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).
Eligibility
Sex
ALL
Min age
12 Years
Max age
50 Years
Healthy volunteers
Accepted
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).
Primary outcome measure(s)
assessment of diagnostic capability of AI in AGA — 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.
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
We use cookies to analyse site traffic and improve your experience. With your consent, we may also use cookies for advertising. You can change your choice at any time.