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IPL Hair Removal Device is indicated for the removal of unwanted hair. The device is also indicated for the permanent reduction in hair regrowth, defined as 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 regime.

IPL Hair Removal Device (Model: UI04 SD, UI04 DG), is an over-the-counter, home-use and personal device for hair reduction by using Intense Pulsed Light (IPL). It works below the skin's surface and does not involve any cutting or pulling, reducing hair growth with minimal pain.

The device is only powered by the external power adapter and its IPL emission activation is by finger switch. This product adopts irreplaceable treatment window and the spot size is 3.3cm² that is suitable for multiple hair removal areas. It contains a skin sensor to detect appropriate skin contact, if the device is not in full contact with the skin, the device cannot emit the treatment light pulses. Besides, the device has the cooling function that will be activated throughout the whole hair removal process to provide users with a more comfortable experience.

IPL Hair Removal Device, model: UI04 SD, UI04 DG have the same indication for use, performance, structure design and operation, the only deference is their enclosure color (UI04 SD is Star White and UI04 DG is Dark Green).

This document is a 510(k) summary for an IPL Hair Removal Device (models UI04 SD, UI04 DG). It outlines the device's characteristics and compares it to predicate devices to demonstrate substantial equivalence.

Based on the provided text, the device in question is an IPL Hair Removal Device. The 510(k) submission primarily focuses on non-clinical performance data to support substantial equivalence, rather than a clinical study involving human readers or AI-assisted diagnostic performance. Therefore, many of the requested elements related to MRMC studies, ground truth establishment for AI, and expert adjudication are not applicable to this type of device and submission.

Here's an analysis of the acceptance criteria and performance as described in the document, focusing on the information that is present:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device's 510(k) clearance are primarily based on demonstrating substantial equivalence to existing legally marketed predicate devices through non-clinical performance testing related to safety and fundamental functionality. The document doesn't present specific quantitative performance metrics like sensitivity, specificity, or AUC, as would be typical for AI/diagnostic devices. Instead, the "performance" is demonstrated by meeting various safety and usability standards and having comparable technical characteristics to cleared predicates.

Study That Proves the Device Meets Acceptance Criteria:

The "study" in this context is a series of non-clinical tests and a detailed comparison to predicate devices, rather than a clinical trial or an AI performance study as might be expected for diagnostic algorithms.

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

   • Test Set (for non-clinical validation): The document does not specify "sample sizes" in the way one would for patient data (e.g., number of patients or images). Instead, testing was performed on physical units of the device and its components to verify their compliance with various electrical, safety, biocompatibility, and usability standards. The number of devices or components tested is not specified but is assumed to be sufficient for standard regulatory compliance testing.
   • Data Provenance: The testing appears to be conducted by the manufacturer or third-party labs on the device itself. The document implicitly suggests this is prospective testing done specifically for this submission. The country of origin of the data is not explicitly stated beyond the manufacturer being in Shenzhen, China.

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

   • Not Applicable. This submission is for an IPL hair removal device, not an AI diagnostic device requiring expert interpretation of medical images or patient outcomes for ground truth. Ground truth in this context means compliance with engineering and safety standards, verified by testing according to those standards.

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

   • Not Applicable. As there are no human readers interpreting data, an adjudication method is irrelevant. Test results against established standards (e.g., pass/fail for electrical safety tests) serve as the "ground truth."

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. An MRMC study was not conducted as this is not an AI-assisted diagnostic device.

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

   • No. This is not a standalone AI algorithm. The software mentioned is for controlling the device's functions, not for diagnostic or analytical purposes.

6. The type of ground truth used:

   • For Biocompatibility: Positive/negative controls in in vitro cytotoxicity and skin sensitization tests, and adherence to ISO standards.
   • For Electrical Safety/EMC/Eye Safety: Pass/fail criteria defined by the respective international standards (e.g., IEC 60601 series, IEC 62471).
   • For Software V&V: Fulfillment of software requirement specifications and mitigation of identified software hazards.
   • For Usability: Compliance with usability standards and FDA guidance on human factors, likely demonstrated through risk analysis and verification activities.
   • For Functional Equivalence: Comparison of technical specifications (wavelength, energy density, etc.) against those of legally marketed predicate devices.

7. The sample size for the training set:

   • Not Applicable. This device does not use an AI model trained on a data set in the typical sense (e.g., medical images for diagnosis). The term "training set" doesn't apply to the non-clinical performance and safety testing described.

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

   • Not Applicable. See point 7.

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