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The RITA® System (Model 500 RF Generator and Accessories) supplies energy for use in electrosurgery, and are indicated for use in percutaneous, laparoscopic, or intraoperative coagulation and ablation of soft tissue, including the partial or complete ablation of non-resectable liver lesions.

The RITA Model 500 Electrosurgical Generator and Accessories are designed to create coagulative necrotic lesions in soft tissue, and allow localized delivery of fluid to the lesion. The RF Generator is specifically designed for use with RITA Electrosurgical Devices (Accessories). The RF Generator provides multiple temperature sensors, impedance, and power monitoring to assist the user in delivering the desired energy to the target tissue. The Accessories consist of monopolar electrosurgical devices that include disposable electrosurgical probes with deployable needle arrays that deliver RF power with temperature and impedance monitoring to ablate predictable volumes of tissue.

The provided text does not contain specific acceptance criteria or a detailed study description with performance metrics in a structured format often associated with AI/ML device evaluations. Instead, it describes a traditional medical device (an electrosurgical RF generator) and its clinical studies, which are assessed differently from typical AI/ML devices.

However, based on the information provided, here's an attempt to answer the questions, focusing on the available details for a non-AI device.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The text mentions "Clinical studies were conducted" and "In one study, lesions were ablated in patients who were scheduled for surgical resection. In other studies, the RITA System was used for the ablation of cancerous liver lesions (primary and metastatic) in patients whose liver cancer was considered to be non-resectable."

• Sample Size: Not explicitly stated. The text refers to "patients," indicating human subjects, but no specific number is provided for any of the studies mentioned.
• Data Provenance: Not explicitly stated (e.g., country of origin). The studies appear to be prospective clinical studies, as they involved patients undergoing ablation procedures specifically for the purpose of evaluating the device.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

Not applicable in the context of this device and the provided text. The evaluation method described is clinical outcome and physical measurements (CT scans), not expert consensus on interpretations.

4. Adjudication Method for the Test Set

Not applicable. The clinical studies assessed outcomes directly (e.g., morbidity, adverse events, ablation size from CT scans), not expert interpretations 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

Not applicable. This device is an electrosurgical RF generator, not an AI/ML imaging or diagnostic aid. Therefore, no MRMC study or AI-assisted improvement analysis would be relevant.

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

Not applicable. This is a medical device that requires direct human operation and intervention for its function. It is not an algorithm that can operate standalone.

7. The Type of Ground Truth Used

The ground truth or evaluation metrics were based on:

• Clinical Outcomes/Events: Morbidity, adverse event rates, and mortality ("no reports of death related to use of the devices")
• Imaging Data: Post-procedure CT scans to determine ablation diameter (consistently averaged 3-4 cm).

8. The Sample Size for the Training Set

Not applicable. This pertains to AI/ML models. The device's performance was evaluated through clinical studies, not by training an algorithm on a dataset.

9. How the Ground Truth for the Training Set was Established

Not applicable, as there is no training set for an AI/ML model for this device.

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