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The Dyonics® Power-HERMES READY™ control unit is indicated for use, when used with appropriate procedure specific blades, for resection of soft and osseous tissues including, but not limited to, use in large articular cavities, small articular cavities, and Functional Endoscopic Sinus Surgery (FESS). The FESS application is limited to those small blades which are appropriate for the procedure.

The Dyonics® Power-HERMES READY™ control unit is is an electro-mechanical device containing systems, controls, and indicators which provides electrical power to motorized hand pieces and accessories for resection of soft and osseous tissue in arthroplasty, synovectorny and intraarticular cutting and shaving. The control unit allows a surgeon to set the blade speed within minimum and maximum speeds programmed for each blade type. The Dyonics® Power-HERMES READY™ control unit incorporates a communication interface for voice activation with the HERMES™ control center. When connected to the HERMES™ control center, the control unit may be voice activated or activated manual by use of a hand held pendant.

This PMA application describes a medical device, the Dyonics® Power-HERMES READY™ control unit, which is an electro-mechanical device that delivers power to motorized handpieces and accessories for tissue resection. The provided documentation does not include information about acceptance criteria or a study that proves the device meets those criteria in the way typically expected for an AI/ML or diagnostic device.

Here's an analysis based on the provided text, explaining why much of the requested information is absent and what is present:

The Dyonics® Power-HERMES READY™ device appears to be a conventional medical device, not an AI/ML-driven diagnostic or prediction tool. The submission is a 510(k) Pre-Market Notification, which focuses on demonstrating substantial equivalence to a predicate device rather than conducting extensive clinical efficacy trials with acceptance criteria thresholds for performance metrics like sensitivity, specificity, accuracy, or AUC.

Therefore, many of your specific questions are not applicable or cannot be answered from the provided text.

Here's a breakdown of the requested information:

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

Explanation: In a 510(k) submission for a non-AI/ML device like this, "acceptance criteria" are generally tied to demonstrating substantial equivalence to a previously cleared predicate device. This involves showing similar technological characteristics, intended use, and generally equal safety and effectiveness, often through adherence to recognized standards and engineering verification/validation, rather than a clinical performance study with statistical endpoints. The primary "performance" is that it functions as intended (delivering power to surgical tools) and is as safe and effective as the predicate.

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

• Not Applicable. This type of device (a power control unit for surgical instruments) does not typically involve a "test set" or clinical data provenance in the context of diagnostic performance or clinical outcomes directly generated by the device itself in the same way an AI/ML solution would. The evaluation is against engineering standards and comparison to a predicate.

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

• Not Applicable. Same reasoning as above. Ground truth is not established by medical experts for the functional performance of a power control unit in this context.

4. Adjudication method for the test set

• Not Applicable. Same reasoning as above.

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. This is not an AI-assisted diagnostic device, and therefore, an MRMC study with human readers/AI assistance is irrelevant.

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

• Not Applicable. This device does not have an "algorithm" in the sense of a standalone AI component. Its function is to control power to mechanical instruments.

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

• Not Applicable / Engineering Standards. For this device, the "ground truth" relates to whether the device meets its engineering specifications, safety standards, and functions as intended, consistent with the predicate device. This would be verified through engineering tests, electrical safety compliance, and functional validation against design outputs.

8. The sample size for the training set

• Not Applicable. As this is not an AI/ML device, there is no "training set."

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

• Not Applicable. As this is not an AI/ML device, there is no "training set" or ground truth for it.

In summary: The provided 510(k) summary for the Dyonics® Power-HERMES READY™ control unit demonstrates its substantial equivalence to a predicate device by focusing on:

• Similar intended use.
• Similar technological characteristics (with the addition of a communication interface for voice activation).
• Compliance with recognized electrical safety and medical device standards.

The questions you've asked are highly relevant for AI/ML-based medical devices or diagnostic imaging devices that rely on clinical performance metrics. This particular submission pertains to a more traditional electro-mechanical surgical control unit.

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