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The iHelmet Laser Comb is indicated to promote hair growth in males with androgenic alopecia who have Norwood-Hamilton classifications of Ha-V or females with androgenic alopecia who have Ludwig-Savin Classifications of I-4, II-1, II-2, or frontal and both with Fitzpatrick Skin Phototypes I-IV.

The iHelmet Laser Comb is a comb-shaped low level laser therapy (LLLT) device that emits laser light with the intention to promote hair growth. The device provides distributed laser to the scalp at 650+/-10nm while the comb teeth simultaneously part the user's hair to ensure the laser light reaches the user's scalp. The Laser Hair Therapy is designed as handheld product, and it consists of main unit, the charging dock and a power cable, as well as it is powered by the built-in rechargeable lithium battery. The device is one-button operated, and has an audible timer that automatically turns the lasers off after the treatment is completed.

This document is a 510(k) Pre-Market Notification, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a novel clinical study with acceptance criteria for a new AI device. Therefore, much of the requested information regarding AI device acceptance criteria and study design is not available in this document.

However, I can extract the relevant information from the provided text about the device and its claimed equivalence.

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't provide specific acceptance criteria in terms of performance metrics (like sensitivity, specificity, accuracy) for a new AI algorithm. Instead, it demonstrates an acceptance of "substantial equivalence" based on various technical and safety benchmarks compared to a predicate device.

2. Sample size used for the test set and the data provenance:

• This document describes safety and performance testing against electrical and biocompatibility standards, not a clinical trial with a "test set" in the context of an AI algorithm.
• The tests mentioned in section 7 ("Test Summary") are bench tests against specific IEC and ISO standards (e.g., IEC 60601-1 for electrical safety, IEC 60825-1 for laser safety, ISO 10993-5/10 for biocompatibility). These standards define test methods and acceptance criteria for the device's physical and electrical characteristics, not for clinical performance or AI algorithm validation.
• Therefore, there is no information about a "sample size" of patients or data, nor data provenance (country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. This document does not describe the establishment of ground truth by clinical experts for a test set of an AI algorithm. The predicate device and the subject device are low-level laser therapy devices, not AI-powered diagnostic or treatment algorithms.

4. Adjudication method for the test set:

• Not applicable. This document does not describe a test set or an adjudication method for an AI algorithm.

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 does not describe an AI device, an MRMC study, or human reader performance.

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

• Not applicable. This document is for a physical medical device (laser comb) and not an AI algorithm.

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

• Not applicable. For the purpose of establishing substantial equivalence, the "ground truth" refers to the established safety and performance of the predicate device and compliance with recognized safety standards for the subject device. There is no AI algorithm requiring a specific type of clinical ground truth.

8. The sample size for the training set:

• Not applicable. This document does not describe an AI device or a training set.

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

• Not applicable. This document does not describe an AI device, a training set, or the establishment of ground truth for it.

In summary: The provided document is a 510(k) summary for a low-level laser therapy device (iHelmet Laser Comb), demonstrating its substantial equivalence to a predicate device. It addresses technical safety and performance aspects, not the validation of an AI algorithm with clinical performance metrics. Therefore, most of the questions related to AI-specific study design (sample size, data provenance, expert ground truth, MRMC studies, standalone performance, training sets) are not applicable to the content of this document.

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iHelmet Hair Growth System (LTD88Lite, LTD36Air, LTD160Pro) 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 Ila-V and for both, Fitzpatrick Classification of Skin Phototypes I to IV.

iHelmet Hair Growth System consists of laser diodes that are spread throughout the helmet. 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. The product will pause automatically treatment if the sensor detects that the head is not in close proximity to the sensor, and will resume again once close enough. At the end of the treatment, an audible tone beeps to indicate the treatment is over and then the helmet automatically shut off.

Here's an analysis of the provided text regarding the iHelmet Hair Growth System's acceptance criteria and studies, organized according to your requested points.

It's important to note that this document is a 510(k) summary for a medical device aiming for substantial equivalence to existing devices. As such, the "studies" described primarily focus on bench testing and comparisons to predicates to demonstrate safety and performance under established standards, rather than formal clinical effectiveness trials with human subjects. There is no mention of a traditional algorithm-based AI system with ground truth development in the context of the provided document. The device is a physical product (infrared lamp for hair growth), not a diagnostic AI system.

Therefore, many of your requested points regarding AI-specific criteria (e.g., ground truth establishment for a test set, AI vs. human reader effectiveness, adjudication methods) are not applicable or cannot be extracted from this documentation.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally based on compliance with various international standards for medical electrical equipment, laser safety, and biocompatibility, as well as maintaining specific physical and operational parameters. The reported performance is the device's adherence to these standards and its performance within defined limits, making it "substantially equivalent" to predicate devices.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not applicable in the context of human subjects or a data test set for an AI model. The "test set" in this document refers to the physical device undergoing various bench tests and comparisons against engineering specifications and predicate devices. No specific "sample size" of a dataset for algorithmic evaluation is mentioned because this is not an AI diagnostic or analytical device.
• Data Provenance: Not applicable. The "data" here comes from direct measurements and compliance testing of the physical iHelmet device itself, against pre-defined engineering standards and benchmarks set by predicate devices.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Not Applicable. This document does not describe the establishment of ground truth by experts for a dataset, as it pertains to a physical medical device (infrared lamp) for hair growth, not an AI system that interprets data or images. The "ground truth" for this device's performance is adherence to established engineering standards and safety limits, and demonstration of substantial equivalence to already cleared devices.

4. Adjudication Method for the Test Set

• Not Applicable. Adjudication methods (e.g., 2+1, 3+1) are typically used in clinical trials or studies to resolve discrepancies among human readers or experts when establishing ground truth for diagnostic AI. This document describes bench testing and comparison studies of a physical device.

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. An MRMC study is not mentioned as this device is not an AI-assisted diagnostic tool for human readers. It is a direct-to-consumer medical device for hair growth.

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

• Not Applicable. This is not an algorithm. It is a physical device.

7. The Type of Ground Truth Used

• The "ground truth" for this device's acceptance is based on adherence to international consensus standards (e.g., IEC, ISO) for electrical safety, laser safety, biocompatibility, and performance, along with demonstrating substantial equivalence to previously cleared predicate devices.
• For biological parameters like hair growth, the ground truth for the predicate devices' effectiveness would have been established through clinical trials (but those trials are not part of this 510(k) submission, which relies on the predicate's prior clearance). The current submission focuses on demonstrating that the new device meets the same safety and performance profiles as the predicates.

8. The Sample Size for the Training Set

• Not Applicable. This device is not an AI system that requires a "training set" in the machine learning sense.

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

• Not Applicable. As there is no AI training set, there's no ground truth establishment for it.

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iHelmet Hair Growth System (Model: LTD200S) is indicated to promote hair growth in females with androgenetic alopecia who have Ludwig-Savin Classifications I - II, in males with androgenetic alopecia who have Norwood Hamilton Classifications IIa - V and for both, Fitzpatrick Classification of Skin Phototypes of I - IV.

iHelmet Hair Growth System consists of laser diodes that are spread throughout the helmet. 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. The product will pause automatically treatment if the sensor detects that the head is not in close proximity to the sensor, and will resume again once close enough. At the end of the treatment, an audible tone beeps to indicate the treatment is over and then the helmet automatically shut off.

The provided text is a 510(k) summary for the iHelmet Hair Growth System (Model: LTD200S). It focuses on establishing substantial equivalence to a predicate device, rather than presenting a detailed study proving the device meets specific acceptance criteria based on clinical outcomes.

Therefore, many of the requested details about acceptance criteria, clinical study design, sample sizes for test and training sets, ground truth establishment, expert involvement, and MRMC studies are not explicitly provided in this regulatory document. This document primarily addresses safety and performance through bench testing and comparison to a predicate device.

Here's a breakdown of the information that can be extracted or inferred from the provided text, addressing your points where possible:

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

The document does not present clinical acceptance criteria for hair growth or efficacy. Instead, it relies on demonstrating substantial equivalence to a predicate device based on technical specifications and safety standards. The "acceptance criteria" here are compliance with recognized safety standards and similarity to the predicate device.

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 describes bench testing of the device for safety and compliance with standards. It does not mention a clinical test set of patients for efficacy evaluation in the way you might expect for an AI algorithm. Therefore, details about sample size, country of origin, or retrospective/prospective nature of a clinical efficacy study are not provided.

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 this document focuses on regulatory clearance via substantial equivalence and bench testing, not a clinical efficacy study relying on expert-established ground truth. Expert involvement would be for regulatory review, not for establishing ground truth for a test set.

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

Not applicable, for the same reasons as point 3.

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. The iHelmet Hair Growth System is a physical device (infrared lamp) for promoting hair growth, not an AI 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. This is not an AI algorithm. The device's performance is standalone in its function as a physical device.

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

For the safety aspects, the "ground truth" is compliance with international safety standards (e.g., IEC, ISO).
For the equivalence claim, the "ground truth" is the established safety and efficacy profile of the predicate devices.
There is no mention of a primary clinical efficacy study using patient outcomes data for the iHelmet itself to establish de novo "ground truth" for hair growth.

8. The sample size for the training set

Not applicable, as this is not an AI algorithm requiring a training set.

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

Not applicable, as this is not an AI algorithm requiring a training set.

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