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The Fast Pass Suture Punch System is indicated for the placement of sutures in Rotator Cuff and Bankart repair procedures. The Fast Pass Suture Punch System is intended to be used for the placement of sutures during arthroscopic and open procedures.

The Fast Pass Suture Punch System comprises three components: the Fast pass Suture Punch, the Fast Pass Suture Needle, and the Fast Pass Cannula/Obturator.

The provided text describes a medical device, the "Fast Pass Suture Punch System," and argues for its substantial equivalence to previously marketed devices. However, it does not contain information about acceptance criteria or a study proving that the device meets specific performance criteria in the way typically expected for an AI/ML medical device submission.

Instead, the provided text describes a submission for a surgical instrument (a suture punch) and its components, focusing on demonstrating its similarity to existing, legally marketed devices. The "study" mentioned is a general statement about basic functionality in a simulated environment, not a robust performance study with statistical metrics.

Therefore, for aspects related to acceptance criteria, specific performance metrics, sample sizes, expert ground truth, MRMC studies, or standalone algorithm performance, the information is not available in the provided text.

Here's how to categorize the available information:

Acceptance Criteria and Study for the Fast Pass Suture Punch System

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not specified. The phrase "a simulated tissue model" suggests a qualitative functional test rather than a structured test set with a specific sample size.
• Data Provenance: The test was conducted using "a simulated tissue model," so it's a bench test, not clinical data from a specific country or retrospective/prospective study.

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

• Not applicable. The "ground truth" for "successfully passed suture through a simulated tissue model" would likely be a direct observation of function, not expert consensus on diagnostic image interpretation. No experts are mentioned in relation to this test.

4. Adjudication method for the test set

• Not applicable. No complex ground truth establishment requiring adjudication is described.

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

• No, an MRMC comparative effectiveness study was not done. This submission is for a physical surgical instrument, not an AI/ML-driven diagnostic or assistive device.

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

• No, a standalone (algorithm only) performance study was not done. This device is a physical instrument directly manipulated by a surgeon.

7. The type of ground truth used

• The "ground truth" for the simulated tissue model test was likely a direct observation of the mechanical function (i.e., whether the suture successfully passed through the simulated tissue), corresponding to a functional test or a "pass/fail" outcome for the mechanical action.

8. The sample size for the training set

• Not applicable. This device is a physical instrument, not an AI/ML algorithm requiring a training set.

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

• Not applicable. No training set is involved.

Summary regarding the provided text:

The document focuses on demonstrating substantial equivalence for a surgical instrument by comparing its components, materials, intended use, and indications to existing predicate devices. The "testing" mentioned is a basic functional check ("successfully passed suture through a simulated tissue model") to confirm suitability for its intended use, which is a common requirement for physical devices. It does not provide the kind of detailed performance study data (with acceptance criteria, sample sizes, expert ground truth, etc.) that would be expected for an AI/ML-driven medical device.

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