(136 days)
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.
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 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.
The provided text describes the BrainInsight device and references its 510(k) summary (K202414). However, it does not contain specific acceptance criteria or a detailed study description with performance metrics, sample sizes, or ground truth establishment relevant to those criteria. The "Non-clinical Performance Data" section lists areas of evaluation but doesn't provide the results against specific criteria.
Therefore, I cannot fulfill the request to provide a table of acceptance criteria and reported device performance based solely on the provided text.
However, I can extract information related to the studies mentioned and other requested points:
1. Table of Acceptance Criteria and Reported Device Performance
- Not explicitly provided in the text. The document lists areas of non-clinical performance data (Cybersecurity and PHI protection, Midline shift, 3D Coordinates and alignment, Segmentation, Data Quality Control, Audit trail, User Manual information, Software control, Ventricle segmentation, Midline shift measurement, Skull stripping). However, it does not state specific acceptance criteria (e.g., "midline shift accuracy > X%") or the actual performance achieved against such criteria.
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: Not specified in the provided text.
- Data Provenance: Not specified in the provided text. The device processes MRI scans from "Hyperfine FSE MRI scans acquired with specified protocols." Whether these were retrospective or prospective, or from specific countries, is not mentioned.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- Not explicitly provided in the text. The document states that "Results must be reviewed by a trained physician," implying human review, but does not detail how ground truth for a test set was established (e.g., number of experts, their qualifications, or their role in defining ground truth for segmentation or measurement accuracy).
4. Adjudication Method for the Test Set
- Not explicitly provided in the text.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No MRMC study mentioned. The document focuses on the device's standalone capabilities and its equivalence to a predicate. There is no mention of a study involving human readers with and without AI assistance or effect sizes.
6. Standalone (Algorithm Only Without Human-in-the-loop) Performance
- Yes, a standalone evaluation was performed. The "Non-clinical Performance Data" section describes software evaluations conducted to confirm various aspects like midline shift, 3D coordinates and alignment, segmentation, ventricle segmentation, and skull stripping. This indicates an assessment of the algorithm's performance independent of human input during the processing phase.
7. Type of Ground Truth Used
- Not explicitly provided in the text. The document describes the device's function (automatic labeling, spatial measurement, volumetric quantification, segmentation, midline shift measurements) and states "Performance data was limited to software evaluations to confirm...". While this implies comparison to some form of truth, the type of ground truth (e.g., expert consensus, manual tracings, pathology, outcomes data) for the segmentation, measurements, and other evaluated features is not detailed.
8. Sample Size for the Training Set
- Not explicitly provided in the text. The device uses "machine learning tools" for its processing architecture, indicating the use of a training set, but its size is not disclosed.
9. How the Ground Truth for the Training Set Was Established
- Not explicitly provided in the text. While it states machine learning is used, the process for establishing the ground truth labels or segmentations used to train these models is not detailed.
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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
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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
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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)
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Page 1 of 4 Section 5 – 510(k) Summary
| 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 andcommunications 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, wholebrain segmentation, ventricle segmentation, and midlineshift measurements of brain structures from a set of MRimages from patients aged 18 or older. The output annotatedand segmented images are provided in a standard imageformat using segmented color overlays and reports that canbe displayed on third-party workstations and FDA clearedPicture Archive and Communications Systems (PACS). Thehigh throughput capability makes the software suitable foruse in routine patient care as a support tool for clinicians irassessment of low-field (64mT) structural MRIs.BrainInsight provides overlays and reports based on 64mT3D MRI series of a T1 and T2-weighted sequence. Theoutputs of the software are DICOM images which include |
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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, spatialmeasurement, and volumetricquantification of brain structuresfrom a set of low-field MRimages and returns annotatedand segmented images, coloroverlays, and reports. | Automatic labeling, visualizationand volumetric quantification ofsegmentable brain structures andlesions from a set of MR images.Volumetric data may becompared to reference percentiledata |
| Target Anatomical Sites | Brain | Brain |
| Technology | • Automated measurement ofbrain tissue volumes andstructures• Automatic segmentation andquantification of brain structuresusing machine learning | • Automated measurement ofbrain tissue volumes andstructures and lesions• Automatic segmentation andquantification of brain structuresusing a dynamic probabilisticneuroanatomical atlas, with ageand gender specificity, based onthe MR image intensity |
| Method of Use | MR images are automaticallysent to BrainInsight andprocessed images areautomatically returned inapproximately 7 minutes. | User manually sends MR imagesto NeuroQuant and processedimages are automaticallyreturned in approximately 7minutes. |
| Device | Proposed DeviceBrainInsight | Predicate DeviceNeuroQuant, K170981 |
| User Interface / PhysicalCharacteristics | • No software required• Operates in a serverless cloud environment• User interface through PACS(multiple vendors) | • Software package installed onUser hardware• Operates on off-the-shelfhardware (multiple vendors)• User interface through thesoftware package |
| Operating System | Supports Linux | Supports Linux, Mac OS X andWindows |
| Processing Architecture | Automated internal pipeline thatperforms:- segmentation- volume calculation- distance measurement- numerical information display | Automated internal pipeline thatperforms:- artifact correction- segmentation- lesion quantification- volume calculation- report generation |
| Data Source | • MRI scanner: Hyperfine FSEMRI scans acquired withspecified protocols• Supports DICOM format asinput | • MRI scanner: 3D T1 MRIscans acquired with specifiedprotocols• NeuroQuant Supports DICOMformat as input |
| Output | Provides volumetricmeasurements of brain structures• Includes segmented coloroverlays and morphometricreports• Supports DICOM format asoutput of results that can bedisplayed on DICOMworkstations and PictureArchive and CommunicationsSystems | Provides volumetricmeasurements of brain structuresand lesions• Includes segmented coloroverlays and morphometricreports• Automatically compares resultsto reference percentile data andto prior scans when available• Supports DICOM format asoutput of results that can bedisplayed on DICOMworkstations and PictureArchive and CommunicationsSystems |
| Safety | Automated quality controlfunctions• Tissue contrast check• Scan protocol verification• Atlas alignment check• Results must be reviewed by atrained physician | Automated quality controlfunctions• Tissue contrast check• Scan protocol verification• Atlas alignment check• Results must be reviewed by atrained physician |
Comparison of Technological Characteristics with Predicate Devices: As required by 807.92(a)(6)
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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
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| 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 substantialAs required by 807.92(b)(2) equivalence. |
| Overall Conclusions: Based on the indications for use, technologicAs 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.
§ 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).