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The AEON™ Endoscopic Powered Stapler has applications in general, abdominal, gynecological, pediatric, and thoracic surgery for resection, transection, and creation of anastomoses. The instrument may be used for transection and resection of liver substance, hepatic vasculature, biliary structures, pancreas, kidney and spleen.

The AEON Endoscopic Powered Stapler system is composed of the AEON Endoscopic Powered Stapler Handle ("Powered Handle") and AEON Endoscopic Stapler Reloads ("Reloads"). The AEON Endoscopic Powered Stapler places two, triple-staggered rows of titanium staples while simultaneously transecting between the two triple-staggered rows of staples. The Powered Handle may be reloaded and fired up to 20 times in a single procedure. The Powered Handle is available in three different lengths (60mm, 160mm and 260mm) and the Reloads are available in multiple staple sizes to accommodate various tissue thicknesses.

The AEON Endoscopic Powered Stapler uses software to control the operation of the stapler and is AC powered. The device is sterile packaged and is labeled for single use.

The provided FDA 510(k) clearance letter and summary for the AEON™ Endoscopic Powered Stapler focuses on demonstrating substantial equivalence to predicate devices, primarily for an expanded indication to include thoracic applications. However, it does not contain the detailed information typically found in a study report proving a device meets specific acceptance criteria, especially for AI/ML-driven devices.

Based on the provided text, here's an analysis of the acceptance criteria and the study that supports it, with the caveat that many details commonly sought for AI/ML device evaluations are not present in this document.

Acceptance Criteria and Study for AEON™ Endoscopic Powered Stapler (Thoracic Applications)

The core "acceptance criteria" discussed in this document revolve around demonstrating substantial equivalence for the expanded indication of "thoracic surgery." This implies that the device, when used for this new application, performs safely and effectively without raising new questions of safety or efficacy. Since the device itself is a mechanical surgical stapler with software control, the criteria are likely related to the mechanical performance and clinical outcomes.

The document explicitly states that the expanded Indications for Use for thoracic applications are supported by real-world evidence data.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) summary for a mechanical device with software control, the "acceptance criteria" are implied by the claim of substantial equivalence and the successful real-world evidence for thoracic applications. Specific quantitative performance metrics typical for AI/ML (e.g., sensitivity, specificity, AUC) are not directly stated. Instead, the acceptance criteria are implicitly that the device is "safe and effective for use for thoracic applications" without adverse events or performance issues that would preclude clearance.

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

• Sample Size: The document does not specify the sample size (number of patients, procedures, or stapler uses) used in the real-world evidence data.
• Data Provenance: The data is described as "Real-world evidence data." The document does not specify the country of origin of this data or whether it was retrospective or prospective.

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

This information is not provided in the document. For a surgical stapler's clinical performance, "ground truth" would typically relate to clinical outcomes assessed by surgeons or other medical professionals.

4. Adjudication Method for the Test Set

This information is not provided in the document.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not mentioned or described. This type of study is typically relevant for diagnostic imaging AI systems where human readers interpret images with or without AI assistance. The AEON™ Endoscopic Powered Stapler is a surgical instrument, not a diagnostic AI. The document states "human factors testing is not applicable to support the expanded Indications for Use," further indicating that comparative studies with human action are not central to this particular clearance process.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was done

The device is a powered surgical stapler that uses software to control its operation. While the software itself might have undergone standalone validation, the performance described (safety and effectiveness for thoracic applications) would inherently involve the device operating with a human surgeon in the loop. A standalone "algorithm only" performance in the context of surgical stapling would be largely meaningless without the mechanical components and user interaction. The document implies that the device's overall performance, including its software-driven functions, was evaluated in real-world use for thoracic applications.

7. The Type of Ground Truth Used

For "real-world evidence data" supporting the safety and effectiveness of a surgical stapler for thoracic applications, the ground truth would most likely be derived from:

• Clinical Outcomes Data: This would include metrics like incidence of staple line failure, leaks, bleeding, tissue trauma, successful resection/transection, complication rates, and patient recovery within a thoracic surgical context.
• Surgeon Assessments: Evaluations by operating surgeons regarding ease of use, stapler performance, and observed tissue response.

The document does not explicitly state the specific type of ground truth, but these are the most probable types for this device and its application.

8. The Sample Size for the Training Set

The document does not mention a "training set" in the context of the expanded indications or real-world evidence. This is expected given that the clearance is for a mechanical device with software control, not an AI/ML diagnostic algorithm that typically relies on extensive training data. The software within the stapler likely has deterministic control logic rather than learning algorithms that require large training datasets in the typical AI sense.

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

Since a "training set" is not mentioned or described in the context of the device's software learning or the expanded indications, the method for establishing its ground truth is not applicable here. The software control functions were likely verified and validated through a design control process, not machine learning model training.

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