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UVB Phototherapy for psoriasis, vitiligo, atopic dermatitis, and leukoderma

The XTRAC XL Plus Excimer Laser Phototherapy Svstem is a complete self-contained compact UV laser light source, which utilizes a XeCl gas mixture to generate dose-selected and target specific ultraviolet light at wavelength of 308 nm. The laser system consists of a keypad and display, a fiberoptic delivery system, a handpiece and a foot-switch. The laser is enclosed in a protective interlocked housing.

This document is a 510(k) premarket notification for a device modification, not a submission for a new device requiring extensive clinical trials to establish efficacy against acceptance criteria. Therefore, the provided text does not contain information typically found in studies designed to prove a device meets specific performance acceptance criteria for a novel device.

The submission focuses on demonstrating substantial equivalence to a predicate device based on technological characteristics and conformance to established standards, rather than clinical performance metrics.

Here's a breakdown of the requested information based on the provided text, highlighting what is not applicable or not found in this specific type of 510(k):

1. Table of Acceptance Criteria and Reported Device Performance

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

• Not Applicable/Not Found: This submission is a modification to an already cleared device. Performance testing referenced is related to safety, electrical, and laser standards (EN 60601, 21 CFR 1040.10 & 1040.11, UL 2601) for the modified hardware, and not clinical efficacy testing with a "test set" of patient data. Therefore, there's no mention of a clinical test set, data provenance, or sample size for such a set.

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

• Not Applicable/Not Found: As there is no clinical test set for efficacy described, there's no mention of experts or their qualifications for establishing ground truth. The "ground truth" for this modification centers on regulatory compliance and substantial equivalence to an existing device.

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

• Not Applicable/Not Found: No clinical test set means no adjudication method described.

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

• Not Applicable/Not Found: This device is a laser phototherapy system and does not involve AI or human "readers" in the diagnostic sense. Therefore, an MRMC study is irrelevant to this submission.

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

• Not Applicable/Not Found: This is a hardware device (excimer laser system) for phototherapy, not an algorithm or AI system.

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

• For demonstrating substantial equivalence: The "ground truth" is that the predicate device (XTRAC Excimer Laser System, model AL7000) is already cleared and shown to be safe and effective for its intended use. The modified device's "ground truth" for clearance is its adherence to relevant safety and performance standards and its technological similarity to the predicate.
• For efficacy: The document refers to "Current published data supports 308 nm UVB to be effective of providing efficacy to the currently approved indications for use." This implies the "ground truth" for the treatment itself comes from existing medical literature and prior clearances of similar devices, rather than new primary data generated for this specific 510(k).

8. The sample size for the training set

• Not Applicable/Not Found: This 510(k) is for a hardware device modification, not a machine learning or AI algorithm. There is no concept of a "training set" in the context of this submission.

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

• Not Applicable/Not Found: As there is no training set, this question is not applicable.

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