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The ZIONIC PRO MAX (radiofrequency) equipment when used with the capacitive or resistive handpiece is indicated to provide topical heating for the purpose of elevating tissue temperature for the treatment of selected medical conditions such as temporary relief of pain, muscle spasms, and increase in local circulation. The massage device is intended to provide a temporary reduction in the appearance of cellulite.

The Zionic Pro Max with Radiofrequency is a non-invasive topical heating device that produces RF energy to warm subcutaneous tissue the therapeutic temperature of 40-45°C. The device includes two different type of RF handpieces, resistive and capacitive, which are connected to a console and software-powered user interface. The Zionic Pro Max adds a new capacitive handpiece and user interface to a previously cleared generation of the device.

The provided FDA 510(k) clearance letter for the Zionic Pro Max (Radiofrequency) device primarily concerns the safety and effectiveness of a medical device intended for topical heating and reduction of cellulite. It is not an AI/ML device that deals with image analysis, diagnosis, or prediction, and therefore, many of the typical acceptance criteria and study details associated with AI/ML medical devices (like MRMC studies, ground truth establishment by experts, sample sizes for training/test sets in the context of machine learning, etc.) are not applicable to this document.

The document discusses performance testing in the context of electrical safety, electromagnetic compatibility, and the device's ability to achieve and maintain a specific tissue temperature. It does not involve any AI/ML components requiring complex ground truth adjudication or studies for human-in-the-loop performance improvement.

Therefore, I will extract relevant information that is present in the document pertaining to the device's performance criteria and how it was shown to meet them.

Acceptance Criteria and Reported Device Performance

The core performance criterion for the Zionic Pro Max device, as established through non-clinical testing, is its ability to heat tissue to a specific therapeutic temperature.

Study Details (Based on available information in the 510(k) Summary)

Since this is not an AI/ML device, the standard questions regarding AI/ML study design (such as data provenance, expert ground truth establishment for diagnostic performance, MRMC studies, training set details) are not directly addressed in this type of 510(k) summary. The "study" here refers to non-clinical, benchtop performance testing to ensure the device meets its physical and electrical specifications and performs its intended function (heating tissue) effectively and safely.

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

   • The document mentions "Tissue thermal testing" and "Bench testing" but does not specify the sample size (e.g., number of test repetitions, number of ex-vivo tissue samples, or number of devices tested).
   • Data Provenance: Not specified, but generally, bench testing data would be generated in a lab setting, likely by the manufacturer (TermoSalud S.L.) or a contracted testing facility. No indication of patient data or country of origin for such data.

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

   • Not applicable. The "ground truth" for this device's performance is determined by physical measurements (temperature, power, frequency, voltage) using calibrated equipment, not by expert interpretation of complex visuals or medical conditions. Therefore, no human experts are mentioned for establishing ground truth in this context.

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

   • Not applicable. Adjudication methods like 2+1 or 3+1 are used in studies where human experts are interpreting complex data (e.g., medical images) and their decisions need to be reconciled. This device's testing involves objective physical measurements.

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. This device is hardware for topical heating and cellulite reduction, not an AI/ML diagnostic or assistive tool. Therefore, MRMC studies are not relevant.

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

   • Not applicable. This device does not have a standalone algorithm in the sense of an AI/ML component performing diagnostic tasks. Its "performance" is based on the physical output and safety of the radiofrequency energy. The "software" mentioned (IEC 62304) relates to the control unit's functionality, not an AI/ML diagnostic algorithm.

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

   • The "ground truth" for the non-clinical testing appears to be measured physical parameters (e.g., temperature attained, electrical output values) against predefined specifications (40-45°C, accurate power/frequency/voltage).

7. The sample size for the training set:

   • Not applicable. There is no indication of a machine learning model being trained with a dataset mentioned in this 510(k) document. The "training" here refers to manufacturing and operational parameters, not data for an AI algorithm.

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

   • Not applicable. As there is no mention of an ML training set, there's no ground truth establishment method for it.

In summary, the provided document details the regulatory clearance for a physical medical device (radiofrequency equipment) based on its direct electrical and thermal performance characteristics, not on the performance of a software algorithm leveraging a dataset of medical cases.

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