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The Mitek RapidLoc™ Meniscal Repair System is intended for use in the arthroscopic fixation of longitudinal vertical meniscus lesions (bucket-handle lesions) located in the vascularized area of the meniscus (red-red and red-white zones).

The Mitek RapidLoc™ Meniscal Repair System described in this 510(k) is a sterile, two I router Description: "Ho witting of a molded PLA Backstop and Tophat.

This 510(k) premarket notification describes a medical device, not an AI/ML device. Therefore, the requested information regarding acceptance criteria, study details, ground truth, and training data, which are typically associated with performance validation of AI/ML models, is not applicable in this context.

The provided document focuses on demonstrating substantial equivalence of the Mitek RapidLoc™ Meniscal Repair System to predicate devices through performance testing and similarities in design, operating principle, and biocompatibility.

Here's a breakdown of the relevant information from the document, tailored to a non-AI/ML medical device submission:

1. A table of acceptance criteria and the reported device performance:

The document does not explicitly state numerical "acceptance criteria" against which the device performance is measured in a quantitative table format. Instead, it indicates that "Safety and Performance data has been provided to support substantial equivalence." The reported performance is in comparison to predicate devices.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

The document does not specify the sample sizes used for the performance testing (strength and suture comparisons) nor does it provide details on data provenance (country of origin, retrospective/prospective). This type of detail is typically found in the full testing reports or study protocols, not usually in the 510(k) summary itself.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

This question is not applicable. For a physical medical device like a meniscal repair system, "ground truth" as it relates to expert consensus for diagnostic interpretation is not relevant. Performance testing involves objective measurements of physical properties (e.g., strength).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

This question is not applicable. Adjudication methods are typically employed for subjective evaluations or consensus-building in clinical assessments, not for objective mechanical performance testing of a physical device.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

This question is not applicable. The device is a surgical implant, not an imaging or diagnostic aid that would involve human "readers" or AI assistance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

This question is not applicable, as the device is not an algorithm or AI system.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

This question is not applicable in the typical sense for an AI/ML device. For this physical device, the "ground truth" against which performance is measured would be established engineering standards, biomechanical principles, and the performance characteristics of legally marketed predicate devices.

8. The sample size for the training set:

This question is not applicable. The device is not an AI/ML model, so there is no "training set."

9. How the ground truth for the training set was established:

This question is not applicable, as the device is not an AI/ML model.

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