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

NCT07294313 | Not Yet Recruiting

• 1 other identifier: MD-192-2025
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 1

Brief Summary

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).

Conditions

Androgenetic Alopecia

Keywords

Androgenetic Alopecia; deep learning; Artificial Intelligence; Convoluted neural network; trichoscopy; female pattern hair loss; male pattern hair loss; handheld dermoscope; hair count

Outcome Measures

Primary Outcomes (1)

• assessment of diagnostic capability of AI in AGA

  Assess accuracy, sensitivity, specificity and positive predictive value of the trained AI models in differentiating AGA affected from non-AGA affected subjects using their macroscopic and trichoscopic images.

  1 year

Secondary Outcomes (2)

• assessment of severity of androgenetic alopecia using AI

  1 year

• facilitation of AI assessment using macroscopic imagies

  1 year

Study Arms (2)

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

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)

Eligibility Criteria

• Age: 12 Years - 50 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Probability Sample

Study Population

Patients diagnosed clinically and trichoscopically with androgenetic alopecia of both genders and apparently healthy controls.

You may qualify if:

• 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

You may not qualify if:

• 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).

Sponsors & Collaborators

• Cairo University: lead

Study Sites (1)

Faculty of Medicine Cairo University

Cairo, Cairo Governorate, 11553, Egypt

Location

Related Publications (6)

• 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.

  PMID: 34426987 BACKGROUND

• 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.

  PMID: 38533753 BACKGROUND

• 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.

  PMID: 38610726 BACKGROUND

• 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.

  PMID: 32229141 BACKGROUND

• 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.

  PMID: 37166619 BACKGROUND

• 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.

  PMID: 36161091 BACKGROUND

MeSH Terms

Conditions

Alopecia

Condition Hierarchy (Ancestors)

Hypotrichosis; Hair Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Noura Nour, MSc, MBBCh

CONTACT

00201271451744; nouraadel41929@postgrad.kasralainy.edu.eg

Ahmed Mourad, MD

CONTACT

00201021534245; ahmedmourad@kasralainy.edu.eg

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Lecturer

Study Record Dates

• First Submitted: December 7, 2025
• First Posted: December 19, 2025
• Study Start: April 25, 2026
• Primary Completion (Estimated): September 25, 2026
• Study Completion (Estimated): November 25, 2026
• Last Updated: March 20, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE
• Time Frame: from 2027 indefintely
• Access Criteria: access will be provided to deidentified data including all study parameters

Locations

EG (1)