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PicoLO laser system is intended for use in surgical and aesthetic applications in the medical specialties of dermatology and general and plastic surgery.

1064nm

The 1064nm wavelength of the PicoLO laser system is indicated for tattoo removal for all skin types (Fitzpatrick I-VI) to treat the following tattoo colors: black, brown, green, blue and purple.

532nm

The 532nm wavelength of the PicoLO laser system is indicated for tattoo removal for Fitzpatrick skin types I-III to treat the following tattoo colors: red, yellow and orange.

The PicoLO laser system is also indicated for benign pigmented lesions removal for Fitzpatrick Skin Types I-IV.

The 1064 handpiece (1064nm) is also indicated for the treatment of acne scars in Fitzpatrick Skin Types II-V.

The PicoLO laser system is also indicated for treatment of wrinkles in Fitzpatrick Skin Types I-IV.

The PicoLO laser system is a multi-wavelength, pulsed laser system designed for the treatment of benign pigmented lesions. A key feature of the device is its ability to produce multiple laser wavelengths (1064 nm and 532 nm). The PicoLO Nd: YAG Picosecond Laser System consists of a set of O-switched Nd:YAG lasers, controlled by an embedded processor, to be used in dermatology. The laser system uses focusing optics to deliver a pattern of thermal energy to the epidermis and dermis. This system consists of main body, color touch screen, articulated arm, hand piece and foot switch.

Based on the provided document, the device in question is a PicoLO Nd:YAG Picosecond Laser System, and the document is a 510(k) summary for its premarket notification (K203491). This type of FDA submission focuses on demonstrating "substantial equivalence" to a legally marketed predicate device, rather than proving safety and effectiveness through a full clinical trial with acceptance criteria for specific performance metrics like those for an AI/ML device.

Therefore, the document does not contain the acceptance criteria and study details typically associated with proving a device (especially an AI/ML device) meets specific performance metrics. Instead, it demonstrates performance by showing compliance with recognized standards and technical characteristics comparable to a predicate device.

Here's why the requested information cannot be fully provided from this document:

• This is not an AI/ML device: The PicoLO Nd:YAG Picosecond Laser System is a laser for surgical and aesthetic applications. It's a hardware device, not a software algorithm that performs diagnostic or prognostic tasks.
• 510(k) pathway: The 510(k) clearance pathway is about demonstrating substantial equivalence to a predicate device, not about establishing novel performance benchmarks. The performance data provided are primarily non-clinical (electrical safety, EMC, usability, risk management, biocompatibility, and laser safety standards), confirming the device's build quality and operation within established safety parameters, not its efficacy compared to a numerical performance target.
• No "acceptance criteria" in the AI/ML sense: The "acceptance criteria" for this device are its compliance with various consensus standards (e.g., AAMI/ANSI ES60601-1, IEC 60601-1-2, IEC 60825-1, ISO 14971, ISO 10993). There are no performance metrics like sensitivity, specificity, or AUC that would involve a test set, ground truth experts, or MRMC studies.

However, I can extract and present the
information that is available in the document, framed in the context of device safety and effectiveness as demonstrated for this type of medical device clearance:

Device Type: PicoLO Nd:YAG Picosecond Laser System (Hardware device, not AI/ML)
FDA Submission Type: 510(k) Premarket Notification (K203491)

Given that this is a 510(k) submission for a laser device (not an AI/ML product), the "acceptance criteria" and "study" are not presented in terms of AI/ML performance metrics (e.g., sensitivity, specificity, F1-score) or comparative effectiveness studies with human readers. Instead, the "acceptance criteria" are compliance with relevant safety and performance standards, and the "study" involves non-clinical testing to demonstrate this compliance and substantial equivalence to a predicate device.

Here's the relevant information from the document:

1. A table of acceptance criteria and the reported device performance

The "acceptance criteria" for a device cleared through the 510(k) pathway primarily relate to its compliance with recognized standards and its comparable performance to the predicate device. These are qualitative rather than quantitative performance metrics for a diagnostic algorithm.

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

• Test set sample size: Not applicable in the context of an AI/ML algorithm's performance study. The "testing" here refers to engineering and safety validation, not a clinical test set of patient data.
• Data provenance: Not applicable. The "data" are measurements from non-clinical laboratory tests and engineering evaluations of the device, not patient data.

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

• Not applicable. This is not a diagnostic device requiring expert interpretation for ground truth.

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

• Not applicable.

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. This device is a laser system, not an AI assistance tool for human interpretation.

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

• Not applicable. This is a hardware device, not a standalone algorithm.

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

• Not applicable. The "ground truth" equivalent for this device's clearance pertains to compliance with engineering standards and safety requirements.

8. The sample size for the training set

• Not applicable. This is not an AI/ML device that requires training data.

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

• Not applicable.

Summary regarding this specific document:

This document is a 510(k) summary for a laser medical device. It demonstrates safety and effectiveness through compliance with recognized consensus standards and by showing substantial equivalence to a previously cleared predicate device and a reference device for new indications. It does not provide information relevant to the types of studies and acceptance criteria typically associated with AI/ML-based medical devices or comparative effectiveness studies involving human readers and AI assistance.

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