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These AEM Instruments incorporate the use of AEM technology and are intended for use in delivering monopolar electrosurgical energy during laparoscopic procedures only. AEM Instruments are intended for use with the AEM Monitoring System and electrosurgical generators having compatibility with the AEM Monitor. Scissors Inserts are intended for use on soft tissue only.

Encision AEM® Monopolar Laparoscopic Instruments and Accessories incorporate the use of AEM technology and are intended for use in delivering monopolar electrosurgical energy during laparoscopic procedures only. Encision AEM® Monopolar Laparoscopic Instruments and Accessories are intended for use with the AEM Monitoring System and electrosurgical generators having compatibility with the AEM Monitor. These Encision AEM® Monopolar Laparoscopic Instruments and Accessories include reusable handles and reusable & disposable insert electrodes plus disposable sheath. Encision AEM® Monopolar Laparoscopic Instruments and Accessories, in conjunction with an AEM Monitor properly connected to the electrosurgical generator (ESU), continuously monitor and dynamically manage "stray energy" (insulation failure and capacitive coupling), which are likely out of the surgeon's field of view.

This document does not contain explicit acceptance criteria and corresponding performance data tables for a diagnostic device, nor does it describe a study that directly proves a device meets such criteria in terms of diagnostic effectiveness (e.g., sensitivity, specificity, accuracy).

Instead, this document is an FDA 510(k) premarket notification for Encision AEM® Monopolar Laparoscopic Instruments and Accessories. The purpose of a 510(k) submission is to demonstrate that a new device is "substantially equivalent" to a legally marketed predicate device, meaning it is as safe and effective.

Therefore, the information provided focuses on demonstrating substantial equivalence rather than fulfilling specific performance acceptance criteria for diagnostic output.

However, I can extract information related to the device's characteristics and the studies performed to support its
FDA clearance, framed within the context of substantial equivalence.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) for electrosurgical instruments, the "acceptance criteria" are primarily related to safety, functionality, and equivalence to predicate devices, rather than diagnostic performance metrics like sensitivity or specificity.

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

The document states, "All testing required per design control procedures was conducted using bench testing." It also mentions "No animal or clinical testing is required."

• Test Set Sample Size: Not explicitly stated as a number of "cases" or "patients" since this is bench testing of physical devices. The "sample" would be the number of devices or components tested. This detail is not provided.
• Data Provenance: Bench testing data, originating from the manufacturer's internal studies. It is retrospective in the sense that it's evaluating the newly designed devices against established performance of predicate devices and recognized standards. There's no mention of country of origin for patients, as no patient data was used.

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

• This question is not applicable (N/A) for this submission. The "ground truth" for electrosurgical instrument performance is established by recognized consensus standards, safety regulations, and functional specifications, rather than expert interpretation of medical images or clinical outcomes. The device's performance is measured objectively against these established engineering and safety benchmarks. There are no "experts" establishing a "ground truth" in the clinical diagnostic sense.

4. Adjudication Method for the Test Set

• This question is not applicable (N/A). Adjudication methods (like 2+1, 3+1) are used in clinical studies, particularly for diagnostic imaging or outcome evaluation where there might be disagreement among human readers. Since this submission relies on bench testing and engineering standards, there is no human adjudication process involved for the "test set" performance.

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

• This question is not applicable (N/A). This device is an electrosurgical instrument, not an AI-powered diagnostic tool. Therefore, no MRMC study or evaluation of AI assistance for human readers was performed or would be relevant.

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

• This question is not applicable (N/A). This device is a medical instrument used directly in surgery, not a standalone algorithm. Its function is to deliver electrosurgical energy safely, monitored by an AEM Monitoring System. There is no "algorithm-only" performance to evaluate in this context.

7. The Type of Ground Truth Used

The "ground truth" for the device's safety and effectiveness is largely defined by:

• Engineering Specifications: Internal design requirements for the device's mechanical, electrical, and material properties.
• Recognized Consensus Standards: Compliance with FDA-recognized standards for sterilization (e.g., ANSI/AAMI/ISO 11137-1, 17665-1), biocompatibility (ANSI/AAMI/ISO 10993-1), electrical safety (ANSI/AAMI/IEC 60601-2-2), electromagnetic compatibility (ANSI/AAMI/IEC 60601-1-2), packaging (ANSI/AAMI/ISO 11607-1), and reprocessing instructions (ANSI/AAMI/ISO 17664).
• Safety and Performance of Established Predicate Devices: The primary ground truth for a 510(k) is demonstrating that the new device performs "as well as" or is "as safe and effective as" the predicate device(s).

8. The Sample Size for the Training Set

• This question is not applicable (N/A). This is not an AI/machine learning device that requires a "training set."

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

• This question is not applicable (N/A). As there is no training set for an AI algorithm, there is no ground truth established for a training set.

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