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510(k) Data Aggregation
(263 days)
ClearPoint Maestro Brain Model
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.
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.
Here's a breakdown of the acceptance criteria and study details for the ClearPoint Maestro™ Brain Model, based on the provided FDA 510(k) summary:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
Segmentation Accuracy: | |
Dice coefficient >0.7 | Met: Mean Dice coefficients for 21 segmented brain structures in 101 subjects were significantly greater than 70%. The only exception was the third ventricle, attributed to manual labeling variability rather than device performance. |
Relative volume difference 70% and mean relative volume differences |
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