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The Intense Pulsed Light (IPL) System is an over-the-counter device intended for the removal of unwanted body hair.

Intense Pulsed Light (IPL) System is a small over-the-counter device for the permanent reduction of hair growth based on 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 Intense Pulsed Light (IPL) System includes main unit, an adaptor and goggles. The device is only powered by the external power adapter and its IPL emission activation is by a switch or auto light emission. Based on the ice-cool technology, the hair removal device with ice-cold compress functions.

I apologize, but the provided text does not contain information about the acceptance criteria or a study that proves the device meets those criteria in the context of typical AI algorithm performance evaluation.

The document discusses the substantial equivalence of an "Intense Pulsed Light (IPL) System" (models T013C, T015C, T015K) to a predicate device (model T012C) for the purpose of removing unwanted body hair.

Here's a breakdown of why it doesn't fit the request for AI algorithm performance:

• Device Type: This is a physical medical device (an IPL system), not an AI algorithm.
• "Acceptance Criteria" in this context: The "acceptance criteria" here refer to meeting regulatory standards for medical device safety and effectiveness to demonstrate substantial equivalence to a predicate device, rather than performance metrics for an AI algorithm (e.g., sensitivity, specificity, AUC).
• "Study that proves the device meets the acceptance criteria": The document refers to non-clinical testing (electrical safety, EMC, photobiological safety, biocompatibility, software life cycle processes) to demonstrate substantial equivalence and mitigate risks associated with minor design changes. There is explicitly no clinical testing performed (as stated in Section 8).

Therefore, I cannot populate the table or answer the specific questions about sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, or ground truth types, as these are related to AI algorithm validation, which is not what this document describes.

The document focuses on non-clinical testing and regulatory compliance for a hardware device.

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