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The Ethicon Megadyne Electrosurgical Generator (ESU) is intended as a general-purpose electrosurgical generator designed to produce radio frequency (RF) current for cutting and coagulation to be delivered to target tissue through an accessory electrode during open and laparoscopic surgical procedures.

The Ethicon Megadyne TM Electrosurgical Generator is a microprocessor controlled, isolated output, high frequency generator designed for use in cutting and coagulation of tissue. The generator has the ability to perform both monopolar cutting and coagulation and bipolar coagulation of tissue in a wide range of surgical applications.

The Megadyne Electrosurgical Generator, a general-purpose electrosurgical generator designed to produce radio frequency (RF) current for cutting and coagulation of tissue during surgical procedures, underwent various performance tests to ensure its safety and effectiveness following design changes.

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on demonstrating substantial equivalence to a predicate device (K193145) rather than explicitly stating acceptance criteria and reporting performance for each individual criterion in a pass/fail format. However, based on the provided "Performance Data" and the "Comparison of Technological Characteristics" tables, the acceptance criteria implicitly involve demonstrating that the device's technical specifications and performance are either identical to the predicate or that any differences do not adversely affect safety and effectiveness.

Here's a summary of the implied acceptance criteria and the device's reported performance:

2. Sample size used for the test set and the data provenance:

• Sample Size: For "Bench Testing (Thermal Effects on Tissue)," testing was performed "in triplicate at minimum". This indicates at least three repetitions for each combination of Generator mode and power setting. The document does not specify a total number of tissue samples or test runs beyond this.
• Data Provenance: The data appears to be prospective bench testing conducted specifically for the purpose of this 510(k) submission to demonstrate the performance of the modified device. The document does not mention the country of origin of the data, but it is implied to be generated by the manufacturer or its contracted laboratories for the FDA submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• The document does not mention the use of experts to establish ground truth for the bench testing. The evaluation of thermal effects was done through "image analysis using open source image processing software." This suggests an objective, quantitative measurement rather than expert interpretation for the thermal effects study. For other tests like electrical safety and EMC, ground truth is established by adherence to specified standards.

4. Adjudication method for the test set:

• None specified. Given the nature of the tests (compliance with standards, objective thermal measurement via software), an adjudication method in the context of expert consensus or disagreement is not applicable or explicitly mentioned.

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:

• No MRMC study was done. This device is an electrosurgical generator, not an AI-powered diagnostic or assistive tool for human readers. Therefore, an MRMC study and analysis of AI-assisted human performance are not relevant to this submission.

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

• Not applicable directly as an "algorithm only" study. The device itself is electro-mechanical, with embedded software. Software verification and validation (V&V) was performed to ensure the software functions correctly independently, but this is not a "standalone algorithm" performance in the sense of a diagnostic or predictive AI. The entire device's performance, including its software, was assessed.

7. The type of ground truth used:

• Compliance with International Standards: For electrical safety and EMC, the ground truth is defined by the requirements and test methods specified in the relevant IEC 60601 series standards.
• Objective Measurement: For thermal effects on tissue, the ground truth was based on quantitative measurements of thermal damage obtained through image analysis software.
• Manufacturer's Specifications/Design Intent: For other characteristics like operating parameters and device features, the ground truth is established by the device's design specifications and comparison to the predicate device's known characteristics.

8. The sample size for the training set:

• Not applicable. This submission is for an electrosurgical generator, which is not an AI/ML-based device that would typically involve a "training set" for model development. The software V&V confirms the performance of the embedded software, but it's not a machine learning model that learns from a training dataset.

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

• Not applicable. As a training set is not relevant for this type of device, this question is not pertinent.

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The Ethicon Megadyne® Electrosurgical Generator is intended as a general-purpose electrosurgical generator designed to produce radio frequency (RF) current for cutting and coagulation to target tissue through an accessory electrode during open and laparoscopic surgical procedures.

The Ethicon Megadyne Electrosurgical Generator is a microprocessor controlled, isolated output, high frequency generator designed for use in cutting and coagulation of tissue. The generator has the ability to perform both monopolar cutting and coagulation and bipolar coagulation of tissue in a wide range of surgical applications.

This document is a 510(k) premarket notification for an electrosurgical generator. It does not pertain to an AI/ML powered device, therefore, the requested information regarding acceptance criteria, study design for AI/ML performance, sample sizes for training/test sets, expert ground truth establishment, adjudication methods, MRMC studies, standalone performance, and ground truth type is not applicable.

The document discusses the substantial equivalence of the Ethicon Megadyne Electrosurgical Generator to a predicate device (Mega Power Electrosurgical Generator). The acceptance criteria and the study proving the device meets these criteria are described in terms of safety and performance bench testing, rather than AI/ML performance metrics.

Here's a breakdown of the relevant information provided, adapted to the context of this traditional medical device:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't present a formal table of "acceptance criteria" and "reported device performance" in the way one would for an AI/ML model with metrics like sensitivity/specificity. Instead, it describes compliance with recognized electrical safety and electromagnetic compatibility standards and comparative bench testing.

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

• Sample Size: For the thermal effects on tissue, testing was performed in triplicate at minimum, default and maximum power settings for each tissue type (skeletal muscle, liver, kidney) and each generator mode. The exact number of tissue samples is not explicitly stated beyond "triplicate" per condition.
• Data Provenance: The thermal effects testing used ex-vivo harvested animal tissue (skeletal muscle, liver, and kidney). The country of origin is not specified, but it's presumed to be associated with the manufacturer's testing facilities. The nature of the study is bench testing rather than retrospective or prospective clinical data.

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

• Not Applicable in the traditional AI/ML sense. For this device, "ground truth" for thermal effects would be the measurable thermal change on the tissue. The measurement was done through "image analysis using open source image processing software." It does not involve human expert consensus or labeling of images for diagnostic purposes. The expertise would lie in the design and execution of the bench tests and the interpretation of the physical measurements, not in expert clinical review for ground truth.

4. Adjudication Method for the Test Set:

• Not Applicable. As this is not an AI/ML diagnostic device with expert annotation, there is no need for an adjudication method for labeling a test set. The data comes from physical measurements in a controlled bench setting.

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. This is not an AI/ML diagnostic device that assists human readers. Therefore, an MRMC study is irrelevant to this submission.

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

• Not Applicable. This is an electrosurgical generator, a hardware device, not a standalone AI/ML algorithm. Its performance is evaluated through bench testing of its physical output and safety features.

7. The Type of Ground Truth Used:

• For thermal effects on tissue, the "ground truth" was established by direct measurement of thermal damage through image analysis of ex-vivo animal tissue, compared to the performance of the predicate device.
• For electrical safety, EMC, and software validation, the ground truth is compliance with relevant recognized consensus standards (IEC standards) and FDA guidance documents.

8. The Sample Size for the Training Set:

• Not Applicable. This device does not use an AI/ML algorithm that requires a training set. The "training" in this context refers to engineering design and development, not machine learning.

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

• Not Applicable. No training set or associated ground truth for machine learning was established for this device.

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