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The Diode Laser Machine (Model: BM-100) is intended for hair removal, permanent hair reduction on all skin types (Fitzpatrick skin type 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 proposed device incorporates a diode laser that emits invisible infrared laser radiation centered on a wavelength of 808 nm. Main components of the device include the laser console, liquid crystal display (LCD) touchscreen control panel, and laser handpiece. The device emits pulses of the 808 nm infrared laser emissions at pulse widths, repetition frequency, and energy levels, conductive to hair removal. The device incorporates water cooling, air cooling fan, and switches/buttons to control the laser output. The device is powered via an alternating current power source.

This document is a 510(k) Premarket Notification from the FDA for a Diode Laser Machine (Model: BM-100) intended for hair removal. It is not a report on an AI/ML medical device, therefore, the requested information regarding acceptance criteria, study details, expert involvement, and ground truth establishment, which are typical for AI/ML device clearances, is not available in this document.

The document focuses on demonstrating substantial equivalence to a predicate device (K162659) based on design specifications, performance, and safety standards, rather than clinical performance with an AI model.

Here's a breakdown of what is in the document related to "acceptance criteria" and "proof":

Acceptance Criteria and Reported Device Performance (as per this document's scope)

The "acceptance criteria" for this device, in the context of a 510(k), are not about statistical performance metrics of an AI model, but rather demonstrating that the device is as safe and effective as a legally marketed predicate device.

The "reported device performance" refers to the technical specifications and compliance with relevant safety standards, which are compared to the predicate device.

Table of Acceptance Criteria (as implied by 510(k) comparison) and Reported Device Performance:

Study Information (as requested, but mostly N/A for this document)

This document describes a 510(k) submission for a physical medical device (diode laser machine), not an AI/ML-based diagnostic or treatment planning system. Therefore, most of the requested points are not applicable and are not found in the provided text.

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

   • N/A. This document details non-clinical laboratory testing for safety and performance (e.g., electrical safety, EMC, laser safety, biocompatibility) rather than a clinical trial with a test set of patient data.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

   • N/A. Ground truth establishment using experts is not relevant to this type of device clearance which relies on engineering and physical performance testing and comparison to a predicate device.

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

   • N/A. Adjudication methods are typically for clinical studies with human readers or AI outputs, which is not the subject of this 510(k).

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:

   • N/A. This document explicitly states: "No clinical study is included in this submission." An MRMC study is not relevant as this is not an AI-assisted device.

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

   • N/A. This is a physical laser machine, not an algorithm.

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

   • N/A. The "ground truth" for this device's clearance pertains to the objective measurements of its physical parameters (e.g., wavelength, power, safety compliance) against established engineering standards and specifications of the predicate device.

7. The sample size for the training set:

   • N/A. The concept of a "training set" applies to machine learning models, which is not applicable to this device.

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

   • N/A. Not applicable.

In summary, the provided FDA 510(k) document is for a conventional medical device (diode laser) and does not involve AI/ML components or associated clinical performance studies that establish "ground truth" or use test/training sets in the context of an algorithm. The "proof" for this device is its demonstrated substantial equivalence to a predicate device through engineering specifications and compliance with recognized safety and performance standards.

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