(263 days)
Not Found
Yes
The document explicitly states that "Machine learning derived outputs are the volumes of the gray and white matter..." and mentions that "All machine learning derived outputs met the acceptance criteria."
No.
The device is intended for automatic labeling, visualization, volumetric and shape quantification of segmentable brain structures from MR images, automating the process of identifying, labeling, and quantifying the volume and shape of brain structures; it is not described as directly treating or diagnosing a condition.
Yes
Explanation: The device provides automated labeling, visualization, volumetric, and shape quantification of brain structures from MR images. It also provides a comparison of segmented volume to normative values, which helps medical professionals analyze patient conditions, making it a diagnostic tool.
Yes
The device description explicitly states it is software that performs automated image processing and segmentation of brain structures from MR images. It describes functional modules (DICOM Read, Segmentation, Visualization, Exporting) and output formats (overlay, 3D mesh, PDF report), all of which are software functionalities. There is no mention of accompanying hardware components that are part of the medical device itself.
Based on the provided information, yes, this device is an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use clearly states that the device is for "automatic labeling, visualization, volumetric and shape quantification of segmentable brain structures from a set of MR images." While it processes images, the output is quantitative data (volumes and shapes) derived from these images.
- Device Description: The description details the processing of T1-weighted MR images to "identify, labeling, and quantifying the volume and shape of subcortical structures." This involves analyzing biological samples (the patient's brain as captured by the MRI).
- Output: The device provides "segmented volume to normative values of brain structures based on reference data" and "comparison of segmented volume to normative values." This comparison of quantitative data derived from the patient's images to reference data is a key characteristic of IVDs used for diagnostic purposes.
- Performance Studies: The performance studies focus on metrics like Dice coefficient and relative volume difference, which are used to assess the accuracy and reproducibility of the quantitative measurements derived from the images.
- Predicate Device: The listed predicate device, K061855 CorTech Labs, Inc. - NeuroQuant, is also an IVD that performs similar quantitative analysis of brain structures from MRI images.
While the device processes images, its primary function is to extract quantitative data (volumes and shapes) from those images for the purpose of comparison and analysis related to brain structures. This aligns with the definition of an IVD, which is used to examine specimens derived from the human body to provide information for diagnostic purposes.
No
The provided text does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The 'Control Plan Authorized (PCCP)' field is marked 'Not Found'.
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.
Product codes
OIH
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.
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.
Mentions image processing
The ClearPoint Maestro™ Brain Model provides automated image processing of brain structures from T1-weighted MR images.
Mentions AI, DNN, or ML
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.
Input Imaging Modality
T1-weighted MR images
Anatomical Site
Brain
Indicated Patient Age Range
Not Found
Intended User / Care Setting
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.
Description of the training set, sample size, data source, and annotation protocol
The training data was created by the three technical experts at Philips Research Hamburg.
Description of the test set, sample size, data source, and annotation protocol
Validation was performed in 101 subjects from the validation dataset, which was completely independent from the training data created by Philips.
Summary of Performance Studies
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 0.7, relative volume difference
§ 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.
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 (DICE@fda.hhs.gov) or phone (1-800-638-2041 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) |
|---|
| Over-The-Counter Use (21 CFR 801 Subpart C) |
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510(k) Summary
ClearPoint Neuro's ClearPoint Maestro™ Brain Model
Submitter
ClearPoint Neuro. Inc. 5 Musick Irvine, CA 92618 Phone: (888) 287-9109 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