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The ECHELON CIRCULAR™ Powered Staplers have applications throughout the alimentary tract for end-to-end, end-to-side, and side-to-side anastomoses.

The ECHELON CIRCULAR™ Powered Staplers are sterile, single-patient use devices that simultaneously staple and cut tissue with a battery powered firing system to create an anastomosis. The devices deliver 2 rows of staples on the outside of the cut line. The ECHELON CIRCULAR™ Powered Staplers are available in a 24 cm curved shaft length in 4 end-effector sizes: 23mm, 25mm, 29mm and 31mm. Each device has a detachable anvil that allows a surgeon to place the anvil in the desired location. The devices are packaged with a battery pack that must be installed prior to use.

The provided document is a 510(k) Summary for a medical device called the "ECHELON CIRCULAR™ Powered Stapler." It is a premarket notification for a new device, and it relies on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving efficacy through clinical studies. Therefore, the information typically requested in your prompt regarding acceptance criteria, study details, sample sizes, expert ground truth, and comparative effectiveness studies with AI is not directly applicable in the context of this 510(k) submission.

This document describes bench and animal testing to demonstrate substantial equivalence, not clinical trials with human subjects.

Here's a breakdown of the available information in relation to your request, with an emphasis on what is not present because it's a 510(k) for a physical medical device, not an AI/software device:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a formal table of acceptance criteria with corresponding performance metrics in a way that would be typical for an AI/software study. Instead, it lists the types of tests performed to demonstrate substantial equivalence:

Note: The phrase "substantially equivalent to the predicate device" is the primary performance metric for each category in a 510(k) submission. Specific numerical acceptance criteria or performance values are generally not detailed in the public 510(k) summary, as the goal is to show similarity to an already approved device.

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

• Test Set Sample Size: Not explicitly stated numerically for each test. The document mentions "bench and animal tests." It does not involve human data.
• Data Provenance: The tests were performed as part of the device development and regulatory submission process by Ethicon, LLC. This is prospective testing related to the new device. There is no mention of country of origin for data as it pertains to clinical data, as this is bench and animal testing.

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

Not applicable. For a physical medical device evaluated through bench and animal testing, the "ground truth" is established by the physical measurements, observations, and histological results of the tests themselves, often interpreted by qualified engineers, scientists, and veterinarians, rather than human experts reviewing images or other decision-making outputs.

4. Adjudication method for the test set

Not applicable. Adjudication methods (like 2+1, 3+1) are relevant for reconciling discrepancies in human expert opinions, typically in an AI/software context when establishing ground truth from human interpretations.

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 device is a surgical stapler, not an AI/software diagnostic tool. There are no "human readers" in the context of interpreting AI outputs.

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

Not applicable. This is a physical surgical device, not an algorithm.

7. The type of ground truth used

For the bench tests, the "ground truth" would be objective physical measurements and engineering specifications. For animal testing, it would involve direct observation, histological analysis of tissue, and clinical assessment by veterinary professionals. It is not "expert consensus, pathology, or outcomes data" in the typical AI/software sense, but rather direct physical and biological evidence.

8. The sample size for the training set

Not applicable. There is no "training set" in the context of a physical device. This is not an AI/machine learning device.

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

Not applicable (as above).

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