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510(k) Data Aggregation
(116 days)
Alzevita is intended for use by neurologists and radiologists experienced in the interpretation and analysis of brain MRI scans. It enables automated labelling, visualization, and volumetric measurement of the hippocampus from high-resolution T1-weighted MRI images. The software facilitates comparison of hippocampal volume against a normative dataset derived from MRI scans of healthy control subjects aged 55 to 90 years, acquired using standardized imaging protocols on 1.5T/3T MRI scanners.
Alzevita is a cloud-based, AI-powered medical image processing software as a medical device intended to assist neurologists and radiologists with expertise in the analysis of 3D brain MRI scans. The software performs fully automated segmentation and volumetric quantification of the hippocampus, a brain structure involved in memory and commonly affected by neurodegenerative conditions.
Alzevita is designed to replace manual hippocampal segmentation workflows with a fast, reproducible, and standardized process. It provides quantitative measurements of hippocampal volume, enabling consistent outputs that can assist healthcare professionals in evaluating structural brain changes.
The software operates through a secure web interface and is compatible with commonly used operating systems and browsers. It accepts 3D MRI scans in DICOM or NIfTI format and displays the MRI image in the MRI viewer allowing trained healthcare professionals to view, zoom, and analyze the MRI scan alongside providing a visual and tabular volumetric analysis report.
The underlying algorithm used in Alzevita is locked, meaning it does not modify its behavior at runtime or adapt to new inputs. This ensures consistent performance and reproducibility of results across users and imaging conditions. Any future modifications to the algorithm including performance updates or model re-training will be submitted to the FDA for review and clearance prior to deployment, in compliance with FDA regulatory requirements and applicable guidance for AI/ML-based SaMD.
Here's a detailed description of the acceptance criteria and the study proving the Alzevita device meets those criteria, based on the provided FDA 510(k) clearance letter:
Acceptance Criteria and Device Performance
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria | Reported Device Performance (Alzevita 95% Confidence Intervals) | Criteria (Pass/Fail) |
|---|---|---|---|
| Overall Dice Score | ≥ 75% | (0.85, 0.86) | Pass |
| Overall Hausdorff Distance | ≤ 6.1 mm | (1.43, 1.59) | Pass |
| Overall Correlation Coefficient | ≥ 0.82 | Not explicitly given as CI, but stated as met | Pass |
| Overall Relative Volume Difference | ≤ 24.6% | Not explicitly given as CI, but stated as met | Pass |
| Overall Bland-Altman Mean Difference (Total Hippocampus Volume) | ≤ 1010 mm³ | Not explicitly given as CI, but stated as met | Pass |
| Subgroup Dice Score (Clinical Subgroups) | ≥ 83% (implied from results) | Control: (0.87, 0.88)MCI: (0.84, 0.85)AD: (0.82, 0.84) | Pass |
| Subgroup Hausdorff Distance (Clinical Subgroups) | ≤ 3 mm (implied from results) | Control: (1.32, 1.41)MCI: (1.44, 1.62)AD: (1.48, 2.10) | Pass |
| Subgroup Dice Score (Gender) | ≥ 83% (implied) | Female: (0.85, 0.87)Male: (0.84, 0.86) | Pass |
| Subgroup Hausdorff Distance (Gender) | ≤ 3 mm (implied) | Female: (1.40, 1.57)Male: (1.41, 1.66) | Pass |
| Subgroup Dice Score (Magnetic Field Strength) | ≥ 83% (implied) | 3T: (0.86, 0.87)1.5T: (0.83, 0.85) | Pass |
| Subgroup Hausdorff Distance (Magnetic Field Strength) | ≤ 3 mm (implied) | 3T: (1.38, 1.47)1.5T: (1.45, 1.79) | Pass |
| Subgroup Dice Score (Slice Thickness) | ≥ 83% (implied) | 1 mm: (0.87, 0.88)1.2 mm: (0.84, 0.85) | Pass |
| Subgroup Hausdorff Distance (Slice Thickness) | ≤ 3 mm (implied) | 1 mm: (1.35, 1.43)1.2 mm: (1.47, 1.72) | Pass |
| Subgroup Dice Score (US Geographical Region) | ≥ 83% (implied) | East US: (0.84, 0.86)West US: (0.85, 0.87)Central US: (0.85, 0.87)Canada: (0.82, 0.88) | Pass |
| Subgroup Hausdorff Distance (US Geographical Region) | ≤ 3 mm (implied) | East US: (1.44, 1.71)West US: (1.35, 1.55)Central US: (1.35, 1.47)Canada: (1.07, 2.34) | Pass |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: 298 subjects.
- Data Provenance: The test set data was collected from the publicly available ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset. It is retrospective and sampled using stratified random sampling, with subjects recruited from ADNI 1 & ADNI 3 datasets.
- Geographical Distribution: Approximately equal geographical distribution within the USA (East coast, Central US regions, West coast) and Canada.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- Number of Experts: Three certified radiologists.
- Qualifications of Experts: They are described as "certified radiologists in India, adhering to widely recognized and standardized segmentation protocols." Specific experience level (e.g., years of experience) is not provided.
4. Adjudication Method for the Test Set
- Adjudication Method: A consensus ground truth was established by integrating individual delineations from the three certified radiologists into a single consensus mask for each case. This integration was performed using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was a MRMC study done? No, the document describes a standalone performance evaluation of the Alzevita algorithm against a consensus ground truth. There is no mention of a human-in-the-loop study comparing human readers with and without AI assistance.
- Effect size of human readers improvement: Not applicable, as no MRMC study was conducted.
6. Standalone Performance Study
- Was a standalone performance study done? Yes. The entire validation study described evaluates the Alzevita algorithm's performance in segmenting the hippocampus and calculating its volume against a ground truth, without human intervention in the segmentation process.
7. Type of Ground Truth Used
- Type of Ground Truth: Expert consensus. Specifically, it was established through manual segmentation by three certified radiologists, with their individual segmentations integrated via the STAPLE algorithm. This STAPLE-derived consensus mask served as the ground truth.
8. Sample Size for the Training Set
- Sample Size for Training Set: 200 cases.
9. How the Ground Truth for the Training Set Was Established
- Training Set Ground Truth Establishment: "Expert radiologists manually segmented the hippocampus to create the ground truth, which is then used as input for training the Alzevita segmentation model." The number and specific qualifications of the expert radiologists for the training set's ground truth are not detailed beyond "expert radiologists." There is no mention of an adjudication method like STAPLE for the training set ground truth, suggesting individual expert segmentation or an unspecified consensus process.
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