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The Rotating Endoscopic Scissors are a monopolar electrosurgical instrument intended for cutting, dissecting and cauterizing soft tissue during endoscopic electrosurgical procedures.

The Ethicon Endo Surgery (EES) Rotating Endoscopic Scissors are a monopolar electrosurgical instrument intended for cutting, dissecting and cauterizing soft tissue during endoscopic electrosurgical procedures. The Rotating Endoscopic Scissors are a sterile, single patient use, disposable instrument consisting of a flexible wire cable and scissors end effector. The end effector can be rotated independently of the shaft. When connected to an electrosurgical generator using a 4.5 mm monopolar cable (not supplied) and activated, the Rotating Scissors delivers a monopolar electrical current to the surgical site. This device passes through gastroscopes having a 2.8 mm or larger working channels. This device is supplied sterile for single-patient use.

The provided text describes a 510(k) summary for the Ethicon Endo Surgery® Rotating Endoscopic Scissors. This document primarily focuses on demonstrating substantial equivalence to predicate devices and adherence to relevant safety standards, rather than reporting on a study meeting specific performance acceptance criteria like those found in AI/ML device submissions.

Therefore, many of the requested categories for AI/ML device studies are not applicable to this submission type. I will fill in the relevant information available and explicitly state when a category is not applicable.

Key points to understand:

• This is a submission for a physical surgical instrument, not an AI/ML diagnostic tool.
• The "performance data" discussed is related to bench testing for safety and functionality, not diagnostic accuracy or efficacy requiring ground truth, expert consensus, or MRMC studies.
• The "acceptance criteria" are related to compliance with established medical device safety standards.

Acceptance Criteria and Device Performance Study for Ethicon Endo Surgery® Rotating Endoscopic Scissors

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample size: Not specified. The document states "Bench testing was performed," which typically involves testing a defined number of devices or components according to a protocol. However, specific quantities are not provided.
• Data provenance: Not explicitly stated, but assumed to be internal laboratory testing conducted by Ethicon Endo-Surgery, Inc., located in Cincinnati, Ohio, USA. This would be considered prospective testing, as devices were manufactured and then tested.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• N/A. This device does not have "ground truth" in the diagnostic sense (e.g., presence or absence of a disease). Its performance is evaluated against engineering standards and functional requirements through bench testing. Expert clinicians might be involved in defining performance requirements or evaluating usability, but not in establishing ground truth for a diagnostic test set.

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

• N/A. Adjudication methods are relevant for subjective medical assessments (e.g., image interpretation). Bench testing for medical device standards typically involves objective measurements and pass/fail criteria, not expert adjudication of results against a medical ground truth.

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

• N/A. This is a physical surgical instrument, not an AI/ML diagnostic or assistive device for human readers. Therefore, MRMC studies are not applicable.

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

• N/A. This is a physical surgical instrument. The concept of "standalone algorithm performance" is not applicable.

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

• N/A. As explained in point 3, the device is evaluated against established engineering and safety standards, and functional performance on tissue surrogates, not against a medical "ground truth" for diagnosis. The "truth" in this context is whether the device meets specific engineering specifications and safety requirements.

8. The sample size for the training set

• N/A. This is a physical surgical instrument. There is no "training set" in the context of machine learning. The device design is based on engineering principles and predicate device comparison, not on data-driven model training.

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

• N/A. As there is no training set for an AI/ML model, this question is not applicable.

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