Androgenetic AlopeciaNCT07294313
Harnessing Artificial Intelligence for Diagnosing Androgenetic Alopecia: A Training and Validation Study
An observational study of Androgenetic Alopecia, run by Cairo University at 1 site in Egypt.
Trial 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). Read more about this study
- Who is it for?
- Ages 12 to 50 yearsHealthy volunteers can take part
- Conditions the study is for
- Androgenetic Alopecia
- How many people
- 400 participants
- Where
- 1 siteEgypt
- Study dates
- 25 Apr 2026to 25 Nov 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 12 to 50 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Study population: Patients diagnosed clinically and trichoscopically with androgenetic alopecia of both genders and apparently healthy controls.
Who may be able to join
- 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
Show all 12Show fewer
- 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).
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Egypt1 site
Faculty of Medicine Cairo University
Cairo, Cairo Governorate, 11553
Noura Nour, MSc, MBBch, 00201271451744, nouraadel41929@postgrad.kasralainy.edu.eg
About this study
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).
Read the detailed 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
- 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
- Medical subject headings
- Alopecia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 year
Secondary outcomes (2)
assessment of severity of androgenetic alopecia using AI
Assess accuracy, sensitivity, specificity and positive predictive value of the trained AI models in assessment of severity of androgenetic alopecia as regards:
- Clinical classification (Sinclair scale for female pattern baldness and the Hamilton-Norwood scale for male pattern baldness)
- Trichoscopic parameters: mean hair thickness (in micrometer, using planimetric analysis), hair density (frequency per cm2), proportion of terminal hairs, proportion of vellus hairs, number of hairs per follicular unit, presence of brown peripilar sign, yellow dots, and scalp honeycomb pigmentation.
Measured: 1 year
facilitation of AI assessment using macroscopic imagies
To compare the model's diagnostic accuracy using the macroscopic versus trichoscopic images alone
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Dec 2025
- Study start25 Apr 2026 (estimated)
- Primary completion25 Sep 2026 (estimated)
- Study completion25 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Androgenetic AlopeciaStudies of the same condition, open and under way.
- More trials run by Cairo UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Noura Adel Ahmed Abdelghany Nour, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07294313
- Sponsor's protocol ID
- MD-192-2025
- Status last verified
- Mar 2026
- First submitted
- 7 Dec 2025
- Last updated
- 20 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: relevant praticipant data shall be provided to researchers who request them on reasonable basis
Publications
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.
PubMed 34426987Wang 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.
PubMed 38533753Kuczara 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.
PubMed 38610726Young 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.
PubMed 32229141Devjani 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.
PubMed 37166619Bokhari 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.
PubMed 36161091
Study at a glance
From the registry record, last updated by the sponsor on 20 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Harnessing Artificial Intelligence for Diagnosing Androgenetic Alopecia: A Training and Validation Study
- ClinicalTrials.gov ID
- NCT07294313
- Acronym / sponsor ID
- MD-192-2025
- Conditions
- Androgenetic Alopecia
- Keywords
- Androgenetic Alopecia, deep learning, Artificial Intelligence, Convoluted neural network, trichoscopy, female pattern hair loss, male pattern hair loss, handheld dermoscope
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- 25 Apr 2026 (estimated)
- Primary completion
- 25 Sep 2026 (estimated)
- Study completion
- 25 Nov 2026 (estimated)
- First posted
- 19 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Detailed 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
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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) |
Who may qualify
- Ages
- 12 to 50 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- Patients diagnosed clinically and trichoscopically with androgenetic alopecia of both genders and apparently healthy controls.
Key inclusion criteria (12 listed)
- 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
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
assessment of diagnostic capability of AI in AGA
Time frame: 1 year
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.
2 secondary outcomes
assessment of severity of androgenetic alopecia using AI
Time frame: 1 year
facilitation of AI assessment using macroscopic imagies
Time frame: 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Mar 2026.
- Background: PubMed 34426987Sacha 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.
- Background: PubMed 38533753Wang 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.
- Background: PubMed 38610726Kuczara 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.
- Background: PubMed 32229141Young 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.
- Background: PubMed 37166619Devjani 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.
- Background: PubMed 36161091Bokhari 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.
- Individual participant dataSharing planned: Yes. relevant praticipant data shall be provided to researchers who request them on reasonable basis
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Androgenetic Alopecia, run by Cairo University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 12 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 20 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Noura Nour, MSc, MBBCh (00201271451744, nouraadel41929@postgrad.kasralainy.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
assessment of diagnostic capability of AI in AGA (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07294313, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Mar 2026.