Predicate Devices

Reference Devices

BrainInsight (K202414)

AI/ML (Artificial Intelligence / Machine Learning)

Yes
The document explicitly mentions "Mentions AI, DNN, or ML: Yes" and describes a training set used for a "brain structural segmentation model," which is a common application of machine learning in medical imaging.

Therapeutic

No.
The device is described as an image post-processing software that provides automatic labeling, visualization, and volumetric quantification of brain structures from MR images and returns segmented images and morphometric reports. It is intended as a support tool for clinicians in assessment of structural MRIs, not for direct treatment or therapy.

Diagnostic

Yes

The device provides "volumetric quantification of segmentable brain structures" and compares "Volumetric data... to reference percentile data." This information, along with the output being presented in "morphometric reports" for use in "routine patient care as a support tool for clinicians in assessment of structural MRIs", indicates its role in aiding diagnosis.

SaMD (Software as a Medical Device)

Yes

The device description explicitly states "Neurophet AQUA is a fully automated MR imaging post-processing medical device software". It processes existing MR images and provides reports and visualizations, without including any hardware components.

IVD (In Vitro Diagnostic)

Based on the provided information, this device is not an In Vitro Diagnostic (IVD).

Here's why:

IVD Definition: In Vitro Diagnostics are devices intended for use in the examination of specimens derived from the human body in order to provide information for diagnostic purposes. This typically involves analyzing biological samples like blood, urine, tissue, etc.
Device Function: Neurophet AQUA processes MR images of the brain. It performs image analysis, segmentation, and volumetric quantification. It does not analyze biological specimens.
Intended Use: The intended use is for "Automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images." This is clearly focused on analyzing medical images, not biological samples.

While the device provides information that can be used to support clinical assessment and diagnosis, the mechanism by which it obtains this information (processing medical images) falls outside the scope of an IVD. It is a medical device software that performs image analysis.

PCCP (Predetermined Change Control Plan) Authorized

No
The input text does not contain any explicit statement that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device.

Overview

Intended Use / Indications for Use

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

Product codes

LLZ

Device Description

Neurophet AQUA is a fully automated MR imaging post-processing medical device software that provides automatic labeling, visualization, and volumetric quantification of brain structures from a set of MR images and returns segmented images and morphometric reports. The resulting output is provided in morphometric reports that can be displayed on Picture Archive and Communications Systems (PACS). The high throughput capability makes the software suitable for use in both clinical trial research and routine patient care as a support tool for clinicians in assessment of structural MRIs.

Neurophet AQUA provides morphometric measurements based on T1 MRI series. The output of the software includes volumes that have been annotated with color overlays, with each color representing a particular segmented region, and morphometric reports that provide comparison of measured volumes to age and gender-matched reference percentile data. Neurophet AQUA processing architecture includes a proprietary automated internal pipeline that performs segmentation, volume calculation and report generation.

The results are displayed in a dedicated graphical user interface, allowing the user to:

Browse the segmentations and the measures, .
Compare the results of segmented brain structures to a reference healthy population,
Read and print a PDF report

Additionally, automated safety measures include automated quality control functions, such as tissue contrast check, scan protocol verification, which validate that the imaging protocols adhere to system requirements.

Mentions image processing

Yes

Mentions AI, DNN, or ML

Although both are technically similar, in the processing architecture, the subject device performs segmentation based on deep learning and the predicate device performs segmentation based on atlas-based.

We identified a reference device (BrainInsight, K202414) which also uses a fully automated MR imaging post-processing medical software that image alignment, whole brain segmentation, ventricle segmentation, and midline shift measurements based on machine learning tools.

Input Imaging Modality

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

Anatomical Site

Brain

Indicated Patient Age Range

Not Found

Intended User / Care Setting

Not Found

Description of the training set, sample size, data source, and annotation protocol

As part of AQUA's training data. 300 T1-weighted MRI scans collected from ten different MRI scanner types were used to train for the brain structural segmentation model.

MRI scanners with 30 scans each contain public datasets, including ADNI, IXI, PPMI, HCP, and AIBL. Ground-truth data were initially generated using FreeSurfer (General Hospital Corporation, Boston, MA, USA, version 6.0) and verified and corrected by four radiologists.

Description of the test set, sample size, data source, and annotation protocol

A total of 64 T1 scans (56%, n=36 US-based data; 62.5%, n=40 females; age ranges 20-90) were used for accuracy and 50 repeated T1 scans (62%, n=31 US-based data; 46%, n=23 females; age ranges 10-90) were used for reproducibility. Both sets include cognitive normal, mild cognitive impairments, and Alzheimer's disease patients from MR scanners of three main vendors (Siemens, Phillips, and GE). The data set met the imaging protocol requirements described in the User Manual. Stratified results across race/ethnicity, age, gender, pathology, scanner, vendor, and magnetic field strength were provided. All the testing data was exclusive from the training dataset.

Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)

SW verification/validation and the measurement accuracy test were conducted to establish the performance, functionality and reliability characteristics of the subject devices. The device passed all of the tests based on pre-determined Pass/Fail criteria.

Neurophet AQUA performance was evaluated by comparing segmentation accuracy with expert manual segmentations and by measuring segmentation reproducibility between same subject scans. The system yields reproducible results that are well correlated with expert manual segmentation.

Neurophet AQUA performance was evaluated by comparing segmentation accuracy with expert manual segmentations and by measuring segmentation reproducibility between same subject scans. The system yields reproducible results that are well correlated with expert manual segmentations.

Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)

Neurophet AQUA segmentation accuracy compared to expert manual segmentations of T1 MRI scans was evaluated using Dice's coefficient metric. For major subcortical brain structures Dice's coefficients are in the range of 80-90% and for major cortical regions are in the range of 75-85%.

Brain structural reproducibility of repeated T1 MRI scans for same subjects was evaluated by using the percentage absolute volume differences. The mean percentage absolute volume differences for all major subcortical structures were in the range of 1-5%.

Predicate Device(s)

NeuroQuant® v2.2 (K170981) by CorTechs Labs, Inc

Reference Device(s)

BrainInsight (K202414) by Hyperfine Research, Inc.

Predetermined Change Control Plan (PCCP) - All Relevant Information

Not Found

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).

Summary

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, with the letters "FDA" in a blue square. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

May 10, 2023

NEUROPHET, Inc. % Priscilla Chung Regulatory Affairs Consultant LK Consulting Group USA, Inc. 18881 Von Karman Ave. STE 160 IRVINE CA 92612

Re: K220437

Trade/Device Name: Neurophet AQUA Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ Dated: March 30, 2023 Received: March 31, 2023

Dear Priscilla Chung:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal

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statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

Daniel M. Krainak, Ph.D. Assistant Director Magnetic Resonance and Nuclear Medicine Team DHT8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K220437

Device Name Neurophet AQUA

Indications for Use (Describe)

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

X Prescription Use (Part 21 CFR 801 Subpart D)

Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) Summary (K220437)

This summary of 510(k) information is being submitted in accordance with requirements of 21 CFR Part 807.92.

1. Date: 5/2/2023

2. Applicant / Submitter

NEUROPHET, Inc. 12F, 124, Teheran-ro, Gangnam-gu Seoul, Republic of Korea Tel : +82-2-6954-7971 Fax : +82-2-6954-7972

3. U.S. Designated Agent

Priscilla Chung LK Consulting Group USA, Inc. 18881 Von Karman Ave. STE 160 Irvine, CA 92612 Fax: 714.409.3357 Tel: 714.202.5789 Email: juhee.c@LKconsultingGroup.com

4. Trade/Proprietary Name:

Neurophet AQUA

5. Common Name:

Medical Image Processing Software

6. Classification:

System, image processing, radiological (21CFR 892.2050, Product code LLZ, Class 2, Radiology)

7. Device Description:

Neurophet AQUA is a fully automated MR imaging post-processing medical device software that provides automatic labeling, visualization, and volumetric quantification of brain structures from a set of MR images and returns segmented images and morphometric reports.

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The resulting output is provided in morphometric reports that can be displayed on Picture Archive and Communications Systems (PACS). The high throughput capability makes the software suitable for use in both clinical trial research and routine patient care as a support tool for clinicians in assessment of structural MRIs.

Neurophet AQUA provides morphometric measurements based on T1 MRI series. The output of the software includes volumes that have been annotated with color overlays, with each color representing a particular segmented region, and morphometric reports that provide comparison of measured volumes to age and gender-matched reference percentile data. Neurophet AQUA processing architecture includes a proprietary automated internal pipeline that performs segmentation, volume calculation and report generation.

The results are displayed in a dedicated graphical user interface, allowing the user to:

Browse the segmentations and the measures, .
Compare the results of segmented brain structures to a reference healthy population,
Read and print a PDF report

Additionally, automated safety measures include automated quality control functions, such as tissue contrast check, scan protocol verification, which validate that the imaging protocols adhere to system requirements.

8. Indication for use:

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

9. Predicate Device:

. Primary Predicate: NeuroQuant® v2.2 (K170981) by CorTechs Labs, Inc
Reference Device: . BrainInsight (K202414) by Hyperfine Research, Inc.

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10. Substantial Equivalence:

	Subject Device	Primary predicate Device	Reference Device
Device name	Neurophet AQUA v2.1	NeuroQuant® v2.2	BrainInsight
510(k)	K220437	K170981	K202414
Manufacturer	NEUROPHET, Inc.	CorTechs Labs, Inc	Hyperfine Research, Inc
Product Code	LLZ	LLZ	LLZ
Indications for
Use	Neurophet AQUA is
intended for Automatic
labeling, visualization
and volumetric
quantification of
segmentable brain
structures from a set of
MR images. Volumetric
data may be compared to
reference percentile data.	NeuroQuant is intended for
automatic labeling,
visualization and
volumetric quantification of
segmentable brain
structures and lesions from
a set of MR images.
Volumetric data may be
compared to reference
percentile data.	Automatic labeling, spatial
measurement, and
volumetric
quantification of brain
structures from a set of
low-field MR images and
returns annotated and
segmented images, color
overlays, and reports.
Target
Anatomical
Sites	Brain	Brain	Brain
Design and
Incorporated
Technology	• Automated
measurement of brain
tissue volumes and
structures
• Automatic
segmentation and
quantification of brain
structures using deep
learning	• Automated measurement
of brain tissue volumes and
structures and lesions
• Automatic segmentation
and quantification of brain
structures using a dynamic
probabilistic
neuroanatomical atlas, with
age and gender specificity,
based on the MR image
intensity	• Automated measurement
of
brain tissue volumes and
structures
• Automatic segmentation
and quantification of brain
structures using machine
learning
Physical
characteristics	• Software package
• Operates on off-the-
shelf hardware (multiple
vendors)	• Software package
• Operates on off-the-shelf
hardware (multiple
vendors)	No software required
• Operates in a serverless
cloud environment
• User interface through
PACS (multiple vendors)
Operating
System	Windows	Supports Linux, Mac OS X
and Windows.	Supports Linux
Processing
Architecture	Automated internal
pipeline that performs:

segmentation
volume calculation
report generation | Automated internal pipeline
that performs:
artifact correction
segmentation
lesion quantification
volume calculation | Automated internal
pipeline that performs:
segmentation
volume calculation
distance measurement
numerical information |

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		- report generation	display
Data Source	• MRI scanner: 3D T1
scans acquired with
specified protocols
• Supports DICOM
format as input	• MRI scanner: 3D T1 and
FLAIR MRI scans acquired
with specified protocols
• Supports DICOM format
as input	• MRI scanner: Hyperfine
FSE MRI scans acquired
with
specified protocols
• Supports DICOM format
as
input
Output	• Provides volumetric
measurements of brain
structures
• Includes segmented
color overlays and
morphometric reports
• Automatically
compares results to
reference percentile data
and to prior scans when
available
• Supports DICOM
format as output of
results that can be
displayed on DICOM
workstations and Picture
Archive and
Communications
Systems	• Provides volumetric
measurements of brain
structures and lesions
• Includes segmented color
overlays and
morphometric reports
• Automatically compares
results to reference
percentile data and to prior
scans when available
• Supports DICOM format
as output of results that can
be displayed on DICOM
workstations and Picture
Archive and
Communications Systems	Provides volumetric
measurements of brain
structures
• Includes segmented color
overlays and
morphometric
reports
• Supports DICOM format
as
output of results that can
be
displayed on DICOM
workstations and Picture
Archive and
Communications Systems
Safety	• Automated quality
control functions

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

Neurophet AQUA and the predicate device are software for automatically identifying and quantifying the volumes of brain structures, automatic labeling and visualization. The devices have the same intended use and operating principle. They take MR brain images as input and generate an electronic report with similar quantitative information. For both devices, output volumes are compared to a normative dataset of control subjects computed based on MRI data from normal control subjects.

Both devices are DICOM compatible and operate on off-the-shelf hardware. Both devices are used by physicians skilled in brain MR imaging.

Neurophet AQUA is functionally similar and improved from a previous 510(k) market-cleared CorTechs Labs NeuroQuant software device (NeuroOuant K170981).

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Both devices have same intended use and basic design and similar operating principle.

	Neurophet AQUA	NeuroQuant
Processing architecture	Segmentation based on deep learning tools, volume calculation and report generation.	Artifact correction, atlas-based segmentation, lesion quantification, volume calculation and report generation.
Operating System	Windows	Supports Linux, Mac OS X and Windows.
Deployment	Installed	Cloud based or installed

Following are the differences between Neurophet AQUA and the predicate device:

Although both are technically similar, in the processing architecture, the subject device performs segmentation based on deep learning and the predicate device performs segmentation based on atlas-based.

Although the predicate device performs artifact correction, the subject device uses the data augmentation technique during deep learning for segmentation, so it robustly performs segmentation on MRI data with artifact correction.

We identified a reference device (BrainInsight, K202414) which also uses a fully automated MR imaging post-processing medical software that image alignment, whole brain segmentation, ventricle segmentation, and midline shift measurements based on machine learning tools. Similarly, the subject device and the reference device segments brain structures from T1 MR images based on a similar principle. Furthermore, for volumes derived from T1 images, the subject device and the reference device provide statistical comparison of normalized values with a normative dataset from a healthy reference population.

However, both systems use clinical MR brain scans as input and automatically identify and measure volumes of brain structures. Both systems provide morphometric measurements based on 3D T1 MRI series. The resulting output is provided in a standard DICOM format as additional MR series that can be displayed on third-party DICOM workstations and PACS. Both systems produce similar reports. The output includes volumes that have been annotated with color overlays. with each color representing a particular segmented region, and morphometric reports that provide comparison of measured volumes to reference percentile data.

They utilize the same automated safety measures and have similar processing architecture. Both devices are DICOM compatible and operate on off-the-shelf hardware. Both systems are used by medical professionals, such as radiologists, neurologists and neuroradiologists, as well as by clinical researchers, as a support tool in assessment of structural MRIs.

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11. Performance Data: