ClearPoint Maestro Brain Model

K213645 · ClearPoint Neuro, Inc. · QIH · Aug 8, 2022 · Radiology

Device Facts

• Record ID: K213645
• Device Name: ClearPoint Maestro Brain Model
• Applicant: ClearPoint Neuro, Inc.
• Product Code: QIH · Radiology
• Decision Date: Aug 8, 2022
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 892.2050
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device

Intended Use

ClearPoint Maestro™ Brain Model is intended for automatic labeling, visualization, volumetric and shape quantification of segmentable brain structures from a set of MR images. This software is intended to automate the process of identifying, labelling, and quantifying the volume and shape of brain structures visible in MRI images.

Device Story

ClearPoint Maestro™ Brain Model is a software-based medical image management and processing system. It accepts T1-weighted MR images as input. The device uses shape-constrained segmentation for sub-cortical regions and voxel-wise tissue parcellation for hemispheres and cerebellum to identify, label, and quantify brain structures. It produces color-coded overlays on MR images, 3D triangular mesh representations, and PDF reports comparing volumes to normative reference data. Used in clinical settings by radiologists, neurologists, and neuroradiologists to simplify MRI segmentation workflows. The output assists clinicians in assessing brain structure volumes, potentially aiding in clinical decision-making by providing standardized, automated measurements.

Clinical Evidence

Bench testing only. Validation performed on 101 subjects independent of training data. Primary endpoints: segmentation accuracy (Dice coefficient >0.7, relative volume difference <0.3) and reproducibility (absolute volume difference <15%). Results: Mean Dice coefficients for 21 structures were >70%; mean relative volume differences were <0.3. Reproducibility analysis on 20 repeated scans showed absolute volume differences <10% for all regions. Machine learning-derived outputs (cerebellum/hemisphere GM/WM) met all accuracy criteria.

Technological Characteristics

Software-based image processing system. Inputs: T1-weighted MRI. Processing: Shape-constrained segmentation and voxel-wise tissue parcellation. Outputs: DICOM overlays, 3D triangular meshes, PDF reports. Connectivity: Standard DICOM integration. Algorithm: Machine learning-based for specific tissue types (cerebellum/hemispheres) and shape-constrained for sub-cortical regions.

Indications for Use

Indicated for use by medical professionals (radiologists, neurologists, neuroradiologists) to automate the identification, labeling, and volumetric/shape quantification of brain structures from T1-weighted MRI scans.

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 (K061855)

Submission Summary (Full Text)

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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.

August 8, 2022

ClearPoint Neuro, Inc. % John J. Smith Partner Hogan Lovells US LPP 555 Thirteenth St., NW WASHINGTON DC 20004

Re: K213645

Trade/Device Name: ClearPoint Maestro™ Brain Model Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: OIH Dated: June 16, 2022 Received: June 16, 2022

Dear John Smith:

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

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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 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 ([e-mail]) or phone [phone] or 301-796-7100).

Sincerely,

for

Michael D. O'Hara, Ph.D. Deputy Director 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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510(k) Number (if known) K213645

#### Device Name

#### ClearPoint Maestro™ Brain Model

#### Indications for Use (Describe)

ClearPoint Maestro™ Brain Model is intended for automatic labeling, visualization, volumetric and shape quantification of segmentable brain structures from a set of MR images. This software is intended to automate the process of identifying, labelling, and quantifying the volume and shape of brain structures visible in MRI images.

Type of Use (Select one or both, as applicable)

| X Prescription Use (Part 21 CFR 801 Subpart D) |
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| Over-The-Counter Use (21 CFR 801 Subpart C) |

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K213645

# 510(k) Summary

## ClearPoint Neuro's ClearPoint Maestro™ Brain Model

### Submitter

ClearPoint Neuro. Inc. 5 Musick Irvine, CA 92618 Phone: [phone] Contact Person: Megan Faulkenberry, VP, Quality and Regulatory

Date Prepared: August 5, 2022

Name of Device: ClearPoint Maestro™ Brain Model

Common or Usual Name: System, Image Processing, Radiological

Classification Name: Medical Image Management and Processing System (892.2050)

Regulatory Class: Class II

Product Code: QIH

### Predicate Devices

CorTech Labs, Inc. - NeuroQuant (K061855)

#### Device Description

The ClearPoint Maestro™ Brain Model provides automated image processing of brain structures from T1-weighted MR images. Specifically, the device automates the manual process of identifying, labeling, and quantifying the volume and shape of subcortical structures to simplify the workflow for MRI segmentation.

The ClearPoint Maestro™ Brain Model consists of the following key functional modules.

- DICOM Read Module .
- Segmentation Module ●
- Visualization Module ●
- . Exporting Module

The segmented brain structures are color coded and overlayed onto the MR images or be displayed as 3-D triangular mesh representation. The viewing capabilities of the device also provides anatomic orientation labels (left, right, inferior, superior, anterior, posterior), image slice selection, standard image manipulation such as contrast adjustment, rotation, zoom, and the ability to adjust the transparency of the image overlay.

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The output from ClearPoint Maestro™ Brain Model can also exported as a report in PDF format. The report also provides a comparison of segmented volume to normative values of brain structures based on reference data.

# Intended Use / Indications for Use

ClearPoint Maestro™ Brain Model is intended for automatic labeling, visualization, volumetric and shape quantification of segmentable brain structures from a set of MR images. This software is intended to automate the process of identifying, labelling, and quantifying the volume and shape of brain structures visible in MRI images.

## Comparison of Technological Characteristics with Predicate Device

Both ClearPoint Maestro™ Brain Model and NeuroQuant automate MR image post-processing to provide labeling, visualization, and volumetric quantification of brain structures. Both systems use as input clinical T1-weighted brain MR scans and automatically provide quantification of brain structures. The output results are provided in DICOM format and provide the segmented structures as a color-coded overlay onto the image series. The results can also be exported within a report as a PDF.

There are minor technological differences between the ClearPoint Maestro™ Brain Model and NeuroQuant. Notably, NeuroQuant performs automatic segmentation and quantification of brain structures using a probabilistic neuroanatomical atlas based on MR image intensity, while ClearPoint Maestro™ Brain Model uses shape-constrained segmentation of sub-cortical brain regions, the hemispheres, and the cerebellum, followed by voxel-wise tissue parcellation of the hemispheres and the cerebellum into tissue types. However, based on the assessment of device performance, these minor differences do not affect safety and effectiveness.

Both ClearPoint Maestro™ Brain Model and NeuroQuant are intended to be used by medical professionals such as radiologists, neurologists, and neuroradiologists as a tool to aid in the assessment and the simplification of the workflow for MR image segmentation.

### Performance Data

The segmentation accuracy and reproducibility of ClearPoint Maestro™ Brain Model was assessed by comparing the device output to manually labeled data T1-weighted MRI data. The segmentation accuracy success was defined as Dice coefficient >0.7 and relative volume difference <0.3 between the device output and manually labeled data for the validation of the validation data. The reproducibility success criteria were defined as absolute volume differences <15% between segmentation performed on two repeated scans.

The means of computed Dice coefficients for 21 segmented brain structures in 101 subjects from the validation dataset were significantly greater than 70%, meeting the acceptance criteria. This result was observed in both the full population and in all subgroups. The only region with a mean Dice coefficient less than 70% was the third ventricle, which was attributed to variability in the manual labeling rather than device performance. This was further supported by the ClearPoint Maestro™ Brain Model reproducibility results, which showed that the device provided results while the manual labeling showed large variability.

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The means of computed relative volume differences for 21 segmented brain structures in 101 subjects from the validation dataset were significantly less than 0.3, meeting the acceptance criteria. This result was observed in the full population and in all subgroups except one (18-25yo) where the left and right lateral ventricles were with higher means. That was again attributed to variability in the manual labeling rather than device performance.

In the reproducibility analysis, absolute volume differences using ClearPoint Maestro Brain Model 1.0 to segment the repeated scans in the 20Repeat data set were less than 10% for all segmented brain regions, including the third ventricle.

Machine learning derived outputs are the volumes of the gray and white matter in the cerebellum and the left and right cerebral hemispheres: Cerebellum GM, Cerebellum WM, L Cortical GM, and R Cortical GM, L Cortical WM, R Cortical WM. The training data was created by the three technical experts at Philips Research Hamburg. Validation was performed in 101 subjects from the validation dataset, which was completely independent from the training data created by Philips. All machine learning derived outputs met the acceptance criteria. Their Dice coefficient was greater than 70% and their mean relative volume difference was less than 0.3 in both the full population and in all subgroups.

Based on the bench testing performance, the ClearPoint Maestro™ Brain Model has a safety and effectiveness profile that is similar to the predicate device.

# Conclusion

The ClearPoint Maestro™Brain Model has the same intended uses and similar indications, technological characteristics, and principles of operation as its predicate device. The minor differences in indications do not alter the intended use of the device and do not affect its safety and effectiveness when used as labeled. In addition, the minor technological differences between the ClearPoint Maestro™ Brain Model and its predicate devices raise no new issues of safety or effectiveness. Performance data demonstrate that the ClearPoint Maestro™ Brain Model is as safe and effective as NeuroQuant. Thus, the ClearPoint Maestro™ Brain Model is substantially equivalent.