Harnessing Artificial Intelligence for Diagnosing Androgenetic Alopecia: A Training and Validation Study

Cairo University (CU)

Status: Not yet enrolling

Conditions: Androgenetic Alopecia

Study type: Observational

Funder types: Other

Identifiers

NCT07294313

MD-192-2025

Details and patient eligibility

About

The aim of this study is to develop and validate deep learning models in diagnosis of male and female pattern hair loss, and assessment of its severity based on clinical and trichoscopic image by handheld dermoscopy and administrative data (age and sex).

Full description

The investigators intend to develop and validate artificial intelligence (AI) and machine learning (ML) models in diagnosis of male and female pattern hair loss, and assessment of its severity based on clinical and trichoscopic image using widely available and accessible handheld dermoscopes.

Conventional androgenetic alopecia (AGA) diagnosis and severity assessment are tedious and time-consuming tasks that are prone to human errors. These challenges can be tackled using artificial intelligence (AI), namely leveraging applications of machine learning and artificial neural networks for enhancing the diagnostic accuracy of scalp disease classification systems via dermoscopic image analysis. Computer aided assessment of hair microphotographs was attempted for decades, yet it faced many technical hurdles before the onset of deep learning and neural networks; and currently available software generate inaccurate results compared with visual counting. More accurate methods of analysis are needed for trichoscopic imaging, utilising deep learning image recognition models trained with a large image dataset. A number of deep learning models have been developed in recent years using videodermoscopy that achieved reliable hair density, thickness and severity classification, yet remain limited by small non-inclusive training datasets, need for hair shaving and lack of detailed reporting. Moreover, to our knowledge all previous models depend on image acquisition from expensive standalone videodermoscopy devices that lack widespread availability, rather than handheld dermoscopes that are commonly available.

The study will enroll 400 participants (200 healthy controls and 200 AGA patients). Controls undergo history and trichoscopic exams to exclude hair disorders. Trichoscopic examination will be conducted using a handheld dermoscope (CuTechs DS175) with a specialized field spacer. Patients will be assessed for disease severity using gender-specific scales. Both groups will have standardized digital and trichoscopic images taken for analysis. Images will be used to manually count and classify hairs, assess follicle units, and identify dermoscopic signs. A structured database will store all data and link clinical and image data to support objective diagnosis. AI models, particularly CNNs using transfer learning, will be trained on preprocessed images for classification and severity scoring. Model performance will be evaluated using metrics like accuracy, precision, recall, F1-score, and AUC-ROC compared with metrics reported by expert trichologists to validate accuracy and reliability

Enrollment: 400 estimated patients

Sex: All

Ages: 12 to 50 years old

Volunteers: Accepts Healthy Volunteers

Inclusion and exclusion criteria

for the patient group:

Inclusion criteria:

• Patients with male or female pattern hair loss diagnosed clinically or suspected clinically and confirmed trichoscopically
• Age of disease onset 12-50 years old
• Both genders
• Any grade of androgenetic alopecia
• Any duration of androgenetic alopecia
• Any skin type

Exclusion criteria by clinical and trichoscopic examination:

• Patients with patchy hair loss or Telogen effluvium only.
• Patients with cicatricial alopecia or diffuse alopecia areata
• Patients with inflammatory scalp disorders (psoriasis, seborrheic dermatitis, lichen planopilaris and frontal fibrosing alopecia in a pattern distribution)
• Lack of patient cooperation.

for the control group: apparently healthy participants not suffering from the following: AGA, patchy hair loss, cicatricial alopecia, diffuse alopecia areata, inflammatory scalp disorders (psoriasis, seborrheic dermatitis, lichen planopilaris and frontal fibrosing alopecia in a pattern distribution).

Trial design

400 participants in 2 patient groups

androgenetic alopecia

Description:

Patients diagnosed clinically and trichoscopically with androgenetic alopecia of both genders. The diagnosis of AGA requires fulfillment of the primary criterion, plus one or more of the secondary criteria, and absence of exclusion criteria: * Primary criterion Hair shaft thickness heterogeneity on the frontal and/or vertex scalp, defined as proportion of hairs \< 0.06 mm (including intermediate, thin, and vellus hairs) ≥20%. * Secondary criteria 1. Proportion of single hair follicle unit on the frontal and/or vertex scalp ≥30%. 2. Proportion of vellus hairs on the frontal and/or vertex scalp \>10%. 3. There are at least two other dermoscopic signs: brown peripilar sign, yellow dots,white dots, scalp honeycomb pigmentation * Exclusion criteria Black dots, broken hairs, exclamation mark hairs

normal controls

Description:

apparently normal participants not suffering from the following : 1. androgenetic alopecia 2. patchy hair loss. 3. cicatricial alopecia or diffuse alopecia areata 4. inflammatory scalp disorders (psoriasis, seborrheic dermatitis, lichen planopilaris and frontal fibrosing alopecia in a pattern distribution)