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The Fotona F-22 Handpiece is intended for:
. In fractionated mode:
Dermatological procedures requiring resurfacing of soft tissue with fractionated handpiece;
: ` In non-fractionated mode:
General Surgery Indications: Surgical incision, vaporization and coagulation of soft tissue during any general surgery application where skin incision, tissue dissection, excision of lesions, complete or partial resection of internal organs, lesions, tissue ablation and vessel coagulation is necessary;

The Fotona FS-01 Handpiece is intended for:
Dermatological procedures requiring resurfacing of soft tissue with fractionated handpiece.

The F-22 Handpiece and FS-01 Handpiece each attach to the Dynamis Er: Y AG/Nd: YAG Laser Systems. The Fotona Dynamis Family is based on Er:YAG (2940 nm) and Nd:YAG (1064 nm) laser technology. The F-22 handpiece and FS-01 handpiece are designed to be used with the Er:YAG (2940 nm) laser wavelength only. The laser system console consists of a flashlamp pumped Er: YAG laser source, power supply, water colling unit, electronics, and a footswitch. Electrical power is supplied to the console by the facility's power source. A red diode aiming beam (650 nm) is combined with the therapeutic Er:YAG laser beam. The combined therapeutic and aiming beams are guided through an articulated arm to an optical manual or scanner hand piece (in the case of the Er:YAG laser). Fotona's power supply and electronics, integrated into the laser system, allows control of the laser energy and the laser pulse duration. The user activates laser emission by means of a footswitch.

This document describes the regulatory submission for the Fotona F-22 Laser Handpiece and Fotona FS-01 Laser Handpiece. It does not contain information about the acceptance criteria or a specific study proving the device meets those criteria in the typical sense of a clinical trial with predefined performance metrics.

Instead, the submission focuses on demonstrating substantial equivalence to existing, legally marketed predicate devices. This means that the new devices are considered safe and effective because they are similar in design, technological characteristics, and intended use to devices already cleared by the FDA.

Therefore, many of the requested categories of information cannot be directly extracted from the provided text in the traditional way an AI model's performance study might be described. However, I will address what information is available and highlight what is not.

Acceptance Criteria and Device Performance

There are no explicit "acceptance criteria" presented in the document in the format of performance thresholds (e.g., sensitivity, specificity, accuracy). Instead, the acceptance is based on demonstrating substantial equivalence to predicate devices, which implies that the new handpieces perform comparably to established devices already deemed safe and effective.

The "reported device performance" is primarily characterized by its technological specifications matching or being similar to the predicate devices, and a general statement about a clinical and histological study.

• Dermatological procedures requiring resurfacing of soft tissue with fractionated handpiece.
• (F-22 only, non-fractionated) General Surgery Indications: Surgical incision, vaporization and coagulation of soft tissue. |
  | Safety and Standards Compliance: Adherence to relevant IEC and CFR standards. | Fotona F-22 and FS-01 handpieces are designed, tested, and will be manufactured in accordance with standards including 21 CFR 1040.10, 1040.11, IEC 60601-1, IEC 60601-1-2, IEC 60601-1-4, IEC 60601-1-6, IEC 60601-2-22, IEC 60825-1, EN 62304, ISO 17664. |

Study Details (Based on the provided text)

1. Sample size used for the test set and the data provenance:

   • The document mentions a "clinical and histological study" but does not specify the sample size for human subjects or histology samples.
   • There is no information about data provenance (e.g., country of origin, retrospective/prospective).
   • A "test set" in the context of an AI model's performance is not directly applicable here. This is a medical device submission, not specifically an AI model submission. The "testing" refers to device validation and verification.

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

   • This information is not provided. The "ground truth" for a laser device would typically relate to the biological effect (e.g., tissue ablation, resurfacing), which is assessed by medical professionals. The document mentions a "clinical and histological study" to demonstrate benefits, implying expert assessment, but gives no specifics on the number or qualifications of clinicians/pathologists.

3. Adjudication method for the test set:

   • This information is not provided.

4. 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:

   • No MRMC comparative effectiveness study is mentioned. This is a laser handpiece, not an AI diagnostic or assistive tool, so such a study design is unlikely to be performed for this device type.

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

   • This question is not applicable. This is a physical laser handpiece, not a software algorithm.

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

   • The document states "A clinical and histological study... has been performed to demonstrate the benefits of dermatological procedures requiring resurfacing and ablation of soft tissue with the fractionated handpieces."
   • This suggests that histology (pathology) and clinical observation/outcomes were used to assess the "benefits."

7. The sample size for the training set:

   • This question is not applicable as this is a medical device, not an AI model requiring a "training set."

8. How the ground truth for the training set was established:

   • This question is not applicable as this is a medical device, not an AI model requiring a "training set."

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