Who is the manufacturer of Neurophet AQUA?

Neurophet., Inc. filed the 510(k) submission for Neurophet AQUA (K220437).

What is the FDA product code for Neurophet AQUA?

LLZ. It is classified under 21 CFR 892.2050 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for Neurophet AQUA?

510(k) clearance (submission type: Traditional).

Does Neurophet AQUA use AI or machine learning?

Yes. Neurophet AQUA (K220437) is flagged as an AI/ML-enabled medical device in the FDA's AI/ML device list.

Who can help prepare an FDA 510(k) submission like Neurophet AQUA?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

Can an AI/ML device like Neurophet AQUA get a guaranteed FDA 510(k) clearance?

Yes. Innolitics offers a guaranteed FDA 510(k) clearance engagement for AI/ML Software as a Medical Device — combining engineering and regulatory execution under one agreement with a guaranteed clearance outcome. Eligibility: the device must be an AI/ML Software as a Medical Device (SaMD), and its performance must meet the criteria Innolitics determines during an initial regulatory strategy engagement to be necessary for FDA clearance. See https://innolitics.com/services/end-to-end-samd/ or contact https://innolitics.com/contact.

Neurophet AQUA

K220437 · Neurophet., Inc. · LLZ · May 10, 2023 · Radiology

Device Facts

Record ID	K220437
Device Name	Neurophet AQUA
Applicant	Neurophet., Inc.
Product Code	LLZ · Radiology
Decision Date	May 10, 2023
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2050
Device Class	Class 2
Attributes	AI/ML, Software as a Medical Device

Intended 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.

Device Story

Neurophet AQUA is an automated MR imaging post-processing software; inputs 3D T1-weighted MRI scans (DICOM format). Uses deep learning-based internal pipeline to perform brain structure segmentation, volume calculation, and report generation. Outputs include segmented images with color overlays and morphometric reports comparing volumes to age/gender-matched reference percentile data. Used in clinical research and routine care by physicians (radiologists, neurologists, neuroradiologists) to support structural MRI assessment. Software operates on off-the-shelf hardware; results displayed on PACS/DICOM workstations. Automated quality control functions (tissue contrast check, scan protocol verification) ensure data integrity. Benefits include standardized, reproducible quantification of brain structures to assist clinical decision-making.

Clinical Evidence

Bench testing only. Accuracy evaluated against expert manual segmentations (FreeSurfer v6.0 ground truth verified by radiologists) using 64 T1 scans; Dice's coefficients: 80-90% (subcortical), 75-85% (cortical). Reproducibility evaluated using 50 repeated T1 scans; mean percentage absolute volume differences: 1-5% for major subcortical structures. Training dataset included 300 T1-weighted MRI scans from ten scanner types (ADNI, IXI, PPMI, HCP, AIBL). Testing data was exclusive from training data.

Technological Characteristics

Software-only device; operates on off-the-shelf hardware (Windows OS). Uses deep learning-based segmentation pipeline. Inputs: 3D T1 MRI (DICOM). Outputs: DICOM-compatible segmented images and morphometric reports. Automated quality control includes tissue contrast and scan protocol verification.

Indications for Use

Indicated for automatic labeling, visualization, and volumetric quantification of brain structures from MR images in patients (including those with cognitive impairment or Alzheimer's disease).

Regulatory Classification

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.

Special Controls

*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).

Predicate Devices

NeuroQuant® v2.2 (K170981)

Reference Devices

BrainInsight (K202414)

Related Devices

K242215 — Neurophet AQUA (V3.1) · Neurophet., Inc. · Oct 25, 2024
K170981 — NeuroQuant · Cortechs Labs, Inc. · Sep 7, 2017
K251483 — SwiftSight-Brain · Airs Medical, Inc. · Sep 23, 2025
K232231 — QP-Brain® · Quibim S.L. · Dec 13, 2023
K193287 — CorInsights MRI · Adm Diagnostics, Inc. · Nov 20, 2020

Submission Summary (Full Text)

{0}------------------------------------------------ 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 {1}------------------------------------------------ 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 {2}------------------------------------------------ ## 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) #### CONTINUE ON A SEPARATE PAGE IF NEEDED. This section applies only to requirements of the Paperwork Reduction Act of 1995. #### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.* The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: > Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff(@fda.hhs.gov "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." {3}------------------------------------------------ # 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. {4}------------------------------------------------ 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. {5}------------------------------------------------ # 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 | {6}------------------------------------------------ | | | - 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). {7}------------------------------------------------ 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. {8}------------------------------------------------ ### 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. 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. 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 {9}------------------------------------------------ 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.