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.

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.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study details for Neurophet AQUA, based on the provided text:

1. Acceptance Criteria and Reported Device Performance

Acceptance Criteria	Reported Device Performance (Neurophet AQUA)
Segmentation Accuracy (Dice's Coefficient for major subcortical brain structures)	In the range of 80-90%
Segmentation Accuracy (Dice's Coefficient for major cortical regions)	In the range of 75-85%
Reproducibility (Mean percentage absolute volume differences for all major subcortical structures)	In the range of 1-5%

2. Sample Size Used for the Test Set and Data Provenance

Sample Size for Accuracy Test Set: 64 T1 scans (36 US-based, 40 females, age range 20-90)
Sample Size for Reproducibility Test Set: 50 repeated T1 scans (31 US-based, 23 females, age range 10-90)
Data Provenance: Both test sets included retrospective data from various sources:
- 36 of 64 (56%) scans for accuracy were US-based data.
- 31 of 50 (62%) scans for reproducibility were US-based data.
- The test sets included cognitive normal, mild cognitive impairment, and Alzheimer's disease patients.
- Data was acquired from MR scanners of three main vendors (Siemens, Phillips, and GE).
- The document explicitly states that "All the testing data was exclusive from the training dataset."

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

The text states, "Ground-truth data were initially generated using FreeSurfer (General Hospital Corporation, Boston, MA, USA, version 6.0) and verified and corrected by four radiologists."
Qualifications of Experts: Four radiologists. Specific experience level (e.g., years of experience) is not provided in the document.

4. Adjudication Method for the Test Set

The ground truth for the training set was initially generated by FreeSurfer and then "verified and corrected by four radiologists." This implies a consensus or expert review process, where the radiologists likely reviewed and refined the FreeSurfer outputs. The specific adjudication method (e.g., 2+1, 3+1) is not explicitly detailed, but it indicates multiple expert review and correction.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not reported in this summary. The study focused on the standalone performance of the AI device against expert manual segmentations (ground truth) and reproducibility.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone performance study was conducted. The performance metrics (Dice's coefficient and percentage absolute volume differences) directly evaluate the algorithm's output against the established ground truth without involving human-in-the-loop performance for the reported results. The device "yields reproducible results that are well correlated with expert manual segmentations," indicating an evaluation of the device's output itself.

7. The Type of Ground Truth Used

The ground truth used was expert consensus / semi-automated expertise. It was "initially generated using FreeSurfer" (a widely-accepted brain segmentation software) and then "verified and corrected by four radiologists."

8. The Sample Size for the Training Set

300 T1-weighted MRI scans.

9. How the Ground Truth for the Training Set Was Established

The ground truth for the training set was established in the same manner as the ground truth for the test set: "Ground-truth data were initially generated using FreeSurfer (General Hospital Corporation, Boston, MA, USA, version 6.0) and verified and corrected by four radiologists."
These 300 scans were collected from ten different MRI scanner types and included public datasets such as ADNI, IXI, PPMI, HCP, and AIBL.

Summary

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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)

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:

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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 forUse	Neurophet AQUA isintended for Automaticlabeling, visualizationand volumetricquantification ofsegmentable brainstructures from a set ofMR images. Volumetricdata may be compared toreference percentile data.	NeuroQuant is intended forautomatic labeling,visualization andvolumetric quantification ofsegmentable brainstructures and lesions froma set of MR images.Volumetric data may becompared to referencepercentile data.	Automatic labeling, spatialmeasurement, andvolumetricquantification of brainstructures from a set oflow-field MR images andreturns annotated andsegmented images, coloroverlays, and reports.
TargetAnatomicalSites	Brain	Brain	Brain
Design andIncorporatedTechnology	• Automatedmeasurement of braintissue volumes andstructures• Automaticsegmentation andquantification of brainstructures using deeplearning	• Automated measurementof brain tissue volumes andstructures and lesions• Automatic segmentationand quantification of brainstructures using a dynamicprobabilisticneuroanatomical atlas, withage and gender specificity,based on the MR imageintensity	• Automated measurementofbrain tissue volumes andstructures• Automatic segmentationand quantification of brainstructures using machinelearning
Physicalcharacteristics	• Software package• Operates on off-the-shelf hardware (multiplevendors)	• Software package• Operates on off-the-shelfhardware (multiplevendors)	No software required• Operates in a serverlesscloud environment• User interface throughPACS (multiple vendors)
OperatingSystem	Windows	Supports Linux, Mac OS Xand Windows.	Supports Linux
ProcessingArchitecture	Automated internalpipeline that performs:- segmentation- volume calculation- report generation	Automated internal pipelinethat performs:- artifact correction- segmentation- lesion quantification- volume calculation	Automated internalpipeline that performs:- segmentation- volume calculation- distance measurement- numerical information

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		- report generation	display
Data Source	• MRI scanner: 3D T1scans acquired withspecified protocols• Supports DICOMformat as input	• MRI scanner: 3D T1 andFLAIR MRI scans acquiredwith specified protocols• Supports DICOM formatas input	• MRI scanner: HyperfineFSE MRI scans acquiredwithspecified protocols• Supports DICOM formatasinput
Output	• Provides volumetricmeasurements of brainstructures• Includes segmentedcolor overlays andmorphometric reports• Automaticallycompares results toreference percentile dataand to prior scans whenavailable• Supports DICOMformat as output ofresults that can bedisplayed on DICOMworkstations and PictureArchive andCommunicationsSystems	• Provides volumetricmeasurements of brainstructures and lesions• Includes segmented coloroverlays andmorphometric reports• Automatically comparesresults to referencepercentile data and to priorscans when available• Supports DICOM formatas output of results that canbe displayed on DICOMworkstations and PictureArchive andCommunications Systems	Provides volumetricmeasurements of brainstructures• Includes segmented coloroverlays andmorphometricreports• Supports DICOM formatasoutput of results that canbedisplayed on DICOMworkstations and PictureArchive andCommunications Systems
Safety	• Automated qualitycontrol functions- Tissue contrast check- Scan protocolverification• Results must bereviewed by a trainedphysician	• Automated quality controlfunctions- Tissue contrast check- Scan protocol verification- Atlas alignment check• Results must be reviewedby a trained physician	Automated quality controlfunctions• Tissue contrast check• Scan protocolverification• Atlas alignment check• Results must be reviewedby 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:

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.

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.

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.

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%.

12. Conclusion:

The subject device is substantially equivalent in the areas of technical characteristics, general function, application, and indications for use. The new device does not introduce a

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fundamentally new scientific technology, and the device has been validated through system level test. Therefore, we conclude that the subject device described in this submission is substantially equivalent to the predicate device.

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).