Search Results

755 nm:
The PicoSure Workstation is indicated for tattoo and benign pigmented lesions removal. The PicoSure workstation with the 2mm and 6mm hand pieces and the Focus Array are indicated for the treatment of acne scars and wrinkles in Skin Types I - IV.

532 nm:
The PicoSure 532 nm Laser Delivery System is indicated for tattoo removal in Skin Types I - III.

1064 nm:
The PicoSure 1064 nm Laser Delivery System is indicated for tattoo and benign pigmented lesions removal.

The PicoSure™ workstation is a high-powered, Alexandrite system that delivers laser energy in the 755-nm nominal wavelength. The system offers fast and efficient treatment through a variety of spot sizes, fluences and repetition rates. Laser activation is by footswitch. In addition to the 755 nm handpiece, optional 532 nm Laser Delivery System and/or 1064 nm Laser Delivery System can replace the 755 nm handpiece at the distal end of the articulated arm. These Delivery Systems convert the 755 nm laser energy into a 532 nm wavelength or a 1064 nm wavelength and are available in multiple spot sizes.

The provided text describes a 510(k) premarket notification for the PicoSure Workstation, specifically focusing on the addition of a 1064 nm Laser Delivery System. The information necessary to fully answer all aspects of your request is not entirely present in the provided document, but I will extract what is available.

Here's a breakdown of the acceptance criteria and study information:

1. Table of acceptance criteria and the reported device performance

The document does not explicitly state pre-defined "acceptance criteria" in terms of specific numerical thresholds for efficacy or safety. Instead, it concludes that the device is "as effective as the predicate device" and "safe and effective."

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

• Sample Size: 66 subjects with a total of 79 tattoos.
• Data Provenance: Not explicitly stated (e.g., country of origin). The study design indicates it was a clinical study, suggesting it was prospective.

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

• Number of Experts: Three blinded reviewers.
• Qualifications of Experts: Not specified beyond being "blinded reviewers." It does not state if they were dermatologists, radiologists, or had any specific years of experience.

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

• Adjudication Method: "Mean scores for all three evaluators were calculated per subject to determine overall mean scores." This implies an averaging or consensus approach, rather than a specific 2+1 or 3+1 adjudication where dissenters might be reviewed by a tie-breaker.

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

• MRMC Study: This was a clinical study comparing a new device (PicoSure) to a predicate device (RevLite) for tattoo removal, not an MRMC study assessing human reader performance with or without AI assistance. Therefore, this question is not applicable to the provided information.

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

• This device is a laser system, not an AI algorithm. Therefore, this question about "algorithm only" performance is not applicable. The study assessed the device's clinical performance in patients.

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

• The ground truth for efficacy was established by expert consensus (mean scores from three blinded reviewers) using a 6-point categorical efficacy scoring scale based on photographic evaluation.
• Safety ground truth was based on the reporting of adverse events (deaths, SAEs, UADEs).

8. The sample size for the training set

• This was a clinical study evaluating a medical device, not an AI algorithm that requires a training set. Therefore, this question is not applicable. The only "training" mentioned for the device itself would be internal engineering and development, not a data training set as understood in AI/ML contexts.

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

• As explained above, this was not an AI algorithm study requiring a training set. Therefore, this question is not applicable.

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