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The CRF Radiofrequency Ablation System is indicated for use in percutaneous, laparoscopic and intraoperative coagulation and ablation of tissue.

The CRF Radiofrequency Ablation System consists of an RF generator, active electrode, grounding pad, and peristallic punp for electrode cooling. This device is designed to cal tissue heating at the tip of the electrode causing coagulation and ablation of tissue. The CRF Generator supplies radiofrequency energy at 480 kHz while monitoring both the tissue imperature at the CRF electrode tip. The CRF Generator monitors the power, resistance, current and temperature during energy delivery to the paient.

The CRF Electrodes are sterile, single-use, hand-held for use in radiofrequency ablation procedures. The electrodes are surgically invasive, transient devices in patient contact typically for less than one hour. Cooling of the electrodes is provided by chilled sterile saline or water that is pumped through the electrode and out via outflow tubing. This is an enclosed system within the electrode and the saline does not contact. The CRF Generator automatically monitors impedance and temperature and adjusts RF output accordingly. The CRF Radiofrequency Ablation System is used in a healthcare facility or hospital setting.

This is a 510(k) premarket notification for a medical device (CRF Radiofrequency Ablation System), not an AI/ML device. Therefore, the traditional acceptance criteria and study designs relating to AI/ML performance metrics (such as sensitivity, specificity, AUC, MRMC studies, and expert adjudication as ground truth) are not applicable here.

The document describes the device's substantial equivalence to a predicate device based on non-clinical data.

However, to address the prompt's structure as best as possible within the context of the provided document, I will reframe some of the categories to fit the information given, focusing on the comparisons and testing performed for this physical medical device.

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

The document does not explicitly present "acceptance criteria" in the traditional sense of numerical thresholds for AI/ML performance (e.g., sensitivity > X%). Instead, the "acceptance" for this 510(k) submission relies on demonstrating substantial equivalence to a predicate device through various non-clinical tests and comparing technological characteristics. The reported device performance is presented as compliance with standards or successful demonstration of equivalence in specific tests.

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

• Sample Size for Test Set: Not applicable in the context of an AI/ML device for which this question is typically posed. For this medical device, various non-clinical tests were conducted:
  • Mechanical, Electrical, EMC: Implied to be on a sufficient number of device units to establish compliance with standards. Details on the exact number of units are not provided but are typically part of a detailed testing report not fully included in this summary.
  • Benchtop Ablation: Performed using an "ex vivo tissue model." The number of tissues or ablation cycles is not specified.
  • Usability, Biocompatibility, Sterilization, Packaging/Shelf Life, Software: Applied to the device and its components.
• Data Provenance: Not applicable in the AI/ML sense. All data appears to be from prospective non-clinical testing conducted specifically for this submission by the manufacturer or authorized labs (e.g., benchtop tests, lab tests for electrical safety, biocompatibility, etc.). Country of origin of data is not specified but would typically be where the manufacturing and testing facilities are located.

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. This is not an AI/ML device requiring expert-established ground truth for image interpretation or diagnosis. Ground truth for this device is established through compliance with engineering standards (e.g., electrical safety, mechanical requirements) and observed physical outcomes in benchtop models (e.g., ablation performance). The "experts" involved are likely engineers, quality assurance personnel, and regulatory affairs specialists who conduct and interpret the non-clinical tests.

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

Not applicable. This device does not involve a diagnostic task requiring human-expert adjudication for a 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

Not applicable. This is not an AI/ML device, and no MRMC study was conducted. No human "readers" are involved with the device in this capacity.

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

Not applicable. This is a physical medical device, not an algorithm. Therefore, "standalone" performance for an algorithm is not relevant. The device operates with a human operator (surgeon/clinician) in a "human-in-the-loop" manner by design, but its performance is evaluated in non-clinical settings without directly assessing human improvement from an AI perspective.

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

The "ground truth" for this device's assessment of substantial equivalence relies on:

• Compliance with codified U.S. and international standards (e.g., IEC 60601 series, ISO 11135, ASTM F1980-16, ISO 10993-1).
• Direct measurement and observation of physical characteristics and performance in laboratory and benchtop settings (e.g., electrical parameters, ablation zone size in ex vivo tissue, material properties, sterilization efficacy).
• Comparison to the established characteristics and performance of a legally marketed predicate device.

8. The sample size for the training set

Not applicable. This device does not involve AI/ML algorithms that require a "training set."

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

Not applicable. As there is no training set, there is no ground truth established for it.

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