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The Encision AEM Monitoring System is an accessory for use with electrosurgical generators and electrodes that is designed to safely deliver electrosurgical energy and to prevent injury caused by insulation failure and capacitive coupling.

The AEM Monitoring System consists of two distinct functions:

• . Active electrode monitoring is intended to control stray monopolar energy caused by insulation failure and capacitive coupling in surgical instruments on the shaft of the instrument.
• End point monitoring is intended to aid the surgeon in determining the end point . of bipolar electrosurgical desiccation.

The Encision AEM Monitoring System, consisting of the AEM Monitor and accessories, is designed to safely deliver electrosurgical energy and to prevent injury caused by insulation failure and capacitive coupling. The AEM Monitoring System consists of two distinct functions:

• 1. End point monitoring is intended to aid the surgeon in determining the end point of bipolar electrosurgical desiccation.
     The end point monitor displays the electrosurgical current being delivered during bipolar coagulation. At the point where the tissue or vessel is no longer conductive, the meter will display zero or near zero current. An audible indicator of current, presented as a variable click rate, can also be turned on. An optional remote display is also available.

There is no change to the end point monitoring function.

• 2. Active electrode monitoring is intended to control stray monopolar energy caused by insulation failure and capacitive coupling in surgical instruments on the shaft of the instrument.
     The active electrode monitoring is performed by measuring the current in the shield of the AEM electrosurgical instrument. The internal conductive shield also diverts capacitivley coupled energy back to the generator, away from the patient. If excessive magnitude or a quality indicative of a sparking condition is detected, an indicator is lit on the monitor front panel and the signal inhibits the ESU (electrosurgical unit) output using the ESU contact quality monitoring circuit.

Connection is made to the ESU contact quality monitoring circuit via a jumper cord. A cord connects the AEM instrument to the monitor and to the ESU output via an adapter. Setup indicators are driven by isolating continuity monitors. These provide checks to ensure the adapters and cable assembly are properly connected.

AEM monitor models, cables and adapters are available for mechanical and electrical compatibility with various ESUs, as well as for footswitching and handswitching monopolar instruments, and bipolar instruments. Minor mechanical and electrical changes may be made to optimize interface with compatible ESUs and instruments, which do not affect the safety and efficacy of the system.

There is no change to the active electrode monitoring function, including the detection circuitry and instrument interface.

Here's a summary of the acceptance criteria and study information based on the provided document:

Acceptance Criteria and Device Performance

The document describes the AEM Monitoring System and its functions but does not explicitly state specific, quantifiable acceptance criteria or reported device performance metrics in a table format. Instead, it focuses on the device's intended functions and its verification against those functions.

Implicit Acceptance Criteria (Derived from Intended Use and Device Description):

Study Information

The provided document is a 510(k) Summary for a modified device, leveraging prior clearances and focusing on compatibility. It does not describe a full clinical study with detailed statistical results as would typically be found for a novel device. The "Non-clinical Performance Testing" section is brief and general.

Here's the breakdown based on the available text:

1. 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 with a numerical value. The document mentions "representative electrosurgical units and with an instrument representative of the worst case criteria for AEM technology." This implies a limited, selected set of test cases, but the exact number is not provided.
   • Data Provenance: Not specified. Likely internal company testing.
   • Retrospective or Prospective: Not specified, but given it's "non-clinical performance testing," it would be an engineered test setting rather than testing on patients.

2. 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):

   • Not Applicable. As this is described as "non-clinical performance testing," it would not involve human expert interpretation of results in the way a diagnostic AI study would. Ground truth would be established through engineering measurements and known physical principles.

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

   • Not Applicable. Adjudication methods like 2+1 are used in studies involving human interpretation and disagreement. This was "non-clinical performance testing."

4. 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 study was not done. The device is not an AI diagnostic assistant for human readers. It is an electrosurgical monitoring system.

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

   • Yes, in spirit. The "Non-clinical Performance Testing" focused on the system's inherent performance. While the end-point monitoring aids a surgeon, the core active electrode monitoring function is described as an automated system that "detects" and "inhibits" without direct human interpretation for its primary safety function. The testing described would primarily be a standalone assessment of the system's technical capabilities.

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

   • Engineering Measurements/Physical Principles: For active electrode monitoring, the ground truth would be the actual stray energy/sparking conditions created in a controlled environment, measured by calibrated equipment. For end-point monitoring, the ground truth would be the known conductivity changes in tissue models or actual tissue, correlated with measured current values by the device.

7. The sample size for the training set:

   • Not Applicable/Not Specified. The document does not describe the use of an algorithm that requires a "training set" in the context of machine learning or deep learning. This device appears to be based on established electrosurgical physics and engineering principles rather than a data-driven learning model.

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

   • Not Applicable/Not Specified. As there's no mention of a training set, the establishment of its ground truth is not discussed.

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