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The DIROS OWL™ URF-3AP(ML) SYSTEM is intended for the following:

1. Lesioning nerve tissue for functional neurosurgical procedures such as thalamotomies, pallidotomies, tractomies, and myelotomies; or
2. radiofrequency heat lesion procedures for the relief of pain
   The MLA-4 Multi-Lesion adaptor is not intended for use in brain surgery.

The Diros OWL™ radiofrequency generator URF-3AP(ML) is used by qualified medical personnel to make heat ablative lesions in central or peripheral nerve tissue for the treatment of movement disorders or for the relief of pain. The lesions are ablative in order to be therapeutic; i.e. the destruction of a small portion of the thalamus within the brain interferes with the motor pathway causing the tremor of Parkinson's disease, thereby relieving the tremor; or the destruction of the facet joint nerves in the lumbar vertebrae to block pain transmission by these nerves and thereby relieve certain types of low back pain.
The URF-3AP(ML) supplies up to 50 Watts of Radio Frequency energy at 481kHz under power or temperature control while continuously monitoring and displaying actual power delivered, measured probe temperature, time of power duration, and measured impedance. This 50W RF source can be directed to one of four channels, one at a time, to create simultaneously up to four lesions.
When used with monopolar probes, the system also delivers low-frequency stimulus pulses in either voltage or current controlled modes.

The provided text from the 510(k) submission for the DIROS OWL™ RF LESION GENERATOR MODEL URF-3AP WITH MULTI-LESION ADAPTOR (MLA-4) does not contain the specific information required to complete a table of acceptance criteria and reported device performance related to diagnostic accuracy, sensitivity, or specificity, nor does it detail a study that proves the device meets such criteria in the way a diagnostic AI device submission would.

Instead, this document focuses on demonstrating substantial equivalence to predicate devices for a radiofrequency lesion generator, which is a therapeutic device. The performance testing outlined here is primarily centered on safety, electrical, mechanical, and EMC standards, and ensuring the device performs its intended function (generating RF energy, monitoring temperature, impedance, etc.) safely and effectively, rather than on diagnostic accuracy metrics.

Therefore, many of the requested fields cannot be populated based on the provided text.

Here's an attempt to answer what is possible, and clearly indicate what information is not available within the provided text:

Acceptance Criteria and Device Performance

Study Details (Based on available information)

1. Sample size used for the test set and the data provenance:

   • Not Applicable / Not Provided. The document describes performance testing for a therapeutic device (RF Lesion Generator) focused on safety and functional performance, not diagnostic accuracy. Therefore, there isn't a "test set" in the sense of a dataset of patient images or clinical cases for evaluation of diagnostic performance. The testing involved electrical, mechanical, and software verification as per medical device standards.

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

   • Not Applicable / Not Provided. As this is not a diagnostic device undergoing a diagnostic performance study, there's no concept of "ground truth" derived from expert consensus on medical images or patient outcomes in the context of this 510(k) submission.

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

   • Not Applicable / Not Provided. There was no "test set" requiring adjudication in the context of diagnostic performance evaluation.

4. 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 / Not Performed. This device is an RF lesion generator, not an AI-powered diagnostic tool. Therefore, an MRMC study related to human reader improvement with AI assistance is not relevant and was not conducted.

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

   • Not Applicable / Not Performed. This is not an algorithm-based diagnostic device. Performance testing focused on the device's hardware and software functionality in isolation (standalone electrical, mechanical, software testing).

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

   • Not Applicable / Not Provided in the context of diagnostic "ground truth." The "ground truth" for this device's performance would be derived from instrumental measurements (e.g., power output, temperature readings, impedance values) against known calibrated standards, and successful completion of predefined engineering and safety tests according to international and national standards.

7. The sample size for the training set:

   • Not Applicable / Not Provided. This is not an AI/ML device that requires training data.

8. How the ground truth for the training set was established:

   • Not Applicable / Not Provided. This is not an AI/ML device.

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