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The Venus Versa System is a multi-application device intended to be used in aesthetic procedures.

The SR515 and SR580 IPL applicators are indicated for the following:

· Treatment of benign pigmented epidermal and cutaneous lesions including: hyperpigmentation, melasma, ephelides (freckles), lentigines, nevi, and cafe-au-lait macules.

· Treatment of benign cutaneous vascular lesions including: port wine stains, hemangiomas, facial, truncal and leg telangiectasias, rosacea, angiomas, poikiloderma of civatte, leg veins and venous malformations. The HR650 and HR690 IPL applicators are indicated for the removal of unwanted hair and to effect stable long-term or permanent hair reduction for skin types I-IV. Permanent hair reduction is the long-term stable reduction in the number of hairs regrowing when measured at 6, 9, and 12 months after the completion of a treatment regimen.

The Venus Versa System is comprised of a console (controller/CPU) and applicators that deliver Intense Pulsed Light (IPL) optical energy to the treatment area. Four IPL handpieces (SR515, SR580, HR650, HR690) are included with the device system. The Versa device provides individual adjustment of light fluency and pulse duration, and has integrated skin cooling features. Using the IPL applicators, the light energy delivered to the patient's skin is used to treat various conditions via the mechanism of selective photothermolysis.

The provided text is related to a 510(k) premarket notification for the "Venus Versa System", an Intense Pulsed Light (IPL) device used in aesthetic procedures. This document primarily focuses on demonstrating substantial equivalence to predicate devices, rather than providing a detailed clinical study report with acceptance criteria and performance metrics for a specific AI/ML medical device.

Therefore, I cannot fully address your request to "Describe the acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of an AI/ML device, as this document does not contain that type of information. Specifically, it does not include:

• A table of acceptance criteria and reported device performance for an AI/ML model.
• Sample sizes for test sets used for AI/ML model validation.
• Information about data provenance (e.g., country of origin, retrospective/prospective) for AI/ML data.
• Number of experts and their qualifications for establishing ground truth for AI/ML test sets.
• Adjudication methods for AI/ML test sets.
• Multi-Reader Multi-Case (MRMC) comparative effectiveness studies for AI+human vs. human-only.
• Standalone (algorithm only) performance metrics for AI/ML.
• Type of ground truth used for AI/ML (e.g., pathology, outcomes data).
• Sample size for training sets for an AI/ML model.
• How ground truth for a training set was established for an AI/ML model.

However, based on the provided text, I can infer some aspects related to general device performance and regulatory acceptance for a non-AI/ML device.

The document describes the regulatory review for the Venus Versa System, focusing on its substantial equivalence to predicate IPL devices. The "performance data" mentioned is primarily related to engineering and safety standards, rather than clinical efficacy studies with specific acceptance criteria for diagnostic or therapeutic accuracy in the way an AI/ML device would be evaluated.

Here's what can be extracted from the document regarding performance and "acceptance":

General Device Performance and Acceptance (as presented in this document for a non-AI/ML device):

The acceptance criteria are implicitly defined by compliance with recognized standards and the demonstration that the device's technological characteristics and performance are "substantially equivalent" to legally marketed predicate devices, and that it "performs as intended for the indicated uses."

1. Table of Acceptance Criteria and Reported Device Performance (as inferred from the document for this non-AI/ML device):

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

• Not Applicable / Not provided for clinical performance: This document does not describe a clinical study with a "test set" in the context of an AI/ML model for accuracy evaluation. The "testing" mentioned refers to engineering and safety compliance tests, and the "sample size" for such tests is typically governed by engineering standards (e.g., number of units tested for electrical safety or energy output consistency).
• Data Provenance: Not applicable.

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

• Not Applicable: This document does not describe the establishment of ground truth for a clinical test set as would be done for an AI/ML device.

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

• Not Applicable: This document does not describe this type of clinical adjudication process.

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 an IPL system, not an AI-powered diagnostic or assistive tool.

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

• Not Applicable: There is no AI algorithm being evaluated for standalone performance.

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

• Not Applicable: As there is no AI/ML model here, there is no need for this type of clinical "ground truth." The "ground truth" for the engineering tests would be the established specifications and regulatory standards.

8. The sample size for the training set:

• Not Applicable: This is not an AI/ML device, so there is no training set mentioned.

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

• Not Applicable: This is not an AI/ML device, so there is no training set ground truth to establish.

In summary, the provided document is a 510(k) clearance letter and summary for a non-AI/ML medical device (IPL system). It focuses on demonstrating substantial equivalence through technological comparison and compliance with engineering and safety standards, rather than clinical performance based on AI model evaluation and ground truth establishment.

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