(53 days)
Not Found
Yes
The device description explicitly states that the processing architecture includes a "proprietary automated internal pipeline based on machine learning tools."
No.
This device is a software tool for automated analysis and quantification of brain structures from MR images, intended to support clinicians in diagnosis and assessment, rather than directly treat or mitigate a disease or condition.
Yes
The device is a support tool for clinicians in the assessment of low-field structural MRIs, providing automatic labeling, spatial measurement, and volumetric quantification of brain structures, which are all diagnostic functions.
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 in standard image formats for display on third-party systems, indicating it is a software application without dedicated hardware.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections. They are used outside of the body (in vitro).
- BrainInsight's Function: BrainInsight processes medical images (MR images) acquired from within the body. It analyzes these images to provide measurements and segmentations of brain structures. This is an in vivo process, not an in vitro one.
- Intended Use: The intended use clearly states it's for processing MR images to provide information about brain structures. It doesn't mention analyzing biological samples.
- Device Description: The description focuses on image processing, segmentation, and measurement from MR images.
- Input Modality: The input is MR images, not biological samples.
Therefore, 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 'Control Plan Authorized (PCCP) and relevant text' section explicitly states "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 overlays and reports.
Product codes (comma separated list FDA assigned to the subject device)
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 MR images. 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 high throughput capability makes the software suitable for use in routine patient care as a support tool for clinicians in assessment of low-field (0.064 T) structural MRIs. BrainInsight provides overlays and reports based on 0.064 T 3D MRI series of T1 Gray/White, T2-Fast, and FLAIR images.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
low-field MR images, 0.064 T 3D MRI series of T1 Gray/White, T2-Fast, and FLAIR images.
Anatomical Site
brain structures, whole brain, ventricle, Brain
Indicated Patient Age Range
Ages 2+, Ages 18+
Intended User / Care Setting
routine patient care as a support tool for clinicians
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-Gray/White contrast and T2, T2-Fast and FLAIR 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. All test images were acquired using Swoop software versions 8.3 and 8.4.
The test set had the following distribution:
| Category | Data Distribution |
|---|---|
| Age | >2 to 12 years - 20.6% >12 to 18 to 90 years - 70.6% |
| Gender | 33% F / 41% M / 25% Anonymized |
| Pathology | Stroke (Infarct) Hydrocephalus Hemorrhage (SAH, SDH, IVH, IPH) Mass/Edema Tumor |
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.)
Midline Shift Discrepancy (Model, Mean Annotator):
T1: Model 0.99, Mean Annotator 1.42
T2: Model 0.76, Mean Annotator 1.00
T2-Fast: Model 1.00, Mean Annotator 1.38
FLAIR: Model 0.90, Mean Annotator 1.21
Lateral Ventricle Left Discrepancy (Model, Mean Annotator):
T1: Model 0.17, Mean Annotator 0.18
T2: Model 0.20, Mean Annotator 0.24
T2-Fast: Model 0.16, Mean Annotator 0.18
FLAIR: Model 0.12, Mean Annotator 0.12
Lateral Ventricle Right Discrepancy (Model, Mean Annotator):
T1: Model 0.19, Mean Annotator 0.19
T2: Model 0.22, Mean Annotator 0.24
T2-Fast: Model 0.15, Mean Annotator 0.16
FLAIR: Model 0.13, Mean Annotator 0.13
Mean Absolute Error (Midline Shift):
T1 Error: 1.01 mm
T2 Error: 0.80 mm
T2-Fast Error: 0.89 mm
FLAIR Error: 0.75 mm
Mean Dice coefficient and Volume Differences (T1):
Left Ventricle: Dice Overlap Model 85%, Annotator 90%; Volume Differences Model 25%, Annotator 9%
Right Ventricle: Dice Overlap Model 83%, Annotator 90%; Volume Differences Model 26%, Annotator 11%
Whole Brain: Dice Overlap Model 95%, Annotator 97%; Volume Differences Model 3%, Annotator 2%
Mean Dice coefficient and Volume Differences (T2):
Left Ventricle: Dice Overlap Model 84%, Annotator 88%; Volume Differences Model 27%, Annotator 21%
Right Ventricle: Dice Overlap Model 82%, Annotator 87%; Volume Differences Model 26%, Annotator 20%
Whole Brain: Dice Overlap Model 96%, Annotator 97%; Volume Differences Model 5%, Annotator 5%
Mean Dice coefficient and Volume Differences (T2-Fast):
Left Ventricle: Dice Overlap Model 86%, Annotator 91%; Volume Differences Model 26%, Annotator 17%
Right Ventricle: Dice Overlap Model 86%, Annotator 92%; Volume Differences Model 23%, Annotator 13%
Mean Dice coefficient and Volume Differences (FLAIR):
Left Ventricle: Dice Overlap Model 89%, Annotator 93%; Volume Differences Model 9%, Annotator 7%
Right Ventricle: Dice Overlap Model 88%, Annotator 94%; Volume Differences Model 11%, Annotator 8%
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
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).
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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, 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.
December 16, 2022
Hyperfine, Inc. % Christine Kupchick Sr. Regulatory Specialist 351 New Whitfield St. GUILFORD CT 06437
Re: K223268
Trade/Device Name: BrainInsightTM Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: October 21, 2022 Received: October 24, 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
1
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.
D. Ryk
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
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Indications for Use
510(k) Number (if known) K223268
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 overlays 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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3
HYPERFINE
BrainInsight™ 510(k) SUMMARY K223268
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 |
Date Prepared:
December 13, 2022
Device Identification
| Trade Name: | BrainInsight™ |
|---|---|
| Common Name: | Automated Radiological Image 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 (K220815). 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 MR images. 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 high throughput capability makes the software suitable for use in routine patient care as a support tool for clinicians in assessment of low-field (0.064 T) structural MRIs. BrainInsight provides overlays and reports based on 0.064 T 3D MRI series of T1 Gray/White, T2-Fast, and FLAIR images.
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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.
Intended Patient Population
The table below shows the intended patient population.
| Application | Patient Population | T1 Gray/White | T2 | T2-Fast | FLAIR |
|---|---|---|---|---|---|
| Midline Shift | Ages 2+ | V | V | V | V |
| Lateral Ventricles | Ages 2+ | V | V | V | V |
| Whole Brain | Ages 18+ | V | V | N/A | N/A |
Technological Characteristics
The subject device has the same indications for use, fundamental technology, and operating principles, as the predicate (K220815). 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 (K220815) |
|---|---|---|
| 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 and pediatric (≥ 2 years) - Lateral | |
| ventricles and midline shift applicationsAdult (≥ 18 years) - Whole brain application | Adult (≥ 18 years) - Lateral ventricles, | |
| midline shift, and whole brain applications | ||
| Technology | Automated measurement of brain tissue | |
| volumes and structures of Al-reconstructed | ||
| low-field MR imagesAutomatic segmentation and quantification | ||
| of brain structures of Al-reconstructed low- | ||
| field MR images using machine learning | ||
| tools | Same | |
| 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 requiredOperates in a serverless cloud environmentUser interface through PACS (multiple | |
| vendors) | Same | |
| Operating System | Supports Linux | Same |
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| Processing
Architecture | Automated internal pipeline that performs:
• segmentation
• volume calculation
• distance measurement
• numerical information display | Same |
|----------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Data Source | • MRI Scanner: Hyperfine Swoop FSE MRI T1-
Gray/White Contrast, T2, T2-Fast and FLAIR
scans acquired with specified protocols
• Supports DICOM format as input | • MRI Scanner: Hyperfine Swoop FSE
MRI T1 and T2 scans acquired with
specified protocols
• 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 PACS | Same |
| Safety | Automated quality control functions:
• Tissue contrast check
• Scan protocol verification
• Atlas alignment check
• Results must be reviewed by a trained
physician
• LV segmentation output quality check | Automated quality control functions:
• Tissue contrast check
• Scan protocol verification
• Atlas alignment check
• Results must be reviewed by a trained
physician |
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 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-Gray/White contrast and T2, T2-Fast and FLAIR 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 Swoop software versions 8.3 and 8.4. The test set had the following distribution:
6
| Category | Data Distribution |
|---|---|
| Age | >2 to 12 years - 20.6% >12 to 18 to 90 years - 70.6% |
| Gender | 33% F / 41% M / 25% Anonymized |
| Pathology | Stroke (Infarct) Hydrocephalus Hemorrhage (SAH, SDH, IVH, IPH) Mass/Edema Tumor |
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.
| Midline Shift
| Discrepancy | T1 | T2 | T2-Fast | FLAIR |
|---|---|---|---|---|
| Model | 0.99 | 0.76 | 1.00 | 0.90 |
| Mean Annotator | 1.42 | 1.00 | 1.38 | 1.21 |
| Lateral Ventricle Left
| Discrepancy | T1 | T2 | T2-Fast | FLAIR |
|---|---|---|---|---|
| Model | 0.17 | 0.20 | 0.16 | 0.12 |
| Mean Annotator | 0.18 | 0.24 | 0.18 | 0.12 |
| Lateral Ventricle Right
| Discrepancy | T1 | T2 | T2-Fast | FLAIR |
|---|---|---|---|---|
| Model | 0.19 | 0.22 | 0.15 | 0.13 |
| Mean Annotator | 0.19 | 0.24 | 0.16 | 0.13 |
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.
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| Application | T1 Error | T2 Error | T2-Fast Error | FLAIR Error |
|---|---|---|---|---|
| Midline Shift | 1.01 mm | 0.80 mm | 0.89 mm | 0.75 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 | 85 | 90 | 25 | 9 |
| Right Ventricle | 83 | 90 | 26 | 11 |
| Whole Brain | 95 | 97 | 3 | 2 |
| Application | Dice Overlap [%] | Volume Differences [%] | ||
|---|---|---|---|---|
| T2 | Device | Annotator | Device | Annotator |
| Left Ventricle | 84 | 88 | 27 | 21 |
| Right Ventricle | 82 | 87 | 26 | 20 |
| Whole Brain | 96 | 97 | 5 | 5 |
| Application | Dice Overlap [%] | Volume Differences [%] | ||
|---|---|---|---|---|
| T2-Fast | Device | Annotator | Device | Annotator |
| Left Ventricle | 86 | 91 | 26 | 17 |
| Right Ventricle | 86 | 92 | 23 | 13 |
| Application | Dice Overlap [%] | Volume Differences [%] | ||
|---|---|---|---|---|
| FLAIR | Device | Annotator | Device | Annotator |
| Left Ventricle | 89 | 93 | 9 | 7 |
| Right Ventricle | 88 | 94 | 11 | 8 |
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The test results show high accuracy of BrainInsight performance as compared to the reference and annotators and the subject device met all acceptance criteria.
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.