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

The IPL Hair Removal Device is a light-based device and an over-the-counter device which is expected to be sold directly to end users. The device provides hair reduction using Intense Pulsed Light (IPL) technology. The purpose of the light is to heat the root where the hair grows. 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 has two working modes, an automatic mode and a manual mode. Each mode has 5 levels of output energy. The device contains a Xenon lamp that emits pulses of light (a total of 350000 pulses over the device's lifetime) and a skin sensor to detect appropriate skin contact. If the device is not properly applied to the treatment area (in full contact with the skin), it will cannot emit the treatment light pulses.

This appears to be a 510(k) summary for an IPL Hair Removal Device (Model: BZ-0721) submitted to the FDA. The document focuses on demonstrating substantial equivalence to a predicate device (IPL Home Use Hair Removal Device, Model: D-1128, K192432) rather than providing detailed acceptance criteria and a study to prove meeting those criteria in the manner one might find for a novel diagnostic or AI-driven device.

Based on the provided text, here's what can be extracted regarding acceptance criteria and studies:

Acceptance Criteria and Reported Device Performance

Since this is a substantial equivalence submission for an IPL hair removal device, the "acceptance criteria" are primarily related to safety standards and comparability to the predicate device. There aren't specific performance metrics like sensitivity or specificity associated with an AI diagnostic. Instead, the performance is demonstrated by meeting recognized safety and electrical standards and showing that key technical specifications are either the same or comparable, without raising new questions of safety or effectiveness.

Missing Information (Not applicable or not typically included in this type of submission for this device category):

For this type of device (IPL hair removal), the concepts of "test set," "ground truth," "experts for ground truth," "adjudication," "MRMC study," "standalone performance," and "training set" are generally not applicable in the context of the provided documentation. These are typically associated with diagnostic devices, often AI-driven, where there is a "diagnosis" or "detection" task.

Here's why:

• This is not an AI/diagnostic device. The device is an IPL hair removal device, demonstrating safety and performance primarily through engineering standards and comparison to a predicate, not through diagnostic accuracy metrics.
• No clinical performance data. Section 8 explicitly states, "No new clinical performance data is reported in this submission." This indicates that a study demonstrating effectiveness (e.g., hair reduction percentage) was likely not part of this submission for the purpose of demonstrating substantial equivalence to the predicate, as the predicate itself likely established that effectiveness. The focus here is on proving the new device is as safe and performs similarly enough to the already-cleared predicate.

Therefore, the following information cannot be extracted from the provided text:

2. Sample size used for the test set and the data provenance: Not applicable as it's not a diagnostic device with a test set.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
4. Adjudication method for the test set: Not applicable.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance: Not applicable, as this is not an AI-assisted diagnostic device.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable, as this is not an algorithm.
7. The type of ground truth used: Not applicable.
8. The sample size for the training set: Not applicable.
9. How the ground truth for the training set was established: Not applicable.

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The RED Light Device is an OTC device indicated to emit energy in the red and IR region of the spectrum for use in dermatology for the treatment of periorbital wrinkles.

The proposed device, RED Light Device is a battery operated device that uses low power light spectrum at red and infrared LED, at wavelength of 633 ±5nm, 830 ±5nm emitting optical power in a uniform distribution with no hot spots.

The device is composed of a handpiece for delivery of light energy, base unit for charging and storage when not in use, and A.C. charging adapter.

It is a hand held light emitting diode (LED) device for the treatment of periorbital wrinkles designed for home-use.

The proposed device is NOT for life-supporting or life-sustaining, not for implant. The proposed device is NOT provided sterile and is NOT a reprocessed single-use device.

The provided text is a 510(k) summary for the "RED Light Device," seeking FDA clearance based on substantial equivalence to a predicate device. It does not describe a clinical study designed to prove the device meets specific acceptance criteria in the way an AI/ML device would be evaluated. Instead, it focuses on non-clinical tests to demonstrate safety and performance equivalence to a previously cleared device.

Therefore, many of the requested categories for AI/ML device evaluation (like acceptance criteria for performance metrics, ground truth establishment for a test set, expert qualifications, adjudication methods, MRMC studies, or standalone performance for an algorithm) are not applicable to this submission, as it's for a light therapy device.

Here's a breakdown of the information that is available, reframed to address your request where possible, and clearly indicating what is not present in the document given its nature:

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

The document frames "acceptance criteria" and "performance" in terms of equivalence to the predicate device and compliance with established safety standards, rather than performance against a clinical efficacy target metric.

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

• Test Set Sample Size: For the Usability/Label Comprehension Study, the sample size was 35 participants.
• Data Provenance: The document does not specify the country of origin for the usability study participants. It's a prospective study for usability.
• For demonstrating technical compliance (e.g., electrical safety, EMC, biocompatibility): These tests are typically performed on samples of the device itself by accredited labs. The "sample size" here refers to the number of devices tested, which isn't specified but generally follows standard testing protocols for device certification.

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)

• Not applicable in the context of clinical efficacy experts determining ground truth for a diagnostic AI.
• For the Usability/Label Comprehension study, the "ground truth" was whether participants could correctly perform tasks and answer questions based on the instructions, which was assessed directly by the study design, not by external experts establishing a clinical ground truth.

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

• Not applicable as there is no clinical test set requiring adjudication for ground truth.
• For the usability study, the performance criteria (e.g., 100% correctly demonstrated setup) are objective and do not require expert adjudication in the manner of medical image interpretation.

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

• No, a MRMC comparative effectiveness study was not done. This device is a direct-to-consumer light therapy device, not an AI-powered diagnostic or assistive tool for human readers.

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

• Not applicable. The "RED Light Device" is a physical light therapy device, not an algorithm. Its performance is inherent in its physical output and user interaction, not in standalone algorithmic interpretation.

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

• Not applicable for clinical efficacy as no clinical study was conducted.
• For Usability: The "ground truth" was successful completion of prescribed tasks and correct answers to questionnaire items as defined by the study protocol related to understanding device operation and safety.

8. The sample size for the training set

• Not applicable. This device does not use machine learning, therefore there is no "training set."

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

• Not applicable. As there is no training set for a machine learning algorithm, ground truth establishment for a training set is irrelevant.

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