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The sensiLight Mini is an over the counter device intended for the removal of unwanted hair. The sensiLight Mini is also intended for permanent reduction in hair regrowth, defined as a long-term, stable reduction in the number of hairs re-growing when measured at 6, 9, and 12 months after the completion of treatment regimen.

The sensiLight Mini device is a pulsed light hair removal device. Light-based hair removal is based on the theory of selective photothermolysis in which optical energy is used to disable hair growth. The sensiLight Mini device is composed of a hand held applicator and an external power supply. The spot size (treatment area) in the sensiLight Mini device is 3 cm2.

The device contains a lamp, a skin proximity sensor and a skin pigmentation sensor to detect appropriate skin tones. If the sensiLight Mini is not properly applied (in full contact with the skin) or user skin tone is too dark/tanned, the sensiLight Mini will not trigger a pulse.

This document is a 510(k) premarket notification for the sensiLight Mini, which is a pulsed light hair removal device. The device described in this submission (K161089) is stated to be identical to a previously cleared device (K140527). Therefore, no new performance data, non-clinical data, or clinical data are reported in this specific submission. The substantiation relies on the data from the predicate devices.

However, based on the information provided, we can infer some details about the clinical performance for the original device that K161089 references (K140527), particularly regarding its "permanent reduction in hair regrowth" claim.

Here's an attempt to answer your questions based on the available information and reasonable inferences:

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

Since this submission explicitly states "No new clinical performance data is reported in this submission" and "The sensiLight Mini device is identical in all details to the sensiLight Mini device cleared under K140527," the acceptance criteria and performance would be associated with the original K140527 submission. The document defines "permanent reduction in hair regrowth" as:

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 K161089 document as it refers back to a previous submission (K140527). To obtain these details, one would need to consult the K140527 submission.

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 provided in the K161089 document. Clinical studies for hair removal devices often involve trained technicians or physicians for evaluation, but specifics are missing here.

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

This information is not provided in the K161089 document.

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

Hair removal devices like the sensiLight Mini are typically consumer-use devices and do not involve "readers" in the context of diagnostic imaging. Therefore, an MRMC study and AI assistance as described are not applicable to this type of device.

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

This device is a physical light-based hair removal device for consumer use, not an algorithm. Therefore, "standalone (algorithm only)" performance is not applicable. The device's performance is inherently tied to human use and application according to instructions.

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

For hair removal studies of this nature, the "ground truth" for efficacy (hair reduction) is typically established by outcomes data in the form of objective measurements of hair count or hair density in treated vs. untreated areas, often performed by trained personnel or experts following a standardized protocol. This would be observed at specified time points (6, 9, and 12 months as indicated by the definition).

8. The sample size for the training set

The device described is a physical medical device, not an AI/ML algorithm that requires a "training set" in the conventional sense of machine learning. Therefore, a "training set" as it applies to AI/ML is not applicable. Its development would involve engineering, safety, and clinical testing, not algorithmic training.

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

As explained in point 8, the concept of a "training set" with ground truth establishment for an AI/ML algorithm is not applicable to this device.

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The sensiLight Mini is an over the counter devices intended for the removal of unwanted hair. The sensiLight Mini is also intended for permanent reduction in hair regrowth, defined as a long-term, stable reduction in the number of hairs re-growing when measured at 6, 9, and 12 months after the completion of treatment regimen.

The sensiLight Mini device is pulsed light hair removal device. Light-based hair removal is based on the theory of selective photothermolysis in which optical energy is used to disable hair growth. The sensiLight Mini device is composed of a hand held applicator and an external power supply. The spot size (treatment area) in the sensiLight Mini device is 3 cm2. The device contains a lamp, a skin proximity sensor and a skin pigmentation sensor to detect appropriate skin tones. If the sensiLight Mini is not properly applied (in full contact with the skin) or user skin tone is too dark/tanned, the sensiLight Mini will not trigger a pulse.

The provided document describes the sensiLight Mini, a pulsed light hair removal device. However, it does not include specific acceptance criteria or a study that directly proves the device meets those criteria in the typical sense of a clinical trial with predefined endpoints and statistical analysis.

Instead, the document focuses on demonstrating substantial equivalence to predicate devices through various tests and comparisons of technological characteristics. The "Performance Data" section primarily lists compliance with recognized standards and non-clinical testing.

Here's an attempt to extract and synthesize the requested information based on the provided text, acknowledging the limitations:

1. Table of Acceptance Criteria and Reported Device Performance

As explicit acceptance criteria with numerical targets are not stated, I will infer them from the safety and usability tests described and the claim of "substantial equivalence."

Regarding the studies: The document describes one non-clinical "Usability Study" as part of the performance data. It does not describe a clinical study in the traditional sense to prove efficiency for hair removal, but rather relies on substantial equivalence to previously cleared predicate devices for those claims.

2. Sample Size and Data Provenance (for the described Usability Study)

• Sample size: 20 potential device end-users were enrolled in the Usability Study. (3 participants were excluded during self-selection screening).
• Data provenance: Not explicitly stated, but it was a simulated home-use environment, indicating a prospective study specifically for this device. The country of origin for the data is not mentioned, but given the company's location (Israel), it's plausible the study was conducted there.

3. Number of Experts and Qualifications (for the Usability Study)

• No experts were used to establish ground truth for the usability study. The study objective was to test the device's self-selection and usability by potential end-users (lay users), not experts.

4. Adjudication Method (for the Usability Study)

• There was no mention of an adjudication method. The study involved participants completing tasks, and "All of the 20 enrolled subjects (100%) completed all tasks successfully." This suggests direct observation and binary outcome (task completed/not completed) rather than a need for expert adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, an MRMC comparative effectiveness study was not done. The device is a user-operated hair removal device, not an imaging or diagnostic AI tool that would typically involve multiple human readers interpreting cases.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance)

• The document implies standalone performance of the device's safety mechanisms (skin proximity and pigmentation sensors) as they automatically prevent pulsation if conditions are not met. However, the overall device function requires human operation. The document doesn't detail an "algorithm-only" performance in the context of an AI device, as this is a physical light-based device. If we interpret "standalone" as the device operating as intended without human intervention in its safety features, then yes, those features function standalone.

7. Type of Ground Truth Used (for the Usability Study)

• For the Usability Study, the "ground truth" was the successful completion of predetermined tasks by the lay users. It was based on direct observation of user interaction with the device and instructions, not expert consensus, pathology, or outcomes data in a medical sense.

8. Sample Size for the Training Set

• The document does not describe the development of an AI algorithm with a training set for this device in the common sense. It's a light-based physical device with embedded software and sensors. Therefore, there's no "training set" sample size to report for an AI model.

9. How the Ground Truth for the Training Set Was Established

• As there's no mention of a traditional AI training set, this question is not applicable to the information provided. The "ground truth" for the device's safety features (e.g., what constitutes appropriate skin tone or proper contact) would have been established during engineering design and testing, likely through physical measurements and pre-defined parameters rather than a data-driven training set for an AI model.

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