(120 days)
Not Found
Yes
The device description explicitly states that the processing architecture includes a proprietary automated internal pipeline based on machine learning tools, and that the modified device includes changes to the machine learning models.
No
This device is a post-processing software that analyzes medical images for diagnostic purposes, not a therapeutic device designed to treat or alleviate a condition.
Yes
The device is a diagnostic device because its intended use includes "automatic labeling, spatial measurement, and volumetric quantification of brain structures" and provides "annotated and segmented images, color overlays and reports," which are used to aid in diagnosis. Additionally, the device description mentions providing "image alignment, whole brain segmentation, ventricle segmentation, and midline shift measurements of brain structures," all of which are diagnostic aids.
Yes
The device description explicitly states "BrainInsight is a fully automated MR imaging post-processing medical software". It processes existing MR images and provides output in standard image formats for display on third-party workstations and PACS, indicating it does not include any hardware components for image acquisition or display.
No, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices used to examine specimens taken from the human body, such as blood, urine, or tissue, to provide information about a person's health. This testing is performed outside of the living body (in vitro).
- BrainInsight's Function: BrainInsight processes images of the brain acquired from an MR scanner. It does not analyze biological samples taken from the patient. It works with data generated by an imaging modality.
Therefore, based on the provided information, BrainInsight falls under the category of medical image processing software, not an In Vitro Diagnostic device.
No
The letter does not state that the FDA has reviewed and approved or cleared a PCCP for this specific device. The provided text explicitly says "Control Plan Authorized (PCCP) and relevant text Not Found".
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 and segmented images, color overlans and reports.
Product codes
QIH
Device Description
BrainInsight is a fully automated MR imaging post-processing medical software that provides image alignment, whole brain segmentation, ventricle segmentation, and midline shift measurements of brain structures from a set of MR images from patients ages 18 years or older. The BrainInsight processing architecture includes a proprietary automated internal pipeline based on machine learning tools. The output annotated and segmented images are provided in 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 modified BrainInsight described in this submission includes changes to the machine learning models to allow for the processing Al-reconstructed low-field MR images. The modified device also includes configuration updates and refactoring changes for incremental improvement.
Mentions image processing
Yes
Mentions AI, DNN, or ML
The BrainInsight processing architecture includes a proprietary automated internal pipeline based on machine learning tools.
The modified BrainInsight described in this submission includes changes to the machine learning models to allow for the processing Al-reconstructed low-field MR images.
Automated measurement of brain tissue volumes and structures of Al-reconstructed low-field MR images Automatic segmentation and quantification of brain structures of Al-reconstructed low-field MR images using machine learning tools
Automatic segmentation and quantification of brain structures of conventional low-field MR images using machine learning tools
Input Imaging Modality
low-field MR images
MRI Scanner: Hyperfine Swoop FSE MRI scans acquired with specified protocols Supports DICOM format as input
Anatomical Site
brain structures
Brain
Indicated Patient Age Range
patients ages 18 years or older.
Adult (>= 18 years)
Min: 19 Max: 77
Intended User / Care Setting
Not Found
Description of the training set, sample size, data source, and annotation protocol
Each model was trained using a training dataset to optimize parameters and a separate validation dataset to select the best set of parameters. Comparing the training and validation metrics helps to monitor and prevent overfitting. The datasets were augmented to improve robustness using standard techniques that apply transformations to the input data.
The data collection for the training and validation datasets were done at multiple sites. Each site used the T1 and T2 sequences from the FDA cleared Hyperfine Swoop Portable MR imaging system. The datasets were annotated by multiple experts. The entire group of training image sets was divided into segments and each segment was given to a single expert. The expert's determination became the ground truth for each image set in their segment.
Description of the test set, sample size, data source, and annotation protocol
The testing dataset was separate from training and validation datasets. Each subject was assigned a unique identifier and all subjects in training and validation data were excluded from the test set.
Each model and application were validated using an appropriate sample size to yield statistically significant results. All test images were acquired using the latest Swoop software version. The test set had the following distribution:
| Category | Data Distribution |
|---|---|
| Age | Min: 19 Max: 77 |
| Gender | 59% F / 41% M |
| Pathology | Stroke (Infarct) Hydrocephalus Hemorrhage (SAH, SDH, IVH, IPH) Mass/Edema Tumor Multiple sclerosis |
Ground truth for midline shift was determined based on the average shift distance of all annotators.
Ground truth for segmentation is calculated using Simultaneous Truth and Performance Level Estimation (STAPLE).
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Quantitative evaluation was performed to validate performance using software. The performance of the model and the annotators to the consensus-based annotation was computed to ensure that the model performance is no worse than the average annotator. The acceptance criteria were defined based on non-inferiority testing, in which the model discrepancy to the annotators can be no worse than the average annotator discrepancy.
The test results show high accuracy of BrainInsight performance as compared to the reference and annotators and the subject device met all acceptance criteria.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
The mean absolute error was used to calculate the error range for midline shift.
| Application | T1 Error | T2 Error |
|---|---|---|
| Midline Shift | 1.03 mm | 0.97 mm |
The mean Dice coefficient was used to calculate the error range for the lateral ventricles and whole brain.
Application | Dice Overlap [%] | | Volume Differences [%] |
---|---|---|---|---
T1 | Device | Annotator | Device | Annotator
Left Ventricle | 84 | 90 | 8 | 8
Right Ventricle | 82 | 89 | 7 | 11
Whole Brain | 95 | 97 | 3 | 2
Application | Dice Overlap [%] | | Volume Differences [%] |
---|---|---|---|---
T2 | Device | Annotator | Device | Annotator
Left Ventricle | 81 | 84 | 11 | 27
Right Ventricle | 79 | 84 | 19 | 26
Whole Brain | 96 | 97 | 5 | 5
Predicate Device(s)
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
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 and 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 blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Hyperfine, Inc. % Christine Kupchick Sr. Regulatory Specialist 351 New Whitfield Street GUILFORD CT 06437
Re: K220815
July 19, 2022
Trade/Device Name: BrainInsight Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: June 22, 2022 Received: June 23, 2022
Dear Christine Kupchick:
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 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,
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Ouality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known) K220815
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 and segmented images, color overlans and reports.
Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
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HYPERFINE
510(k) SUMMARY K220815
510(k) Submitter
| Company Name: | Hyperfine, Inc. |
|---|---|
| Company Address: | 351 New Whitfield St |
| Guilford, CT 06437 |
Contact
| Name: | Christine Kupchick |
|---|---|
| Telephone: | (203) 343-3404 |
| Email: | ckupchick@hyperfine.io |
July 14, 2022 Date Prepared:
Device Identification
| Trade Name: | BrainInsight |
|---|---|
| Common Name: | MR Imaging Post-Processing Software |
| Regulation Number: | 21 CFR 892.2050 |
| Product Code: | QIH |
| Regulatory Class: | Class II |
Predicate Device Information
The subject BrainInsight is substantially equivalent to the predicate BrainInsight (K202414). The predicate device has not been subject to a design-related recall.
Device Description
BrainInsight is a fully automated MR imaging post-processing medical software that provides image alignment, whole brain segmentation, ventricle segmentation, and midline shift measurements of brain structures from a set of MR images from patients ages 18 years or older. The BrainInsight processing architecture includes a proprietary automated internal pipeline based on machine learning tools. The output annotated and segmented images are provided in 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 modified BrainInsight described in this submission includes changes to the machine learning models to allow for the processing Al-reconstructed low-field MR images. The modified device also includes configuration updates and refactoring changes for incremental improvement.
4
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 and segmented images, color overlays and reports.
Technological Characteristics
The subject device has the same intended use, fundamental technology, and operating principles, as the predicate (K202414). Therefore, the subject device is substantially equivalent to the predicate.
Substantial Equivalence Discussion
The table below compares the subject device to the predicate.
| Attribute | Subject BrainInsight | Predicate BrainInsight (K202414) |
|---|---|---|
| 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 and | ||
| segmented images, color overlays and reports. | Same | |
| Target Anatomical | ||
| Sites | Brain | Same |
| Patient Population | Adult (≥ 18 years) | Same |
| Technology | Automated measurement of brain tissue | |
| volumes and structures of Al-reconstructed | ||
| low-field MR images Automatic segmentation and quantification | ||
| of brain structures of Al-reconstructed low- | ||
| field MR images using machine learning | ||
| tools | Automated measurement of brain | |
| tissue volumes and structures of | ||
| conventional low-field MR images Automatic segmentation and | ||
| quantification of brain structures of | ||
| conventional low-field MR images | ||
| using machine learning tools | ||
| Method of Use | MR images are automatically sent to | |
| BrainInsight, and processed images are | ||
| automatically returned in ~7 minutes | Same | |
| User Interface / | ||
| Physical | ||
| Characteristics | No software required Operates in a serverless cloud environment User interface through PACS (multiple | |
| vendors) | Same | |
| Operating System | Supports Linux | Same |
| Processing | ||
| Architecture | Automated internal pipeline that performs: | |
| segmentation volume calculation distance measurement numerical information display | Same | |
| Data Source | MRI Scanner: Hyperfine Swoop FSE MRI | |
| scans acquired with specified protocols Supports DICOM format as input | Same | |
| Output | Provides volumetric measurements of brain | |
| structures: | ||
| Includes segmented color overlays and | ||
| morphometric reports | Same |
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| Supports DICOM format as output of results that can be displayed on DICOM workstations and PACS | ||
|---|---|---|
| Safety | Automated quality control functions: | |
| Tissue contrast check Scan protocol verification Atlas alignment check Results must be reviewed by a trained physician | Same |
Performance
As part of demonstrating substantial equivalence to the predicate, a risk analysis was completed to identify the risks associated with the software modifications. Software verification as related to the modifications was performed per IEC 62304:2006 and as recommended in the FDA Guidance, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The subject device passed all the testing in accordance with internal requirements and applicable standards to support substantial equivalence.
Each model was trained using a training dataset to optimize parameters and a separate validation dataset to select the best set of parameters. Comparing the training and validation metrics helps to monitor and prevent overfitting. The datasets were augmented to improve robustness using standard techniques that apply transformations to the input data. The testing dataset was separate from training and validation datasets. Each subject was assigned a unique identifier and all subjects in training and validation data were excluded from the test set.
The data collection for the training and validation datasets were done at multiple sites. Each site used the T1 and T2 sequences from the FDA cleared Hyperfine Swoop Portable MR imaging system. The datasets were annotated by multiple experts. The entire group of training image sets was divided into segments and each segment was given to a single expert. The expert's determination became the ground truth for each image set in their segment. Each model and application were validated using an appropriate sample size to yield statistically significant results. All test images were acquired using the latest Swoop software version. The test set had the following distribution:
| Category | Data Distribution |
|---|---|
| Age | Min: 19 Max: 77 |
| Gender | 59% F / 41% M |
| Pathology | Stroke (Infarct) Hydrocephalus Hemorrhage (SAH, SDH, IVH, IPH) Mass/Edema Tumor Multiple sclerosis |
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Quantitative evaluation was performed to validate performance using software. The performance of the model and the annotators to the consensus-based annotation was computed to ensure that the model performance is no worse than the average annotator. The acceptance criteria were defined based on non-inferiority testing, in which the model discrepancy to the annotators can be no worse than the average annotator discrepancy.
The mean absolute error was used to calculate the error range for midline shift. Ground truth for midline shift was determined based on the average shift distance of all annotators.
| Application | T1 Error | T2 Error |
|---|---|---|
| Midline Shift | 1.03 mm | 0.97 mm |
The mean Dice coefficient was used to calculate the error range for the lateral ventricles and whole brain. Ground truth for segmentation is calculated using Simultaneous Truth and Performance Level Estimation (STAPLE).
| Application | Dice Overlap [%] | Volume Differences [%] | ||
|---|---|---|---|---|
| T1 | Device | Annotator | Device | Annotator |
| Left Ventricle | 84 | 90 | 8 | 8 |
| Right Ventricle | 82 | 89 | 7 | 11 |
| Whole Brain | 95 | 97 | 3 | 2 |
| Application | Dice Overlap [%] | Volume Differences [%] | ||
|---|---|---|---|---|
| T2 | Device | Annotator | Device | Annotator |
| Left Ventricle | 81 | 84 | 11 | 27 |
| Right Ventricle | 79 | 84 | 19 | 26 |
| Whole Brain | 96 | 97 | 5 | 5 |
The test results show high accuracy of BrainInsight performance as compared to the reference and annotators and the subject device met all acceptance criteria.
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Conclusion
Based on the indications for use, technological characteristics, performance results, and comparison to the predicate, the subject BrainInsight has been shown to be substantially equivalent to the predicate and does not present any new issues of safety or effectiveness.