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The Palomar Q-YAGTM Nd:YAG laser system is indicated at the 1064 nm wavelength for skin resurfacing with or without adjuvant preparation.

The complete system consists of a power supply unit, a cooling unit, a foot switch, and the hand piece that connects the laser unit and cooling unit using an umbilical cord. In standard use, the hand piece is held against the treatment area and the light pulse is delivered when the foot switch and hand switch is depressed. Laser parameters and other system features are controlled from a display panel located on the front of the power supply unit.

This document is a 510(k) summary for the Palomar Q-YAG™ Nd:YAG Laser System. It focuses on establishing substantial equivalence to a predicate device rather than presenting a study demonstrating the device meets specific performance acceptance criteria for a diagnostic AI/ML device.

Therefore, the requested information regarding acceptance criteria, performance data, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment is not available in the provided text.

The document states:

• Intended Use: The Palomar Q-YAG™ is indicated for laser skin resurfacing with or without adjuvant preparation.
• Performance Data: "The differences in the specifications of the laser and the predicate device do not result in different performance or raise new questions of safety or efficacy." This indicates that direct performance data against specific acceptance criteria for a diagnostic task was not a requirement or was not included in this submission summary. The basis for clearance is substantial equivalence.
• Conclusion: "Based on the foregoing, the Palomar Q-YAGTM Nd:YAG Laser System is substantially equivalent to the legally-marketed claimed predicate device, i.e., the Thermolase SoftLight."

This type of submission focuses on demonstrating that the new device is as safe and effective as a legally marketed predicate device, especially by showing that differences in specifications do not lead to different performance or new safety/efficacy concerns. It does not typically involve detailed studies with acceptance criteria, ground truth, and statistical analyses of diagnostic performance as would be expected for an AI/ML-driven diagnostic device.

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