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joimax® Bipolar Electrosurgical Instruments
The joimax® bipolar Vaporflex and Legato electrosurgical instruments are used for coagulation of soft tissue during open or minimally invasive surgical procedures when used in conjunction with a compatible radio frequency generator. The joimax® bipolar electrosurgical instruments have not been shown to be effective for tubal coagulation for sterilization procedures and should not be used for these procedures.

joimax® Monopolar Electrosurgical Instruments
The joimax® monopolar Legato electrosurgical instruments are used for cutting and/or coagulating soft tissue during open or minimally invasive surgical procedures when used in conjunction with a compatible radio frequency generator.
The joimax® monopolar electrosurgical instruments have not been shown to be effective for tubal sterilization or tubal coagulation for sterilization procedures and should not be used for these procedures.

The joimax® Electrosurgical Instruments are comprised of a series of instruments to facilitate the delivery of electrical energy from an RF generator to the patient tissue for use in cutting and/or coagulation of tissue.
The joimax® Electrosurgical Instruments use Radio Frequency (RF) energy to induce thermal effects on tissue. The radio frequency is generated by high oscillating electrical current received from a commercially available RF generator.
The joimax® Probes are available in both monopolar and bipolar probe configurations. All joimax® Probes are designed for specific use with joimax® Handpieces. The joimax Handpieces can be used with compatible commercially available surgical generators.

The provided text discusses the joimax® Electrosurgical Instruments and their substantial equivalence to predicate devices, focusing on safety and performance. However, it does not explicitly detail a study with specific acceptance criteria and detailed performance metrics as one might expect for an AI/ML device. The document primarily focuses on demonstrating that the electrosurgical instruments meet general safety and performance standards equivalent to existing legally marketed devices.

Based on the provided text, here's a breakdown of the requested information, noting where specific details are not available for this type of device (e.g., sample sizes for AI/ML models, expert ground truth for image interpretation).

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a formal table of acceptance criteria with corresponding performance results in a quantitative manner for a comparative study against a specific numerical target. Instead, it confirms compliance with various standards and qualitative comparisons to predicate devices. The "acceptance criteria" are implied by adherence to these standards and the demonstration of similar performance to predicates.

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

For the performance testing, the document states: "Ex-vivo testing was conducted using multiple types of soft animal tissue (kidney, liver and muscle tissue)." It does not specify a numerical sample size for the ex-vivo test set, nor does it provide provenance details like country of origin or whether the data was retrospective or prospective. This is typical for device performance testing involving animal tissue, where the focus is on demonstrating functional equivalence under controlled conditions rather than clinical dataset characteristics.

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 type of information (number and qualifications of experts for ground truth) is usually relevant for AI/ML devices that interpret data (e.g., images). For electrosurgical instruments, "ground truth" related to tissue effect is typically established through direct observation, standardized pathological assessment, or established physical/chemical measurements by qualified technical personnel or pathologists, rather than expert consensus on interpretation. The document does not specify the number or qualifications of experts involved in assessing the ex-vivo tissue effects, but rather that effect was "assessed."

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

Adjudication methods like 2+1 or 3+1 are typically used for establishing ground truth in AI/ML studies where human interpretation of complex data (like medical images) is involved. For performance testing of electrosurgical devices, the assessment of tissue effect is often based on objective measurements or standardized observational criteria, and thus explicit adjudication among multiple reviewers isn't usually described in this context. The document does not mention any adjudication method for the performance test set.

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

An MRMC comparative effectiveness study is not mentioned as this device is an electrosurgical instrument, not an AI-assisted diagnostic or interpretive system that human readers would use to improve their performance. Therefore, there is no discussion of human reader improvement with or without AI assistance.

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

This question is not applicable. The device is a physical electrosurgical instrument, not an algorithm. Therefore, "standalone" algorithm performance is not a relevant concept here. The performance described is of the physical instrument itself.

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

For the performance testing, the "ground truth" for the effect on tissue was likely established through direct observation during ex-vivo testing and possibly subsequent pathological assessment, although pathology is not explicitly named. The document states, "Effect on tissue was assessed and determined to be highly comparable to the predicate device." This "assessment" forms the basis of the functional ground truth for the device's intended use.

8. The sample size for the training set

This question is not applicable as this is not an AI/ML device. Therefore, there is no "training set" in the context of machine learning.

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

This question is not applicable as this is not an AI/ML device with a training set.

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