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The Electrosurgical Generator is used to deliver RF energy via an assortment of surgical devices to cut and coagulate different kinds of tissue.

The ES-100 and ES-300 Electrosurgical Generator are advanced high-frequency surgical devices designed to provide versatility and safety in various surgical procedures. Both models offer a range of operating modes, including monopolar electrosurgical excision modes, monopolar electrocoagulation modes, and bipolar modes, catering to the diverse needs of surgeons. The maximum output power of the ES-100 is 100 W, while the ES-300 offers increased output power of up to 300 W, providing surgeons with enhanced capabilities for cutting and coagulation in various surgical procedures. Both models are equipped with manual and foot switch controls, allowing for seamless operation during surgeries. They feature volume control for adjusting the device's audio output, memory functions for storing recent settings, and built-in return electrode monitor systems for real-time safety monitoring.

This document is a 510(k) summary for an Electrosurgical Generator, not a study evaluating an AI/ML device. Therefore, the requested information regarding acceptance criteria, study design, and performance metrics for an AI/ML powered device cannot be extracted from this document.

The document describes the regulatory submission for electrosurgical generators (ES-100 and ES-300 models) and compares them to a predicate device (Bovie IDS-310 High Frequency Electrosurgical Generator). The core of the submission is to demonstrate "substantial equivalence" to the predicate device, not to prove performance against specific acceptance criteria for an AI/ML algorithm.

Here's how each of your requested points relates to the provided document:

1. A table of acceptance criteria and the reported device performance: This document does not specify "acceptance criteria" in the context of an AI/ML study. Instead, it presents a comparison table of technical characteristics between the subject device and the predicate device (pages 7-8). The "performance" mentioned refers to electrical safety, electromagnetic compatibility, output performance, and thermal damage characteristics of the electrosurgical unit itself, not an AI/ML algorithm's diagnostic performance.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. This document is about hardware medical devices and their electrical/thermal performance, not an AI/ML algorithm that processes data.

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): Not applicable. "Ground truth" in the context of expert consensus for AI/ML validation is not a component of this submission.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

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: Not applicable.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): Not applicable. The "ground truth" for electrosurgical generators would be physical measurements of electrical output and thermal effects, verified through accredited testing, not expert consensus on medical images or patient outcomes data.

8. The sample size for the training set: Not applicable. This is for an electrosurgical hardware device, not an AI/ML model.

9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document is a 510(k) premarket notification for electrosurgical generators, demonstrating substantial equivalence to a predicate device through non-clinical testing (electrical safety, EMC, bench testing for output performance, and preclinical thermal damage assessment). It does not contain any information related to AI/ML device validation.

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