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The NiTi ColonRing™ device is intended for use throughout the alimentary tract for endto-end, end-to-side and side-to-side anastomoses, in both open and laparoscopic surgeries. Once wound strength is sufficient to maintain the anastomosis, the NiTi ColonRing™ is passed from the body.

The ColonRing™ device is a sterile single use device. The ColonRing™ provides a simple method for the creation circular compression anastomosis of the alimentary tract. After a period of 7-10 days, a compression-induced necrosis of the tissue sides underneath the ring occurs and the whole device, together with the necrosed tissue that was compressed by the rings, detaches and is naturally expelled with the stool.

This device, the ColonRing™ Device, is a physical surgical device, not an AI/ML-driven diagnostic or image analysis tool. The provided text describes its intended use, technological characteristics, and substantial equivalence to a predicate device, focusing on its physical properties and function. Therefore, most of the questions related to acceptance criteria, specific performance metrics (like sensitivity, specificity, AUC), sample sizes for test sets, expert adjudication, MRMC studies, standalone performance, and ground truth establishment for AI/ML devices are not applicable.

The submission is for a 510(k) premarket notification, which relies heavily on demonstrating "substantial equivalence" to a legally marketed predicate device.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Not applicable in the conventional sense for an AI/ML device. For physical medical devices seeking 510(k) clearance, acceptance criteria are generally demonstrated by proving substantial equivalence to a predicate device through:

• Same intended use.
• Similar technological characteristics.
• Performance data (bench, animal, clinical if needed) showing it is as safe and effective.

The provided document states:

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

Not applicable. There is no "test set" in the context of an AI/ML device that analyzes data. The performance data mentioned (bench tests, animal data) would involve a sample size relevant to those studies, but these details are not provided in this summary. The data provenance is not stated, but it would be from the studies (bench, animal) conducted for this device.

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

Not applicable, as this is not an AI/ML diagnostic device requiring expert interpretation for ground truth.

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

Not applicable.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done

Not applicable.

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

Not applicable in the AI/ML sense. For this device, "ground truth" would relate to objective measurements from bench tests (e.g., strength of anastomosis, compression force, time to detachment) and outcomes in animal studies (e.g., healing, anastomotic integrity, absence of adverse events). This is typical for demonstrating performance and safety of a surgical device.

8. The sample size for the training set

Not applicable. There is no concept of a "training set" for this type of physical surgical device.

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

Not applicable.

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