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The Q Switched Nd: YAG Laser machine (QNHF-01) is indicated for use in tattoo removal, treatment of benign vascular lesions, treatment of benign pigmented lesions, incision, ablation, vaporization of soft tissue for general dermatology as follows: 1064nm: -Tattoo Removal Dark ink: blue and black. -Treatment of Benign Pigmented Lesions Nevus of ota. 532mm: -Tattoo Removal Light ink: red, Light ink: sky blue and green. - Treatment of Benign Vascular Lesions Port wine birthmarks; Telangiectasias; Spider angioma; Cherry angioma; Spider nevi. -Treatment of Benign Pigmented Lesions Cafe-au-lait birthmarks; Solar lentiginos; Senile lentiginos; Becker's nevi; Freckles; Nevus spilus.

Q Switched Nd: YAG Laser machine (QNHF-01) is laser system which delivers laser at a wavelength 1064nm or 532nm. It consists of control panel module, main control module, laser power module, temperature and humidity control module and laser module. The physician is able to select the desired wavelength and the related output energy via control panel.

The provided document is a 510(k) Premarket Notification from the FDA for a medical device: the Q Switched Nd:YAG Laser machine (QNHF-01). This type of document is a notification of intent to market and not a detailed study report providing acceptance criteria and performance data for an AI/ML-driven device, particularly not in the format requested.

Therefore, I cannot provide the requested information as the document does not contain details about:

• Acceptance criteria table and reported device performance for an AI/ML component.
• Sample sizes for test sets or their provenance.
• Number and qualifications of experts for ground truth establishment.
• Adjudication methods.
• Details of multi-reader multi-case (MRMC) comparative effectiveness studies.
• Standalone algorithm performance.
• Type of ground truth used.
• Sample size for training sets.
• How ground truth for training sets was established.

The document explicitly states: "No clinical study is included in this submission." This further confirms that it does not contain the kind of performance data usually required to demonstrate AI/ML device capabilities against pre-defined acceptance criteria.

The submission focuses on demonstrating substantial equivalence to a predicate device (K163123) based on technological similarities and non-clinical performance testing against recognized safety and performance standards (e.g., IEC 60601-1, ISO 10993 series for biocompatibility). This is typical for a traditional medical device, not an AI/ML device that requires complex evaluation of predictive accuracy or clinical utility improvements due to AI assistance.

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