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Brain WMH is intended for automatic labeling, visualization, and volumetric quantification of WMH from T2w FLAIR MR images. The output consists of segmentations, visualizations and volumetric measurements of WMH. It is intended to provide the trained medical professional with complementary information for the evaluation and assessment of MR brain images and to aid the trained medical professional in quantitative reporting.

Brain WMH is a software as a medical device (SaMD) that provides automatic quantification of white matter hyperintensities (WMHs) based on magnetic resonance (MR) images to assist trained medical professionals. The device takes fluid-attenuated inversion recovery (FLAIR) MR images as input. Its output consists of a report in DICOM encapsulated pdf and DICOM secondary capture format, and DICOM secondary captures of the segmentations as color overlay on the input image.

Here's a breakdown of the acceptance criteria and study details for the Quantib Brain WMH device, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance

Note: The document indicates that the device's standalone segmentation performance (Dice coefficient) was higher than the predicate device's standalone performance, further suggesting it met or exceeded expectations.

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set Sample Size: 110 studies from 90 patients.
   • Data Provenance: Multiple clinical sites, with the majority of data acquired in the United States. The data was retrospective, collected from ethnically diverse male and female adult patients.
   • Scan-Rescan Reproducibility Test Set: A separate, unspecified number of short-interval, same-subject scan pairs.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:

   • Ground Truth Experts: Three experts.
   • Adjudicator: One expert.
   • Qualifications: The document states "Three experts served as truthers and one expert as an adjudicator," but does not specify their qualifications (e.g., "radiologist with 10 years of experience").

3. Adjudication Method for the Test Set:

   • The document states: "The ground truth process included multiple experts. Three experts served as truthers and one expert as an adjudicator." This implies a 3+1 adjudication method, where three experts initially define the ground truth, and a fourth expert adjudicates any discrepancies or provides a final decision.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • An MRMC study was not explicitly mentioned for evaluating human reader improvement with AI assistance. The performance testing section primarily focuses on standalone performance and comparison to the predicate device's standalone performance.
   • No effect size for human reader improvement with AI vs. without AI assistance is reported.

5. Standalone Performance (Algorithm-Only) Study:

   • Yes, a standalone performance study was done. The document explicitly states: "The standalone performance of Brain WMH segmentation, as measured by Dice coefficient (0.58 ± 0.21) was higher than the standalone performance of the predicate device and fell within the range of interobserver variability."
   • Further, "the anatomical location labeling performance was also within the range of the interobserver performance."

6. Type of Ground Truth Used:

   • Expert Consensus/Labeling: The ground truth for the test set was established through "multiple experts," specifically "Three experts served as truthers and one expert as an adjudicator." This indicates an expert consensus approach to annotation.

7. Sample Size for the Training Set:

   • Training Set Sample Size: 474 T2-weighted FLAIR scans.

8. How the Ground Truth for the Training Set was Established:

   • The document states that the WMH segmentation model was "trained with 474 T2-weighted FLAIR scans." However, it does not explicitly describe the method used to establish the ground truth for this training set. It only mentions the data was "collected from different geographical areas, with the majority of the data acquired in the United States."

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