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The J-Screw indicated for use in osteotomy, reconstruction, arthrodesis, joint fusion, fracture repair, and fracture fixation of small bones of the upper and lower extremities.

The J-Screw is a two part bone screw for use in osteotomy, reconstruction, arthrodesis, joint fusion, fracture repair, and fracture fixation of small bones of the upper and lower extremities. The unique design of the device allows it to fixate or fuse small bones with infinitely adjustable fusing compression, in a fixed yet flexed position.

The provided document is a 510(k) Pre-Market Notification for a medical device called the "J-Screw," a bone fixation fastener. This type of document is a submission to the FDA to demonstrate that a device is substantially equivalent to a legally marketed predicate device, rather than a clinical study report with detailed acceptance criteria and performance data for an AI/ML powered device.

Therefore, the specific information requested about acceptance criteria, study design, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for a device meeting acceptance criteria in the context of AI/ML performance metrics cannot be found in this document.

However, I can extract information related to the performance data that was gathered for the J-Screw as outlined in the "Performance Data" section of the 510(k) Summary (Page 4). This data relates to the mechanical properties of the screw, not an AI/ML algorithm's performance.

Here's an attempt to answer based on the available information, noting the significant conceptual difference between the requested AI/ML context and the provided mechanical device document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in the format typically seen for algorithm performance. Instead, it lists the types of mechanical performance data that were gathered and implies that these met the standards for substantial equivalence to predicate devices. There are no numerical acceptance thresholds or reported performance values provided for these tests in this summary.

2. Sample size used for the test set and the data provenance

The document does not specify the sample size for the mechanical tests. The data provenance is not mentioned, as these are likely mechanical "bench" tests rather than clinical data from patients.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This is not applicable as the device is a physical bone screw, not an AI/ML algorithm requiring expert interpretation for ground truth. The "ground truth" for mechanical properties would be the physical measurement itself.

4. Adjudication method for the test set

Not applicable for mechanical performance testing.

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

Not applicable. The J-Screw is a physical bone fixation device, not an AI-powered diagnostic or assistive tool.

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

Not applicable.

7. The type of ground truth used

For the mechanical tests, the ground truth would be the direct measurement of physical properties (e.g., torque, strength, angle) as determined by standardized test methods (ASTM F543-13).

8. The sample size for the training set

Not applicable, as this is not an AI/ML device.

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

Not applicable, as this is not an AI/ML device.

In summary, the provided document describes a medical device (a bone screw) that is subject to mechanical testing to demonstrate substantial equivalence to existing devices, not an AI/ML powered device. Therefore, most of the requested information, which is relevant to AI/ML device evaluation, cannot be found or fabricated from this document.

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