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Diode laser system is used for hair removal, HR mode is for professional hair removal, and FHR is for rapid hair removal. The indications for use for the 810nm Diode Laser Module include: The HR mode and FHR mode are intended for hair removal and permanent hair reduction. Use on all skin types (Fitzpatrick I-VI) including tanned skin.

Permanent hair reduction is defined as the long-term, stable reduction in the number of hairs regrowing when measured at 6, 9, and 12 months after the completion of a treatment regime.

The product is intended for the removal of unwanted body hair. It is suitable for all skin types, including tanned skin. The Diode laser system consists of the following main components:

1. The main unit, including the CPU main board, power supply module, control panel, service panel and cooling system.
2. Handlpiece, including the diode laser, TEC, handle switch, signal, cable and plug.
3. Foot switch.

The provided FDA 510(k) clearance letter (K250206) for the "Diode laser system (Night Universe; Predator)" does not contain information typically associated with AI/ML device performance studies, specifically regarding acceptance criteria, test set details (sample size, provenance, expert adjudication), MRMC studies, standalone algorithm performance, or ground truth establishment for training and test sets.

The information primarily focuses on the device's technical specifications, non-clinical test results, and substantial equivalence to predicate devices for its intended use (hair removal and permanent hair reduction). The testing described relates to electrical safety, electromagnetic compatibility, laser safety, and biological evaluation (biocompatibility) under various IEC and ISO standards.

Therefore, I cannot populate the requested table or answer the specific questions about the study that proves the device meets acceptance criteria in the context of an AI/ML-driven medical device.

Here's why the requested information is not present in the document:

• Device Type: The "Diode laser system" is a hardware medical device (laser surgical instrument) used for a physical intervention (hair removal). It is not an AI/ML-driven diagnostic or prognostic device that would require performance metrics like sensitivity, specificity, AUC, or studies involving interpretation of medical images by experts.
• Acceptance Criteria Mentioned: The acceptance criteria mentioned in the document are related to compliance with safety and performance standards for laser devices (e.g., IEC 60601-1, IEC 60601-2-22, IEC 60825-1) and biocompatibility (ISO 10993). The "reported device performance" in this context would be that the device successfully passes these standard-based tests (e.g., emits laser at specified wavelengths and power, is electrically safe, compatible with human tissue).
• No AI/ML Component: There is no indication or mention of any artificial intelligence or machine learning component in the device's description, function, or the studies performed to support its clearance.

In summary, the provided document does not describe the kind of acceptance criteria or performance study typically associated with AI/ML medical devices.

If the question implicitly assumes an AI/ML component based on the general prompt, then the provided text does not support that assumption.

However, if we strictly interpret "acceptance criteria" and "study" as presented in the document for this specific device, here's what can be inferred:

Acceptance Criteria and Study for Diode Laser System (Night Universe; Predator)

The acceptance criteria for this device are primarily based on its compliance with established international safety and performance standards for medical electrical equipment and laser products. The study proving these criteria are met consists of non-clinical bench testing to demonstrate adherence to these standards and substantial equivalence to predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: Not applicable in the context of clinical trial-like "test sets" for diagnostic performance. The "test" consists of a single device (or representative samples) undergoing bench testing against regulatory standards.
• Data Provenance: Not applicable in the sense of patient data. The "data" comes from engineering and laboratory test results on the device itself.

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

• Not applicable. "Ground truth" in this context refers to the defined parameters and safety requirements of the standards themselves, not a medical "diagnosis" by experts. Compliance is assessed by accredited testing laboratories and regulatory bodies.

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

• Not applicable. This concept pertains to expert review for diagnostic devices, not hardware safety testing.

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. This device is not an AI-assisted diagnostic tool.

6. 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 for performance evaluation. Its "standalone performance" is its adherence to physical and electrical safety standards.

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

• The "ground truth" for this device's acceptance is based on engineering and biophysical principles codified in international consensus standards (IEC, ISO). For example, a "safe" laser emission level is determined by these standards. "Performance" is meeting the specified output parameters safely.

8. The sample size for the training set:

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

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

• Not applicable.

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