(136 days)
Not Found
Yes
The device description explicitly states that the processing architecture includes "machine learning tools".
No
The device is a post-processing software that provides quantitative measurements and annotations from MR images; it does not directly treat or diagnose a disease or condition.
Yes.
The device's intended use is to provide "automatic labeling, spatial measurement, and volumetric quantification of brain structures from a set of low-field MR images," which directly supports clinical assessment and aids in determining the nature or cause of a disease or condition. Its output, including "annotated images, color overlays, and reports," serves as a support tool for clinicians in the assessment of MRIs, thereby contributing to diagnosis.
Yes
The device description explicitly states "BrainInsight is a fully automated MR imaging post-processing medical software". It processes existing MR images and provides outputs (annotated images, reports) that are displayed on third-party workstations and PACS. There is no mention of any hardware component being part of the device itself.
Based on the provided information, BrainInsight is NOT an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostics are devices intended for use in the collection, preparation, and examination of specimens taken from the human body (such as blood, urine, tissue) to provide information for the diagnosis, treatment, or prevention of disease.
- BrainInsight's Function: BrainInsight processes medical images (MR images) of the brain. It does not analyze biological specimens taken from the body. Its function is to provide quantitative and spatial information about brain structures based on these images.
Therefore, while BrainInsight is a medical device that uses advanced technology (machine learning) for clinical support, its intended use and mechanism of action fall outside the definition of an In Vitro Diagnostic device.
No
The letter does not mention that the FDA has reviewed and approved or cleared a PCCP for this device.
Intended Use / Indications for 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 images, color overlays, and reports.
Product codes
LLZ
Device Description
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
low-field MR images, 64mT 3D MRI series of a T1 and T2-weighted sequence
Anatomical Site
Brain
Indicated Patient Age Range
18 or older
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance data was limited to software evaluations to confirm:
- 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
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found
Predicate Device(s)
NeuroQuant, K170981
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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).
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)
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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 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.