(359 days)
The CorInsights MRI Medical Image Processing Software is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from MRI images. Volumetric measurements are compared to reference percentile data. CorInsights MRI is for adults age 45 to 95.
CorInsights MRI is a fully automated MR medical image processing software intended for automatic labeling, visualization and volumetric quantification of identifiable brain structures from DICOM formatted magnetic resonance images. The resulting output consists of a pdf report for review, which can be used in research and clinical use, and a DICOM image showing the anatomical structure boundaries identified by the software. The proposed device provides morphometric measurements based on T1 MRI series. The output of this software only device includes morphometric reports that provide comparison of measured volumes to age and gender-matched reference data and an image volume that has been annotated with color overlays representing each segmented region. The architecture has a proprietary automated internal process that includes artifact correction, atlas-based segmentation, volume calculation, and report generation. Quality control measures include automated quality control including image header checks to verify that the scan acquisition protocol and provided data adhere to system requirements, an image morphometry check, a tissue contrast check, and value range checks.
Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document doesn't explicitly state "acceptance criteria" as a separate, pre-defined column with specific numeric thresholds. Instead, it describes accuracy and reproducibility goals in terms of achieving certain statistical measures (ICC, DICE coefficient, mean absolute percentage difference) against ground truth or in test-retest scenarios.
I will infer the "acceptance criteria" based on the reported "Performance" values, as these are the results presented to demonstrate the device's capability against known ground truths or expected variations.
| Category | Specific Measure | Acceptance Criteria (Implied from Reported Performance) | Reported Device Performance |
|---|---|---|---|
| Accuracy | Hippocampal Volume | ||
| IntraClass Correlation (ICC) | ICC >= 0.95 (Highly correlated with ground truth) | 0.95 | |
| DICE Coefficient (Left) | DICE >= 83% (Good overlap with ground truth) | 83% (SD 2.5%) | |
| DICE Coefficient (Right) | DICE >= 83% (Good overlap with ground truth) | 83% (SD 2.7%) | |
| Mean Absolute % Difference (Left) | MAD = 0.99 (Highly correlated with ground truth) | 0.99 | |
| DICE Coefficient | DICE >= 95% (Excellent overlap with ground truth) | 95% (SD 1.6%) | |
| Mean Absolute % Difference | MAD = 0.89 (Strong correlation with ground truth) | 0.89 | |
| DICE Coefficient | DICE >= 95% (Excellent overlap with ground truth) | 95% (SD 1.1%) | |
| Mean Absolute % Difference | MAD = 0.98 (Very strong correlation with ground truth) | 0.98 (left and right) | |
| DICE Coefficient (Left) | DICE >= 88% (Good overlap with ground truth) | 88% (SD 5.2%) | |
| DICE Coefficient (Right) | DICE >= 87% (Good overlap with ground truth) | 87% (SD 5.6%) | |
| Mean Absolute % Difference (Left) | MAD = 0.97 (Very high test-retest consistency) | 0.97 | |
| DICE Coefficient | DICE >= 89% (Good test-retest overlap) | 89% (SD 4.0%) | |
| Mean Absolute % Difference Range (Across all volumes) | MAD (range) 0.7-5.8%, Average |
§ 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).