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StitchKit® Suture Delivery Canister with Polypropylene Surgical Suture facilitates minimally invasive robotic surgery by transporting suture to the operative field and removing used needles after suturing. It contains polypropylene surgical suture indicated for use in general soft tissue approximation and/or ligation.

The StitchKit® Suture Delivery Canister with Polypropylene Surgical Suture ("StitchKit") is a sterile, single-use plastic canister that is pre-loaded with multiple strands of legally marketed monofilament polypropylene surgical suture (with needles attached). The device facilitates robotic-assisted endoscopic surgery by introducing these multiple strands of suture to the surgical field all at once. The surgeon can then dispense the suture strands one at a time. The device is sized to be passed through a ≥12 mm trocar. As suturing is completed with each strand, used needles are deposited into a compartment within the StitchKit® canister. Once the surgeon has finished suturing, the device is removed along with the used needles inside. It is supplied sterile in a plastic tray with Tyvek® lid.

The provided document is a 510(k) premarket notification for a medical device (StitchKit® Suture Delivery Canister with Polypropylene Surgical Suture). It describes the device, its intended use, and its substantial equivalence to predicate devices based on performance data. Importantly, this document is for a physical medical device (a suture delivery system), not an AI/ML algorithm or software device. Therefore, many of the requested fields regarding AI/ML study design are not applicable.

Here's the information that can be extracted or stated as not applicable:

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: The document does not specify exact sample sizes for the various tests. It generally states that "performance testing was performed."
• Data Provenance: Not explicitly stated, but typically such testing is conducted in a controlled lab environment by the manufacturer. The document does not mention human patient data for testing device performance.

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

• Not Applicable. This device is a physical suture delivery system. Its performance is evaluated through engineering and biocompatibility tests, not by expert interpretation of data or images. Ground truth as typically understood in AI/ML validation (e.g., expert consensus on medical images) does not apply.

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

• Not Applicable. See explanation above. The performance tests are objective engineering and biological assessments.

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. This is a physical medical device, not an AI/ML diagnostic or assistive tool. No human reader studies with or without AI assistance were conducted.

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

• Not Applicable. This is a physical medical device, not an AI/ML algorithm.

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

• Ground Truth for Physical Device Testing: The "ground truth" for this device's performance tests would be established by:
  • Validated Test Methods: Adherence to recognized standards (e.g., USP for suture properties).
  • Objective Measurements: Quantitative measurements of physical properties (knopull strength, diameter, dispensing force, shelf life parameters) and biological responses (pyrogenicity, extraction levels).
  • Predicate Device Performance: Comparison to the known performance characteristics of the legally marketed predicate devices.

8. The sample size for the training set

• Not Applicable. This is not an AI/ML device, so there is no training set.

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

• Not Applicable. No training set exists for this device.

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