Harnessing Artificial Intelligence for Diagnosing Androgenetic Alopecia: A Training and Validation Study
NCT ID: NCT07294313
Last Updated: 2025-12-30
Study Results
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Basic Information
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NOT_YET_RECRUITING
400 participants
OBSERVATIONAL
2025-12-25
2026-11-25
Brief Summary
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Detailed Description
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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
Conditions
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Keywords
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Study Design
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CASE_CONTROL
PROSPECTIVE
Study Groups
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androgenetic alopecia
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
No interventions assigned to this group
normal controls
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)
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* 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
* 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).
12 Years
50 Years
ALL
Yes
Sponsors
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Cairo University
OTHER
Responsible Party
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Noura Adel Ahmed Abdelghany Nour
Assistant Lecturer
Locations
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Faculty of Medicine Cairo University
Cairo, Cairo Governorate, Egypt
Countries
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Central Contacts
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Facility Contacts
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Noura Nour, MSc, MBBch
Role: primary
References
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Sacha JP, Caterino TL, Fisher BK, Carr GJ, Youngquist RS, D'Alessandro BM, Melione A, Canfield D, Bergfeld WF, Piliang MP, Kainkaryam R, Davis MG. Development and qualification of a machine learning algorithm for automated hair counting. Int J Cosmet Sci. 2021 Nov;43 Suppl 1:S34-S41. doi: 10.1111/ics.12735.
Wang Y, Ding W, Yao M, Li Y, Wang M, Wang L, Li Z, Sun S, Yang M, Zhu Y, Zhou N. Diagnostic and grading criteria for androgenetic alopecia using dermoscopy. Skin Res Technol. 2024 Apr;30(4):e13649. doi: 10.1111/srt.13649.
Kuczara A, Waskiel-Burnat A, Rakowska A, Olszewska M, Rudnicka L. Trichoscopy of Androgenetic Alopecia: A Systematic Review. J Clin Med. 2024 Mar 28;13(7):1962. doi: 10.3390/jcm13071962.
Young AT, Xiong M, Pfau J, Keiser MJ, Wei ML. Artificial Intelligence in Dermatology: A Primer. J Invest Dermatol. 2020 Aug;140(8):1504-1512. doi: 10.1016/j.jid.2020.02.026. Epub 2020 Mar 27.
Devjani S, Ezemma O, Kelley KJ, Stratton E, Senna M. Androgenetic Alopecia: Therapy Update. Drugs. 2023 Jun;83(8):701-715. doi: 10.1007/s40265-023-01880-x. Epub 2023 May 11.
Bokhari L, Cottle P, Grimalt R, Kasprzak M, Sicinska J, Sinclair R, Tosti A. Efficiency of Hair Detection in Hair-to-Hair Matched Trichoscopy. Skin Appendage Disord. 2022 Sep;8(5):382-388. doi: 10.1159/000524345. Epub 2022 May 12.
Other Identifiers
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MD-192-2025
Identifier Type: -
Identifier Source: org_study_id