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JMOON NouvelleSkin facial toning device is intended for facial stimulation and indicated for overthe-counter cosmetic use.
JMOON NouvelleSkin facial toning device is an over-the-counter device that emits energy in the red and near-infrared spectrum, designed for the treatment of facial wrinkles.

Jmoon NouvelleSkin Facial Toning device is an at-home handled device that provides an advanced, easy-to-use and efficient solution for better facial wellness. This device includes microcurrent, red and infra-red light LED, which allow you to customize your treatments according to your face skin. The device provides indicator lights and beeps feedback to guide the user during the treatment cycle.
The microcurrent therapy will deliver 3 different low-level electrical microcurrent impulses on face within a few minutes to complete facial stimulation. The red light and infra-red therapy is a treatment that uses red light to reportedly improve your skin's appearance, like reducing wrinkles.

The provided text describes a 510(k) premarket notification for the JMOON NouvelleSkin Facial Toning Device. The submission aims to demonstrate substantial equivalence to predicate devices, rather than proving performance against specific acceptance criteria through a clinical study with real-world data and expert adjudication. Therefore, much of the requested information (like sample size for test set, data provenance, number of experts, adjudication method, MRMC study, standalone performance, and ground truth for training set) is not applicable or not present in the provided document.

However, the document does contain "acceptance criteria" in the form of conformance to various safety and performance standards for non-clinical testing.

Here's the breakdown of the information based on the provided text:

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

The acceptance criteria for this device are its conformance to recognized safety and performance standards. The reported performance is that the device "Conforms" to these standards.

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

The document explicitly states: "No clinical test data was used to support the decision of substantial equivalence."
The testing performed was non-clinical (lab bench testing). Therefore, there is no "sample size for the test set" in the context of human subjects or clinical data, nor is there data provenance in terms of country of origin or retrospective/prospective studies.

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. As no clinical test data was used, no experts were needed to establish ground truth for a test set based on human subjects. The acceptance criteria were based on adherence to international technical standards, which are established by expert consensus in regulatory and technical bodies.

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

Not applicable. No clinical test set with human subject data requiring adjudication was used.

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. No MRMC study was conducted, as the device is not an AI-assisted diagnostic or interpretive tool, and no clinical studies were performed.

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

This concept is not directly applicable to this device, which is a physical facial toning device. However, the non-clinical testing evaluates the device's standalone physical, electrical, and biocompatibility performance against specified standards. The results indicate that the device "Conforms" to these standards in a standalone capacity.

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

The "ground truth" for the non-clinical testing is compliance with the specified international standards (e.g., IEC 60601-1, ISO 10993 series). These standards represent an expert consensus on safety and performance requirements for medical devices.

8. The sample size for the training set

Not applicable. The device is not based on a machine learning algorithm; therefore, there is no "training set" in the conventional sense of AI/ML development.

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

Not applicable. There is no training set for this device.

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Looper (Model: ZX-579S) is a hand-held device for over-the counter aesthetic purposes. The Photon mode red light is indicated for the use in treating wrinkles on the blue light is indicated for the treatment of the mild to moderate inflammatory acne.

Looper (Model: ZX-579S) is a hand-held device for over-the-counter aesthetic purposes, it's a multifunctional comprehensive beauty instrument. It's combined with the two kinds of operation functions: SONIC and PHOTON mode.

Sonic mode: The Sonic waves can provide vibrating to facial skin. The device provides 5 vibration intensities for users to choose from.

Photon mode: The red light is intended for use in treating wrinkles on the face, and the blue light is intended for the treatment of the mild to moderate inflammatory acne. It emits energy in the red spectrum for the treatment of facial wrinkles. The therapeutic probe is designed for direct contact with the face for treatment.

The Looper device consists of an applicator and charging base. An applicator is a hand-held unit used for treatment. The treatment surface is located at the applicator tip and comes in direct contact with the skin. The charging base charging for the applicator. And there is no external data connection capability (e.g., through USB, Ethernet, WiFi, Bluetooth, etc.) for the device.

There are two keys: the PHOTON key and the SONIC key. The PHOTON is to select red light and the Sonic is to select the SONIC mode and adjust the sonic. Both keys can turn on/off the device.

The provided text describes the 510(k) summary for the "Looper (Model: ZX-579S)" device, which is an over-the-counter powered light-based device for aesthetic purposes, specifically for treating wrinkles and mild to moderate inflammatory acne.

Based on the document, this is a substantially equivalent (SE) determination based on comparison to predicate devices and adherence to relevant safety and performance standards. It is not a study that proves the device meets specific performance acceptance criteria based on clinical outcomes or AI model performance. The document emphasizes compliance with safety and performance standards (like IEC 60601 series) rather than clinical efficacy studies with predefined acceptance criteria. Therefore, most of the requested information regarding AI model performance, clinical study design, and ground truth establishment is not available in this document.

This device is a physical light-based therapy device, not an AI/ML-based diagnostic or assistive device. Thus, questions related to AI model performance, test sets, training sets, expert adjudication, MRMC studies, and AI-specific ground truth are not applicable to this 510(k) submission.

Here's a breakdown of what is provided and why other requested information is absent:

Device Performance and Acceptance Criteria (as per this document)

The "acceptance criteria" in this context are primarily related to safety, electrical performance, electromagnetic compatibility (EMC), usability, and biocompatibility, as outlined by the referenced IEC and ISO standards. The "study that proves the device meets the acceptance criteria" refers to lab bench testing against these engineering and safety standards, rather than clinical trials demonstrating efficacy with specific performance metrics.

Table of Acceptance Criteria and Reported Device Performance:

Reasons for Missing Information & Inapplicability:

This document is a 510(k) Pre-market Notification for a physical medical device (light therapy), not an Artificial Intelligence/Machine Learning (AI/ML) enabled device. Therefore, the following requested information is not applicable or not provided in this type of submission:

2. Sample sized used for the test set and the data provenance: Not an AI/ML device. "Test set" refers to engineering verification and validation against standards, not a clinical data set for AI model evaluation. The data provenance is "lab bench testing."
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not an AI/ML device. Ground truth for an AI/ML algorithm (e.g., image annotation) is not relevant here. Ground truth here means compliance with established engineering and medical device safety standards.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable for a physical device's engineering and safety verification.
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 an AI/ML device. MRMC studies are for evaluating performance of AI-assisted diagnostic tools.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not an AI/ML device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Ground truth for this device revolves around objective measurements of light output, electrical safety, ergonomic performance, and adherence to established biocompatibility and usability standards. It's not about clinical outcomes or diagnostic accuracy in the way an AI model would be.
8. The sample size for the training set: Not an AI/ML device.
9. How the ground truth for the training set was established: Not an AI/ML device.

In summary, the 510(k) for the Looper device demonstrates substantial equivalence to predicate devices and compliance with relevant safety and performance standards for light-based medical devices through lab bench testing. It does not involve AI/ML components or associated clinical validation studies that would require the specific data points requested in your prompt.

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