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IPL Hair Removal is an over-the-counter device intended for removal of unwanted body and/or facial hair.

The IPL Hair Removal is a personal, light-based, hair reduction device intended to be sold over-the-counter directly to the end user. The device provides hair reduction using Intense Pulsed Light (IPL) technology. 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. The device contains Xenon Lamp to emit light and skin sensor to detect appropriate skin contact. If the device is not properly applied to the treatment area (in full contact with the skin), the device cannot trigger a pulse. The IPL Hair Removal includes two models, W-1095 and W-1098. The two models are the same in intended use, working principle, the main differences are appearance and output parameters.

The provided text is a 510(k) Summary for an IPL Hair Removal device. It does not describe an AI/ML powered device, but rather a physical medical device. Therefore, much of the requested information regarding acceptance criteria and study data for an AI/ML model (e.g., sample size for test set, ground truth experts, MRMC study, training set details) is not applicable or present in this document.

However, I can extract the relevant information regarding the device's "performance" and the non-clinical tests performed to demonstrate safety and effectiveness, which serve as the acceptance criteria from a regulatory perspective for this type of device.

Here's a breakdown of the available information based on your request, with an emphasis on what is present and what is not:

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

The document does not present "acceptance criteria" in the typical sense of a target metric for an AI model (e.g., AUC > X, sensitivity > Y). Instead, the "acceptance criteria" are implied by the compliance with recognized standards for medical devices and the demonstrated substantial equivalence to predicate devices. The "reported device performance" is primarily in terms of meeting these technical and safety standards.

Similar but different values: Energy density (W-1095: 1.674.46 J/cm², W-1098: 1.162.79 J/cm² vs. Predicates: 1.3-4.5 J/cm²), Spot size (W-1095: 2.69cm², W-1098: 4.3cm² vs. Predicates: 2.0-4.5cm²), Pulse duration (4-13ms vs. Predicates: 3-15ms), Output intensity levels (W-1095: 5 levels, W-1098: 3 levels vs. Predicates: 5 levels).

Different: Dimensions, Weight, Materials of skin-contacting components (ABS, PS vs Plastic, metal, ABS, PC, Aluminium alloy). |
| Electrical Safety | Compliance with IEC 60601-1, IEC 60601-1-2, IEC 60601-1-11, IEC 60601-2-83. | The device complies with IEC 60601-1, IEC 60601-1-2, IEC 60601-1-11, IEC 60601-2-83. |
| Photobiological Safety | Compliance with IEC 62471. | The device complies with IEC 62471. |
| Biocompatibility | Compliance with ISO 10993-5 (Cytotoxicity) and ISO 10993-10 (Irritation and Skin Sensitization). | All user directly contacting materials are compliance with ISO10993-5 and ISO10993-10 requirements. |
| Software Verification & Validation | Compliance with FDA guidance "Guidance for Pre Market Submissions and for Software Contained in Medical Devices." | Software verification and validation test performed according to FDA guidance. |
| Usability Evaluation | Compliance with FDA guidance "Applying Human Factors and Usability Engineering to Medical Devices." | Usability evaluation performed according to FDA guidance. |

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the document. For a physical device like this, "test set" and "data provenance" as described for an AI/ML model are not typically relevant. The testing is based on engineering validation and adherence to safety standards rather than a clinical dataset for algorithmic performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not applicable/provided. There is no "ground truth" in the diagnostic sense for this type of device, as it's not performing a diagnostic task where expert interpretation is needed to label data.

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

This information is not applicable/provided. Adjudication methods are relevant for resolving discrepancies in expert labeling of ground truth data, which is not the context here.

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

This information is not applicable/provided. An MRMC study is specific to evaluating the impact of AI on human reader performance in diagnostic tasks. This device is not an AI-assisted diagnostic tool.

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

This information is not applicable/provided. This is a physical device, not an algorithm.

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

This information is not applicable/provided. As explained, there is no "ground truth" of this nature for a hair removal device. The "ground truth" for regulatory clearance is that the device is safe and effective when used as intended, demonstrated by meeting engineering standards, biocompatibility, and substantial equivalence to legally marketed predicate devices.

8. The sample size for the training set

This information is not applicable/provided. There is no "training set" as understood in the context of machine learning.

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

This information is not applicable/provided. There is no "training set" or "ground truth" for it in this context.

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