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The Ethicon™ Circular Staplers have application throughout the alimentary tract for end, end-to-soside anastomoses.

The Ethicon™ Circular Staplers are sterile, single use devices that simultaneously staple and cut tissue to create an anastomosis. The devices deliver 2 concentric rows of staples on the outside of the cut line. The Ethicon™ Circular Staplers are available in two device configurations: CDH and ECS and two shaft lengths; a standard 26 cm shaft and an XL 37 cm shaft. Each configuration is available in 4 endeffector sizes: 21 mm, 29 mm, 33 mm. These configurations function in the same manner - to compress tissue and to produce the same closed staple on tissue. Each device has a detachable anvil that allows a surgeon to place the anvil in the desired location.

This 510(k) summary describes a device modification, rather than a completely new device. Therefore, the "acceptance criteria" and "device performance" discussed here refer to the device's ability to maintain its previous performance after the modification, not to establish initial performance benchmarks.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state the specific sample sizes used for each of the "ex-vivo (bench) testing" aspects (tissue cutting reliability, force-to-fire, stapling performance). It only mentions that testing was performed.

The data provenance is retrospective in the sense that the testing was performed on the modified device to verify that the changes did not negatively impact performance, essentially re-validating against existing design requirements. The data is from bench testing, meaning it was conducted in a laboratory setting, not in a clinical environment. No country of origin for the data is specified, but it can be inferred to be associated with the manufacturer, Ethicon Endo-Surgery, LLC.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

Not applicable. This was bench testing against design specifications, not a study requiring expert interpretation of results or establishing ground truth in a clinical or diagnostic context.

4. Adjudication Method for the Test Set

Not applicable. As this was bench testing, results likely involved objective measurements against predefined engineering specifications. There would be no need for adjudication by multiple human observers.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The submission form explicitly states: "This submission does not include data from Clinical Studies."

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

Not applicable. The device is a mechanical surgical stapler. It does not involve any algorithms or AI that would require standalone performance evaluation.

7. The Type of Ground Truth Used

The "ground truth" for this submission was the existing design requirements and specifications for the Ethicon™ Circular Staplers. The testing aimed to demonstrate that the modified device continued to perform to these established specifications.

8. The Sample Size for the Training Set

Not applicable. There is no "training set" as this device is a mechanical stapler and does not involve machine learning or AI.

9. How the Ground Truth for the Training Set was Established

Not applicable. As there is no training set for a mechanical device. The original design requirements and performance characteristics would have been established during the initial development and clearance processes for the predicate devices.

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The Ethicon™ Circular Staplers have applications throughout the alimentary tract for end-to-end, end-to side, and side-to-side anastomoses. The predicate device also has the same indications for use.

The Ethicon™ Circular Staplers are sterile, single use devices that simultaneously staple and cut tissue to create an anastomosis. The devices deliver 2 concentric rows of staples on the outside of the cut line. The Ethicon™ Circular Staplers are available in two device configurations: CDH and ECS and two shaft lengths; a standard 26 cm shaft and an XL 37 cm shaft. Each configuration is available in 4 endeffector sizes: 21 mm, 25 mm, 29 mm, 33 mm. These configurations function in the same manner - to compress tissue and to produce the same closed staple on tissue. They differ primarily in the length of the curved shaft and the shaft's ability to maintain a pneumatic seal specifically for use in laparoscopic surgical procedures. Each device has a detachable anvil that allows a surgeon to place the anvil in the desired location.

The provided text describes a 510(k) premarket notification for Ethicon™ Circular Staplers. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study of the device against specific acceptance criteria for a new and innovative AI/software device. Therefore, much of the requested information regarding AI/software specific studies, such as sample sizes for test sets, data provenance, expert ground truth establishment, adjudication methods, MRMC studies, or standalone algorithm performance, is not applicable or available in this document.

However, I can extract information related to the performance data provided to demonstrate substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a quantitative manner for each test. Instead, it states that the performance data demonstrates "substantial equivalence" to the predicate device and that "the differences between the devices were found not to affect safety or performance." The reported performance is an equivalency to the predicate.

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

• Sample Size for Test Set: Not explicitly stated in the document. The performance data section lists types of tests (e.g., "leak onset pressure equivalency," "acute hemostasis evaluation") but does not provide specific sample sizes for these tests.
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). The tests are described as "bench and animal tests."

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

This is not applicable as the study involved bench and animal testing of a surgical stapler, not an AI/software device requiring expert interpretation for ground truth.

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

Not applicable. This type of adjudication method is relevant for studies involving human interpretation of data, typically in AI/software performance evaluations.

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 document describes a medical device (surgical stapler), not an AI-assisted diagnostic or treatment system.

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

Not applicable. The device is a mechanical surgical stapler and does not involve an algorithm.

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

For the bench and animal tests, the "ground truth" would be established by direct physical measurements, observations, and potentially histological analysis in the case of healing studies. For example:

• Staple line integrity: Physical inspection, burst pressure measurements.
• Biocompatibility: Standardized material testing and biological assays.
• Healing of stapled anastomosis: Histopathological examination of animal tissues.

8. The sample size for the training set:

Not applicable. There is no mention of a training set as this is not an AI/machine learning device.

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

Not applicable, as there is no training set for an AI/machine learning device.

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