Who is the manufacturer of BrainInsight?

Hyperfine Research, Inc. filed the 510(k) submission for BrainInsight (K202414).

What is the FDA product code for BrainInsight?

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

What is the regulatory pathway for BrainInsight?

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

Does BrainInsight use AI or machine learning?

Yes. BrainInsight (K202414) 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 BrainInsight?

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 BrainInsight 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.

BrainInsight

K202414 · Hyperfine Research, Inc. · LLZ · Jan 7, 2021 · Radiology

Device Facts

Record ID	K202414
Device Name	BrainInsight
Applicant	Hyperfine Research, Inc.
Product Code	LLZ · Radiology
Decision Date	Jan 7, 2021
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2050
Device Class	Class 2
Attributes	AI/ML, Software as a Medical Device

Intended Use

BrainInsight is intended for 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.

Device Story

BrainInsight is automated MR imaging post-processing software for low-field (64mT) structural MRI. It accepts 3D T1 and T2-weighted DICOM images as input. The device uses a proprietary machine learning-based pipeline to perform whole brain segmentation, ventricle segmentation, and midline shift measurements. It includes automated quality control functions, such as tissue contrast checks and scan protocol verification. The system operates in a serverless cloud environment and returns annotated images, color overlays, and morphometric reports in DICOM format. These outputs are displayed on third-party workstations or PACS for review by a trained physician. The device supports clinicians in the assessment of structural MRIs by providing quantitative data and visualizations, potentially aiding in clinical decision-making for adult patients.

Clinical Evidence

No clinical data was required to demonstrate substantial equivalence. Evidence consists of bench testing and software evaluations confirming cybersecurity, PHI protection, midline shift measurement accuracy, 3D coordinate alignment, and segmentation performance.

Technological Characteristics

Software-based image processing system for 64mT MRI. Operates in a serverless cloud environment. Inputs/outputs via DICOM. Features automated segmentation, volume calculation, and distance measurement using machine learning. Includes automated quality control (tissue contrast, scan protocol, atlas alignment).

Indications for Use

Indicated for automatic labeling, spatial measurement, and volumetric quantification of brain structures from low-field (64mT) MR images in patients aged 18 or older.

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 (K170981)

Related Devices

K223268 — BrainInsight · Hyperfine, Inc. · Dec 16, 2022
K220815 — BrainInsight · Hyperfine, Inc. · Jul 19, 2022
K251483 — SwiftSight-Brain · Airs Medical, Inc. · Sep 23, 2025
K193287 — CorInsights MRI · Adm Diagnostics, Inc. · Nov 20, 2020
K252362 — GBrain MRI · Galileo Cds, Inc. · Aug 22, 2025

Submission Summary (Full Text)

{0}------------------------------------------------ January 7, 2021 Image /page/0/Picture/1 description: The image contains 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, which is a blue square with the letters "FDA" in white. To the right of the FDA logo is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue. Hyperfine Research, Inc. % Robert W. Fasciano, Ph.D. Head of Quality Assurance & Regulatory Affairs 530 Old Whitfield Street GUILFORD CT 06437 Re: K202414 Trade/Device Name: BrainInsight Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: December 4, 2020 Received: December 7, 2020 Dear Dr. Fasciano: 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/cfpmp/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 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 {1}------------------------------------------------ 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, For Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health Enclosure {2}------------------------------------------------ # Indications for Use 510(k) Number (if known) K202414 Device Name BrainInsight Indications for Use (Describe) BrainInsight is intended for automatic labeling, spatial measurement, and volumetric quantification of brain structures from a set of low-field MR images and returns annotated images, color overlays, and reports. | Type of Use (Select one or both, as applicable) | | |-------------------------------------------------|--| |-------------------------------------------------|--| 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}------------------------------------------------ Page 1 of 4 Section 5 – 510(k) Summary K202414 | 510(k) Summary (As required by 21 CFR 807.92) | | | |-----------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------| | Date Summary Prepared: | August 21, 2020 | | | Company Name: As required by 807.92(a)(1) | Hyperfine Research, Inc. | | | Robert W. Fasciano, PhD | | | | Head of Quality Assurance & Regulatory Affairs | | | | | 530 Old Whitfield St. | | | | Guilford, CT 06437 | | | | (617) 435-9098 | | | | rfasciano@hyperfine-research.com | | | Device Name: As required by 807.92(a)(2) | Device/Trade Name: | BrainInsight | | | Device Common Name: | System, Image Processing, Radiological | | | Regulation Number: | 21 CFR 892.2050 | | | Regulation Name: | System, Image Processing, Radiological | | | Regulation Description: | Picture archive and communications system | | | Class: | II | | | Product Code: | LLZ | | Predicate Device(s): As required by 807.92(a)(3) | NeuroQuant, K170981 | | | Device Description: As required by 807.92(a)(4) | BrainInsight is a fully automated MR imaging post- processing medical software that image alignment, whole brain segmentation, ventricle segmentation, and midline shift measurements of brain structures from a set of MR images from patients aged 18 or older. The output annotated and segmented images are provided in a standard image format using segmented color overlays and reports that can be displayed on third-party workstations and FDA cleared Picture Archive and Communications Systems (PACS). The high throughput capability makes the software suitable for use in routine patient care as a support tool for clinicians ir assessment of low-field (64mT) structural MRIs. BrainInsight provides overlays and reports based on 64mT 3D MRI series of a T1 and T2-weighted sequence. The outputs of the software are DICOM images which include | | {4}------------------------------------------------ volumes that have been annotated with color overlays, with each color representing a particular segmented region, spatial measurement of anatomical structures, and information reports computed from the image data, segmentations, and measurements. The BrainInsight processing architecture includes a proprietary automated internal pipeline that performs whole brain segmentation, ventricle segmentation, and midline shift measurements based on machine learning tools. Additionally, the system's automated safety measures include automated quality control functions, such as tissue contrast check and scan protocol verification. The system is installed on a standard computing platform, e.g. server that may be in the cloud, and is designed to support file transfer for input and output of results. Statement of Intended Use: BrainInsight is intended for automatic labeling, spatial As required by 807.92(a)(5) 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. | Device | Proposed Device | Predicate Device | |----------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | | BrainInsight | NeuroQuant, K170981 | | Classification | Class II, LLZ, 21 CFR 892.2050 | Class II, LLZ, 21 CFR 892.2050 | | Intended Use | 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. | 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 | | Target Anatomical Sites | Brain | Brain | | Technology | • Automated measurement of brain tissue volumes and structures • Automatic segmentation and quantification of brain structures using machine 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 | | Method of Use | MR images are automatically sent to BrainInsight and processed images are automatically returned in approximately 7 minutes. | User manually sends MR images to NeuroQuant and processed images are automatically returned in approximately 7 minutes. | | Device | Proposed Device BrainInsight | Predicate Device NeuroQuant, K170981 | | User Interface / Physical Characteristics | • No software required • Operates in a serverless cloud environment • User interface through PACS (multiple vendors) | • Software package installed on User hardware • Operates on off-the-shelf hardware (multiple vendors) • User interface through the software package | | Operating System | Supports Linux | Supports Linux, Mac OS X and Windows | | Processing Architecture | Automated internal pipeline that performs: - segmentation - volume calculation - distance measurement - numerical information display | Automated internal pipeline that performs: - artifact correction - segmentation - lesion quantification - volume calculation - report generation | | Data Source | • MRI scanner: Hyperfine FSE MRI scans acquired with specified protocols • Supports DICOM format as input | • MRI scanner: 3D T1 MRI scans acquired with specified protocols • NeuroQuant Supports DICOM format as input | | Output | 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 | 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 | | Safety | 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 | ## Comparison of Technological Characteristics with Predicate Devices: As required by 807.92(a)(6) {5}------------------------------------------------ # Comparison of Technological Characteristics with Predicate Devices: As required by 807.92(a)(6) Performance data was limited to software evaluations to Non-clinical Performance Data: As required by 807.92(b)(1) confirm: - Cybersecurity and PHI protection • - . Midline shift - 3D Coordinates and alignment • - . Segmentation {6}------------------------------------------------ | Data Quality Control Audit trail User Manual information Software control Ventricle segmentation Midline shift measurement Skull stripping | |-------------------------------------------------------------------------------------------------------------------------------------------------------| | Assessment of Clinical Data: No clinical data was required to demonstrate substantial As required by 807.92(b)(2) equivalence. | | Overall Conclusions: Based on the indications for use, technologic As required by 807.92(b)(3) characteristics, and comparison to predicate device | ical ice, BrainInsight has been shown to be substantially equivalent to the predicate and is safe and effective for its intended use.