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510(k) Data Aggregation
(108 days)
OsteoDetect analyzes wrist radiographs using machine learning techniques to identify and highlight distal radius fractures during the review of posterior-anterior (PA) and lateral (LAT) radiographs of adult wrists.
OsteoDetect is a software device designed to assist clinicians in detecting distal radius fractures during the review of posterior-anterior (PA) and lateral (LAT) radiographs of adult wrists. The software uses deep learning techniques to analyze wrist radiographs (PA and LAT views) for distal radius fracture in adult patients.
1. Table of Acceptance Criteria and Reported Device Performance
Standalone Performance
| Performance Metric | Acceptance Criteria (Implicit) | Reported Device Performance (Estimate) | 95% Confidence Interval |
|---|---|---|---|
| AUC of ROC | High | 0.965 | (0.953, 0.976) |
| Sensitivity | High | 0.921 | (0.886, 0.946) |
| Specificity | High | 0.902 | (0.877, 0.922) |
| PPV | High | 0.813 | (0.769, 0.850) |
| NPV | High | 0.961 | (0.943, 0.973) |
| Localization Accuracy (average pixel distance) | Small | 33.52 pixels | Not provided for average distance itself, but standard deviation of 30.03 pixels. |
| Generalizability (AUC for all subgroups) | High | ≥ 0.926 (lowest subgroup - post-surgical radiographs) | Not explicitly provided for all, but individual subgroup CIs available in text. |
MRMC (Reader Study) Performance - Aided vs. Unaided Reads
| Performance Metric | Acceptance Criteria (Implicit: Superiority of Aided) | Reported Device Performance (OD-Aided) | Reported Device Performance (OD-Unaided) | 95% Confidence Interval (OD-Aided) | 95% Confidence Interval (OD-Unaided) | p-value for difference |
|---|---|---|---|---|---|---|
| AUC of ROC | AUC_aided - AUC_unaided > 0 | 0.889 | 0.840 | Not explicitly given for AUCs themselves, but difference CI: (0.019, 0.080) | Not explicitly given for AUCs themselves, but difference CI: (0.019, 0.080) | 0.0056 |
| Sensitivity | Superior Aided | 0.803 | 0.747 | (0.785, 0.819) | (0.728, 0.765) | Not explicitly given for individual metrics, but non-overlapping CIs imply significance. |
| Specificity | Superior Aided | 0.914 | 0.889 | (0.903, 0.924) | (0.876, 0.900) | Not explicitly given for individual metrics, but non-overlapping CIs imply significance. |
| PPV | Superior Aided | 0.883 | 0.844 | (0.868, 0.896) | (0.826, 0.859) | Not explicitly given for individual metrics, but non-overlapping CIs imply significance. |
| NPV | Superior Aided | 0.853 | 0.814 | (0.839, 0.865) | (0.800, 0.828) | Not explicitly given for individual metrics, but non-overlapping CIs imply significance. |
2. Sample Size and Data Provenance for Test Set
Standalone Performance Test Set:
- Sample Size: 1000 images (500 PA, 500 LAT)
- Data Provenance: Retrospective. Randomly sampled from an existing validation database of consecutively collected images from patients receiving wrist radiographs at the (b) (4) from November 1, 2016 to April 30, 2017. The study population included images from the US.
MRMC (Reader Study) Test Set:
- Sample Size: 200 cases.
- Data Provenance: Retrospective. Randomly sampled from the same validation database used for the standalone performance study. The data includes cases from the US.
3. Number of Experts and Qualifications for Ground Truth
Standalone Performance Test Set and MRMC (Reader Study) Test Set:
- Number of Experts: Three.
- Qualifications: U.S. board-certified orthopedic hand surgeons.
4. Adjudication Method for Test Set
Standalone Performance Test Set:
- Adjudication Method (Binary Fracture Presence/Absence): Majority opinion of at least 2 of the 3 clinicians.
- Adjudication Method (Localization - Bounding Box): The union of the bounding box of each clinician identifying the fracture.
MRMC (Reader Study) Test Set:
- Adjudication Method: Majority opinion of three U.S. board-certified orthopedic hand surgeons. (Note: this was defined on a per-case basis, considering PA, LAT, and oblique images if available).
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was an MRMC study done? Yes.
- Effect Size (Improvement of Human Readers with AI vs. without AI assistance):
- The least squares mean difference between the AUC for OsteoDetect-aided and OsteoDetect-unaided reads is 0.049 (95% CI, (0.019, 0.080)). This indicates a statistically significant improvement in diagnostic accuracy (AUC) of 4.9 percentage points when readers were aided by OsteoDetect.
- Sensitivity: Improved from 0.747 (unaided) to 0.803 (aided), an improvement of 0.056.
- Specificity: Improved from 0.889 (unaided) to 0.914 (aided), an improvement of 0.025.
6. Standalone (Algorithm Only) Performance Study
- Was a standalone study done? Yes.
7. Type of Ground Truth Used
Standalone Performance Test Set:
- Type of Ground Truth: Expert consensus (majority opinion of three U.S. board-certified orthopedic hand surgeons).
MRMC (Reader Study) Test Set:
- Type of Ground Truth: Expert consensus (majority opinion of three U.S. board-certified orthopedic hand surgeons).
8. Sample Size for Training Set
The document does not explicitly state the sample size for the training set. It mentions "randomly withheld subset of the model's training data" for setting the operating point, implying a training set existed, but its size is not provided.
9. How Ground Truth for Training Set Was Established
The document does not explicitly state how the ground truth for the training set was established. It only refers to a "randomly withheld subset of the model's training data" during the operating point setting.
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