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The Relign Arthroscopic System is indicated for use in orthopedic and arthroscopic procedures. The Fluid Management System of the Relign Arthroscopic System provides fluid distension of the knee, shoulder, ankle, elbow, wrist and hip, and fluid suction during diagnostic and operative arthroscopic procedures. The Shaver Blade/RF Probe of the Relign Arthroscopic System provides abrasion, resection, debridement, and removal of bone through its shaver blade; removal, ablation, and coagulation of soft tissue; as well as hemostasis of blood vessels through its shaver blade and probe. Examples of uses of the product include resection of torn knee cartilage, subacromial decompression, and resection of synovial tissue in other joints.

The Relign Arthroscopic System is a combination radiofrequency controller, shaver system, and fluid management system. The integrated system simultaneously controls a reusable shaver handpiece with disposable shaver blades and probes that include electrosurgical energy, as well as fluid management through inflow and outflow pumps that operate on the peristaltic principle. The system is comprised of five (5) major system components: The Relign Controller that includes a Footswitch, a reusable Shaver Handpiece, a disposable sterile single-use Combination Shaver Blade, a disposable sterile single-use Reciprocating RF Probe, and a disposable sterile single-use Fluid Management Accessories.

Here's an analysis of the provided text regarding the ReLIGN Arthroscopic System's acceptance criteria and studies, organized by your requested information:

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

The document does not explicitly present a table of "acceptance criteria" alongside specific reported performance metrics in the way one might expect for a diagnostic AI device (e.g., sensitivity, specificity, AUC). Instead, it describes performance testing conducted to ensure the device meets its design specifications and complies with various standards. The "reported device performance" is essentially that the device "satisfies design specifications and acceptance criteria" and that various tests were "found to be acceptable."

Here's a summary of the performance claims based on the text:

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 provides very limited detail on specific sample sizes for most tests, particularly for the " cadaver testing" or "simulated use" that would involve a "test set" in the context of evaluation of AI/diagnostic devices.

• Human Factors/Usability Testing: "User testing" was performed, but no number of users is specified.
• Other Performance Tests (Flow rate, pressure, mechanical, durability, etc.): No specific sample sizes (e.g., number of devices, number of repetitions) are provided in this summary.
• Data Provenance: Not mentioned. It's a medical device submission, typically implying company internal testing.

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 information is not provided in the document. The ReLIGN Arthroscopic System is a surgical tool, not a diagnostic AI device that would typically involve "ground truth" established by experts in the context of a diagnostic dataset. The "cadaver testing" and "human factors testing" would involve users (surgeons, nurses, technicians) but their role would be as operators or participants, not necessarily "experts establishing ground truth" in the diagnostic sense.

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

This information is not applicable/provided. As above, the device is a surgical tool, not a diagnostic system requiring adjudication of diagnostic outcomes.

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 information is not applicable/provided. This is a surgical device, not an AI-assisted diagnostic tool for "human readers." MRMC studies are specific to diagnostic performance evaluation.

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

This information is not applicable/provided. The ReLIGN Arthroscopic System is a physical device operated by a human, not a standalone algorithm.

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

This information is not applicable/provided in the context of diagnostic "ground truth." For a surgical device, "ground truth" would relate to the successful execution of surgical tasks, device functionality (e.g., actual flow rates matching set flow rates, tissue ablation matching expectations), and safety. The document indicates that "cadaver testing" was performed, which would serve as a form of "ground truth" for surgical performance in a simulated environment, but no specifics on how success was measured or by whom are given in this summary.

8. The sample size for the training set

This information is not applicable/provided. Since the device is a physical surgical tool and not an AI/ML algorithm that requires a "training set" in the conventional sense, this concept does not apply.

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

This information is not applicable/provided as the device is not an AI/ML algorithm with a training set requiring ground truth establishment.

Summary of Device Type and Document Limitations:

It's important to note that the provided document is a 510(k) summary for a surgical device (arthroscopic system), not an AI/ML-driven diagnostic device. Therefore, many of the requested points, particularly those related to "test sets," "ground truth," "experts," "adjudication," and "MRMC studies," are not directly applicable or are described using different terminology. The evaluation of this device focuses on its functional performance, safety, sterility, biocompatibility, and substantial equivalence to legally marketed predicate devices, rather than the diagnostic accuracy or impact on human reader performance that would be pertinent for AI/ML diagnostic tools.

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