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The Racz Neurostat RF Generator is intended for lesioning of neural tissue. It is indicated for use in the peripheral nervous system. It is to be used with Epimed RF probes and cannula.

The Racz Neruostat RF Generator is a touchscreen controlled Radio Frequency (RF) Generator used to lesion neural tissue for pain management. It has 4 outputs for delivering RF from a single source into the patient, it includes functions for nerve stimulation (Sensory and Motor), Continuous Thermal RF Lesioning, Pulsed RF Lesioning and Pulsed Dose RF Lesioning. The RF Energy is transmitted via each individual probe and a Neutral Electrode when in monopolar mode; or between probes when running in bipolar mode. The device will monitor the patient's impedance, probe temperature, along with the voltage and current of the RF Energy during a procedure.

Here's an analysis of the provided text regarding the acceptance criteria and the study that proves the device meets them, structured according to your requested information.

Based on the provided FDA 510(k) summary for the Racz Neurostat RF Generator (K232632), the information focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with specific acceptance criteria that would typically be seen for a novel AI/software-based medical device.

The Racz Neurostat RF Generator is a radiofrequency lesion generator, a hardware device, and not an AI or software algorithm in the traditional sense that generates diagnostic output or assists human readers in interpretation. Therefore, many of the typical questions for AI/software performance studies (like sample size for test/training sets, expert ground truth, MRMC studies, standalone performance, etc.) are not directly applicable or explicitly detailed in this type of submission.

The "acceptance criteria" discussed in this document are primarily related to safety, electrical performance, and functional equivalence to a predicate device, rather than diagnostic accuracy or clinical effectiveness in the way an AI algorithm would be evaluated.

Acceptance Criteria and Device Performance (as inferred from the 510(k) summary)

The "acceptance criteria" for a hardware device like this are generally satisfied by demonstrating compliance with recognized standards and functional equivalence to a legally marketed predicate device.

Detailed Study Information (Based on provided text):

As the device is a hardware RF generator with embedded software for control, the "study" is more akin to engineering testing and validation rather than a clinical trial assessing diagnostic performance.

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

• Test Set Sample Size: Not applicable/not specified in the context of an FDA 510(k) for a hardware device like this. The "test set" for this device refers to the specific physical units or simulated environments on which engineering and performance tests were conducted. These are not datasets of patient images or clinical data.
• Data Provenance: Not applicable. The "data" being generated and tested are electrical signals, temperature readings, and physical lesion formation, not patient-derived medical data. The tests are likely performed in a lab setting.

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

• Not applicable for this type of device. "Ground truth" for this device would be established by engineering specifications, physical laws, and recognized medical device standards (e.g., a specific power output measured by calibrated equipment, or a lesion size measured by a pathologist/engineer during comparative testing). There's no subjective expert interpretation required in the same way as for image-based diagnostics.

4. Adjudication method for the test set:

• Not applicable. The "adjudication" for this type of device involves comparing measured performance against engineering specifications and predicate performance, not resolving discrepancies between expert readings.

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, an MRMC study was not done. This type of study is relevant for AI systems that aid human interpretation (e.g., radiologists reading images). This device is a therapeutic generator, not a diagnostic aid.

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

• The device inherently operates as a "standalone" therapeutic device, meaning its core function (generating RF energy) does not require "human-in-the-loop" interpretation for its primary output. However, it is human-controlled to perform procedures. Performance testing was done on the device itself (hardware and embedded software), as detailed in the "Performance Testing" section.

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

• The "ground truth" for this device's performance would be established through a combination of:
  • Engineering specifications: (e.g., accurate power output, temperature control).
  • Measurement against calibrated standards: using specialized test equipment.
  • Physical demonstration: (e.g., the ability to generate a lesion of a certain size/shape in a controlled medium during "Comparative Lesion Testing").
  • Compliance with recognized standards: (e.g., IEC 60601 series).
  • Functional equivalence to predicate: demonstrating it performs the same functions as the already cleared predicate device.

8. The sample size for the training set:

• Not applicable. This device does not use machine learning in a way that requires a "training set" for an AI model.

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

• Not applicable, as there is no training set for an AI model.

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