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Hair Growth Device is indicated to promote hair growth in females with androgenetic alopecia who have Ludwig-Savin Classifications of I-II, in males with androgenetic alopecia who have Norwood-Hamilton Classifications of IIa-V and for both, Fitzpatrick Classification of Skin Phototypes I to IV.

The product is composed of main unit, USB cable, and silicone pad. The main unit consists of laser diodes that are spread throughout the helmet. Low-energy laser therapy (LLLT) is a safe and effective treatment for androgenic alopecia (AGA). The Hair Growth Device employs intelligent LLLT technology, utilizing a 650 nm, 5-milliwatt low-energy laser that penetrates the scalp surface to a depth of 3-5 mm, reaching the hair follicles. Hair Growth Device is a small over-the-counter, portable, noninvasive, low-level laser device intended to treat Androgenetic Alopecia (Hair Loss) and to promote hair growth in Males and Females. The product uses diode lasers to cover the entire area of the head that is normally covered with hair, and this unique design allows the treatment of the entire scalp without manual movement. Hair Growth Device consists of main unit, USB cable and silicone strap and no communication function. The device will then turn on and you'll hear an audible beep.

This FDA 510(k) clearance letter is for a medical device (Hair Growth Device) rather than a software algorithm used for interpretation/diagnosis. As such, the information typically requested for AI-powered diagnostic devices (e.g., test set sample size, expert qualifications, adjudication methods, MRMC studies, ground truth types for training/test sets, etc.) is not applicable or present in this document.

The document primarily focuses on demonstrating substantial equivalence to predicate devices through technical specifications and non-clinical testing. It explicitly states, "No clinical test data was used to support the decision of substantial equivalence."

Therefore, I cannot provide the requested information about acceptance criteria for an AI algorithm's performance or details about a study proving such performance, as this document does not pertain to such a device or study.

However, I can extract the general acceptance criteria and the type of study performed to prove substantial equivalence based on the provided text, recognizing these are not related to AI performance.

Based on the provided FDA 510(k) clearance letter for the Hair Growth Device:

1. Table of Acceptance Criteria and Reported Device Performance:

• Product service life
• Software validation
• Biocompatibility evaluation
• Electromagnetic compatibility and electrical safety
• Safety of laser products
• Performance test |

Regarding the study that proves the device meets the acceptance criteria (note: this is a substantial equivalence study, not an AI performance study):

2. Sample size used for the test set and the data provenance: Not applicable. No clinical test set or patient data was used. The study primarily relies on comparison to predicate devices and non-clinical engineering tests.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No clinical test set requiring expert ground truth was used for this substantial equivalence determination.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. No clinical test set 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. This device is not an AI-powered diagnostic tool, and no MRMC study was conducted.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an AI algorithm. The device operates independently (Over-The-Counter).

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable to an AI context. For substantial equivalence, the "ground truth" is established by compliance with recognized standards (e.g., IEC, ISO) and comparison to legally marketed predicate devices that have established safety and effectiveness.

8. The sample size for the training set: Not applicable. This is not an AI algorithm, so there is no training set in the machine learning sense.

9. How the ground truth for the training set was established: Not applicable. No AI training set.

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The Hair Growth Device is indicated to promote hair growth in males with androgenetic alopecia who have Hamilton-Norwood Classifications of IIa-V and females with androgenetic alopecia who have Ludwig-Savin Classifications of I-II and Fitzpatrick Classification of Skin Phototypes I to IV. All users should also have Fitzpatrick Skin Types I to IV.

The Hair Growth Device LS-D620 is composed of 80 laser diodes (wavelength: 650-660nm, power

It appears the provided document is a 510(k) premarket notification for a medical device called "Hair Growth Device" (Models LS-D620 and LS-D630). This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than undergoing a de novo clinical trial with explicit performance acceptance criteria and a detailed study report.

Therefore, the document does not contain the information requested regarding acceptance criteria and a study proving the device meets those criteria in the typical sense of a clinical performance study with predefined endpoints and statistical analysis.

Instead, the document focuses on:

• Substantial Equivalence: Comparing the proposed device's technical characteristics and intended use to predicate devices to argue that it raises no new questions of safety or effectiveness.
• Non-Clinical Testing: Demonstrating compliance with various safety standards (biocompatibility, electrical safety, EMC, laser safety, software validation).
• Usability Study: To show that users can safely and effectively use the device according to the manual.

Here's a breakdown of why the requested information isn't present in this document:

1. A table of acceptance criteria and the reported device performance: This is not provided. The 510(k) process doesn't typically require a clinical performance study with specific acceptance criteria in the same way a PMA or De Novo submission would. The "performance" demonstrated here is primarily related to safety, electrical output consistency, and user understanding, not a specific clinical efficacy metric like "percentage of hair growth subjects achieving X density increase."

2. Sample size used for the test set and the data provenance: Not applicable for a clinical performance test set. The usability study involved 15 participants for each model. Data provenance is not specified beyond "testing of 15 participants was conducted."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable as there's no clinical "test set" requiring ground truth established by experts for a performance metric.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.

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. This is a device for hair growth, not an AI-assisted diagnostic tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable for device performance in the clinical efficacy sense. For non-clinical tests like biocompatibility, the "ground truth" is adherence to ISO standards and passing specific assays. For the usability study, the "ground truth" is whether participants could safely and effectively use the device based on observation and feedback.

8. The sample size for the training set: Not applicable. This device is not an AI/ML algorithm that requires a training set.

9. How the ground truth for the training set was established: Not applicable.

Summary of what is available from the document:

• Non-Clinical Tests Performed:
  • Biocompatibility Testing: Per ISO 10993-1:2018 (including in vitro cytotoxicity, skin sensitization, and skin irritation). All tests passed.
  • Electrical Safety and Electromagnetic Compatibility Testing: Complied with ANSI AAMI ES60601-1:2005/(R)2012 and A1:2012, C1:2009/(R)2012 and A2:2010/(R)2012, IEC 60601-1-2:2014, IEC 60601-1-11:2015.
  • Laser Safety: Complied with IEC 60825-1:2007.
  • Software Verification and Validation: Documentation consistent with moderate level of concern, system validation testing demonstrated all software requirement specifications are met and hazards mitigated.
• Usability Study:
  • Sample Size: 15 participants for LS-D620, 15 participants for LS-D630.
  • Findings: Participants were able to understand the user manual and box labeling and were able to safely and effectively use the device.

The document concludes that based on the non-clinical tests and usability study, and the substantial equivalence to predicate devices, the Hair Growth Device LS-D620, LS-D630 does not raise new types of questions regarding its safety and efficacy.

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The Hair Growth Device is indicated to promote hair growth in males with androgenetic alopecia who have Hamilton-Norwood Classifications of Ila-V and females with androgenetic alopecia who have Ludwig-Savin Classifications of I-II and Fitzpatrick Classification of Skin Phototypes I to IV.

The Hair Growth Device LS-D601 is composed of 26 3R class laser diodes (wavelength: 650-660nm, power

The provided text does not describe a study that proves the device meets specific acceptance criteria related to its performance in promoting hair growth.

The document is a 510(k) premarket notification for a "Hair Growth Device" (Model LS-D601) and focuses on establishing substantial equivalence to existing predicate devices. It primarily details non-clinical testing for safety and electrical characteristics, along with a usability study, to demonstrate that the device is as safe and effective as previously cleared devices.

Therefore, I cannot provide a table of acceptance criteria and reported device performance for hair growth efficacy, nor can I answer questions about sample size for a test set, expert involvement in ground truth establishment, MRMC studies, or standalone algorithm performance, as these relate to a clinical performance study that is not detailed in this document.

Here's what can be extracted from the document regarding the information requested:

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

This document does not provide acceptance criteria or performance data related to the efficacy of hair growth. The "performance" mentioned refers to non-clinical tests demonstrating safety and electrical conformity.

Questions that cannot be answered from the provided text as they pertain to clinical efficacy studies:

• 2. Sample size used for the test set and the data provenance: Not applicable, no clinical efficacy test set described.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, no clinical efficacy test set described.
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable, no clinical efficacy test set described.
• 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. This document is for a physical device, not an AI model, and no MRMC study for efficacy is mentioned.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This document is for a physical device, not an algorithm.
• 7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): Not applicable, no clinical efficacy test set described.
• 8. The sample size for the training set: Not applicable, no training set for a machine learning model is described for efficacy.
• 9. How the ground truth for the training set was established: Not applicable, no training set for a machine learning model is described for efficacy.

Summary of what the document does convey about "proving the device meets acceptance criteria":

The document demonstrates that the "Hair Growth Device" meets safety and electrical performance standards, as well as usability requirements, through non-clinical testing. This supports its substantial equivalence to predicate devices, allowing it to be legally marketed. However, it does not provide evidence of new clinical studies to prove its efficacy in promoting hair growth, but rather relies on the established efficacy of the predicate devices based on similar technology and intended use.

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