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Electrosurgical unit « MCB » is intended for use for the ablation, removal, resection, and coagulation of soft tissue, and where associated hemostasis is required in endoscopic urological surgical procedures.

The device is intended for use by qualified medical personnel trained in the use of electrosurgical equipment.

MCB is a reusable, non-sterile electrosurgical bipolar generator with cutting and coagulation modes. The maximum output power is 500 W.

The front panel GUI (graphical user interface) features soft keys and digital displays for:
• the connection status of accessories connected to the electrosurgical generator.
• the current settings of the chosen output mode (Cut/ Coag), and possibility to adjust it
• Sound Level adjustment and LEDs (Green for Sound and Yellow/Blue for output activation)
• Electrode shortcut Alarm reset

At switch on, Serial Number and Software Version are displayed

The provided text is a 510(k) summary for the MCB UNIT Model: V10GMCBUS electrosurgical unit. It includes information about the device, its intended use, and the studies conducted to demonstrate substantial equivalence to a predicate device. However, it does not contain the detailed acceptance criteria and a specific study proving the device meets those criteria, as typically found in clinical performance studies.

The document primarily focuses on non-clinical testing and regulatory compliance, not on clinical performance metrics with acceptance criteria.

Therefore, I cannot fulfill all parts of your request based on the provided text. I can, however, extract the relevant non-clinical performance data and what is described regarding the validation studies.

Here's what can be extracted and inferred:

1. A table of acceptance criteria and the reported device performance:

The document does not explicitly state numerical acceptance criteria for specific device performance metrics in a format that would allow for a table comparing "acceptance criteria" against "reported device performance" in a clinical context (e.g., sensitivity, specificity, accuracy for an AI device).

Instead, the non-clinical performance data section refers to validation studies based on recognized standards and FDA guidance for electrosurgical devices. The conclusion states that "Slight differences do not raise any questions regarding safety and effectiveness," implying that the device's performance, as evaluated against these standards, demonstrated sufficient safety and effectiveness to be substantially equivalent to the predicate.

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

The document mentions "Thermal Effect studies on representative tissues for urological Application" in the "Summary of the Non-Clinical performance data" section. However, it does not provide any details regarding:

• The sample size of these "representative tissues."
• The data provenance (e.g., country of origin, retrospective or prospective nature of the tissue samples).

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):

This information is not provided in the document. The studies mentioned are non-clinical, focusing on the electrosurgical unit's physical characteristics and compliance with electrical safety and usability standards. The concept of "ground truth" established by experts, as it would apply to diagnostic AI devices, does not directly apply here.

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

This information is not provided as the studies are non-clinical and do not involve human interpretation of diagnostic data requiring adjudication.

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 device is an electrosurgical unit, not an AI diagnostic tool. Therefore, a multi-reader multi-case (MRMC) comparative effectiveness study to assess human reader improvement with AI assistance is not applicable and was not performed.

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

This is an electrosurgical hardware device, not an AI algorithm. So, a standalone algorithm performance study is not applicable.

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

For the non-clinical performance data, the "ground truth" would be established by the physical and electrical safety standards outlined (e.g., IEC 60601-2-2 for Safety of Electrosurgical Generator, IEC 60601-1-2 for EMC). The "Thermal Effect studies on representative tissues" would presumably use objective measurements of tissue effects (e.g., lesion depth, coagulation extent) against expected outcomes based on the device's settings and the predicate device's known performance. However, the specific methodology and objective measures are not detailed.

8. The sample size for the training set:

This device is an electrosurgical unit. There is no mention of a training set as it is not an AI/ML device that requires data for training algorithms.

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

As there is no training set for an AI/ML algorithm, this question is not applicable.

Summary of what is present:

• Non-clinical performance data: Validation studies were based on recognized standards (ISO 14971, IEC 62304, IEC 62366-1, IEC 60601-2-2, IEC 60601-1-2) and FDA guidance for electrosurgical devices (March 9, 2020), specifically for "Thermal Effect studies on representative tissues for urological Application."
• Software validation: Based on "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005), with the software considered a "Moderate Level of Concern."
• Usability: Assessed and found to be safe and effective for its intended uses.
• Overall Conclusion: Substantial equivalence to the predicate device (GYRUS ACMI Inc. PK SUPERPULSE SYSTEM GENERATOR MODEL 744000, 510k Number : K100816) based on same Indications for Use, similar technological and technical characteristics, and results of non-clinical tests.

The document primarily demonstrates compliance with regulatory and safety standards, rather than clinical performance against specific acceptance criteria like an AI diagnostic device would.

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