are correct and can be
applied without change. Additionally, calculation time was measured to
check whether it meets interactive requirements.
The test results show that the editing actions for the scanner technician
can be reduced to a minimum and that the calculation time is fast
enough for interactive speed during scanning. For more than 90% of the
ranges no editing action was necessary to cover standard ranges. For
more than 95%, the speed of the algorithm was sufficient.

The bench test contains a detailed description and evaluation of the new
HD FoV 5. 0 algorithm for extended field of view reconstruction. Results

Table 6: Non-clinical performance testing (bench testing).