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The intended use for Cobra Adhere Surgical System is as follows: The Cobra Adhere Surgical System (System) is intended for the coagulation of cardiac tissue using radiofrequency (RF) energy during cardiac surgery. The System can be used during general surgery to coagulate soft tissues. The System may also be used to coagulate blood and soft tissue to produce hemostasis.

The Cobra Adhere Surgical System is an Electrosurgical Probe, with either a malleable or flexible shaft, used in conjunction with the Cobra Electrosurgical Unit (ESU). The system is intended for use by surgeons for the coagulation of cardiac and soft tissues during open surgical procedures.

This looks like a 510(k) submission for a medical device without an AI/ML component. The information provided heavily emphasizes hardware characteristics and intended use for electrosurgical probes, not software or algorithmic performance.

Therefore, many of the requested categories related to AI/ML device performance and testing (such as "sample size used for the test set," "number of experts used to establish ground truth," "adjudication method," "MRMC study," "standalone performance," "training set size") are not applicable to this document.

The document states: "non-clinical tests conducted for the Device showed the device met its design-input criteria, and was safe and effective for its intended use." However, it does not provide specific acceptance criteria or detailed study results for these non-clinical tests.

Here's a breakdown of the requested information, highlighting what is available and what is not applicable:

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

• Acceptance Criteria: Not explicitly stated in the provided document. The document generally refers to "design-input criteria" but does not detail what these criteria were.
• Reported Device Performance: The document states that "non-clinical tests conducted for the Device showed the device met its design-input criteria, and was safe and effective for its intended use." However, no specific performance metrics (e.g., coagulation time, temperature achieved, lesion size, safety endpoints) or quantitative results from these tests are provided.

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

• Not applicable for an AI/ML device. This document describes an electrosurgical probe, not an AI/ML system that processes data. The "non-clinical tests" likely involved bench testing and potentially animal models, but no details on sample size or provenance are given.

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 for an AI/ML device. Ground truth, in the context of AI/ML, refers to validated labels for data used to train and test algorithms. This device does not use ground truth in that sense.

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

• Not applicable for an AI/ML device. Adjudication relates to resolving discrepancies in ground truth labeling for AI/ML data.

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 device. Therefore, no MRMC study comparing human readers with and without AI assistance would have been conducted or is relevant.

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

• No. This is a physical electrosurgical probe, not a standalone algorithm.

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

• Not applicable for an AI/ML device. See point 3.

8. The sample size for the training set

• Not applicable for an AI/ML device. This device is hardware; it does not have a "training set" in the context of machine learning.

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

• Not applicable for an AI/ML device. See point 8.

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