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510(k) Data Aggregation
(193 days)
QP-Prostate**®** CAD
QP-Prostate® CAD is a Computed Aided Detection and Diagnosis (CADe/CADx) image processing software that automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI. The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Patient management decisions should not be based solely on the results of QP-Prostate® CAD.
QP-Prostate® CAD is an artificial intelligence-based Computed Aided Detection and Diagnosis (CADe/CADx) image processing software. QP-Prostate® CAD uses Al-based algorithms trained with pathology data to detect suspicious lesions for clinically significant prostate cancer. The device automatically detects and identifies suspected lesions in the prostate gland based on bi-parametric prostate MRI and provides marks over regions of the images that may contain suspected lesions. There are two possible markers that are provided in different colors suggesting different levels of suspicion of clinically significant prostate cancer (moderate or high suspicion level).
The software is intended to be used as a concurrent read by physicians with proper training in a clinical setting as an aid for interpreting prostate MRI studies. The results can be displayed in a variety of DICOM outputs, including identified suspected lesions marked as an overlay onto source MR images. The output can be displayed on third-party DICOM workstations and Picture Archive and Communication Systems (PACS). Based on biparametric input consisting of T2W and DWI series, the analysis is run automatically, and the output in standard DICOM formats is returned to PACS.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
Acceptance Criteria and Reported Device Performance
Table 1: Acceptance Criteria and Reported Device Performance (Standalone)
| Metric (lesion level) | Acceptance Criterion (Implicit) | Reported Device Performance |
|---|---|---|
| AUC-ROC | Evidence of good discriminatory ability (e.g., above a certain threshold) | 0.732 (95% CI: 0.668-0.791) |
| Sensitivity (high suspicion marker) | Evidence of good detection rate for clinically significant findings | 0.677 (95% CI: 0.593-0.761) |
| False Positive Rate per Case (high suspicion marker, any biopsy source) | Evidence of acceptable false positive rate | 0.417 (95% CI: 0.313-0.522) |
| Sensitivity (high and moderate suspicion markers) | Evidence of good detection rate for clinically significant findings | 0.795 (95% CI: 0.722-0.861) |
| False Positive Rate per Case (high and moderate suspicion markers, any biopsy source) | Evidence of acceptable false positive rate | 0.855 (95% CI: 0.709-0.996) |
Note: The document does not explicitly state numerical acceptance criteria thresholds for the standalone performance metrics (AUC-ROC, Sensitivity, FPR). Instead, it presents the results and implies that these values "demonstrate the safety and effectiveness" in comparison to the predicate device. The general implicit acceptance criterion for these metrics would be that they exhibit performance levels indicative of a useful diagnostic aid.
Table 2: Acceptance Criteria and Reported Device Performance (Multi-Reader Multi-Case Study)
| Metric | Acceptance Criterion (Explicit) | Reported Device Performance |
|---|---|---|
| ΔAUC (AUCaided - AUCunaided) (Primary Endpoint) | A statistically significant improvement (p-value |
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