Automated Early Detection Of Androgenetic Alopecia Using Deep Learning On Trichoscopic Images From A Korean Cohort: A Retrospective Model Development And Validation Study

July 2025 in “ Harvard Dataverse ”

Ewha Medical Journal Medical Journal

androgenetic alopecia deep learning trichoscopic images early detection dermatological applications pattern hair loss AI in dermatology

Studysummary A deep learning model accurately detects early hair loss signs using scalp images.

Automatically generated from the study's abstract, not written by a person, and not a review of the full paper. Not medical advice or a treatment recommendation. Read the original study, and consult a qualified healthcare professional before changing treatment. Full disclaimer ·

View this study on dataverse.harvard.edu →

Discuss this study in the Community →

Similar Research

5 / 1000+ results

research Advance in classification of androgenetic alopecia

January 2004 in “Journal of Clinical Dermatology”

Automated Early Detection of Androgenetic Alopecia Using Deep Learning on Trichoscopic Images From a Korean Cohort: A Retrospective Model Development and Validation Study

research Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study

July 2025 in “The Ewha Medical Journal”

This study developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.

BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks, and Clinical Features

research BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks and Clinical Features

July 2026 in “International Journal of Advanced Research in Science Communication and Technology”

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

research Ewha Medical Journal’s inclusion in PubMed Central and PubMed, and artificial intelligence and guidelines in this issue

1 citations , July 2025 in “The Ewha Medical Journal”

The Ewha Medical Journal is now in PubMed, has an AI article editor, and offers Korean reporting guidelines.

research Caffeine as an Active Ingredient in Cosmetic Preparations Against Hair Loss: A Systematic Review of Available Clinical Evidence

February 2025 in “Healthcare”

In this systematic review, the researchers observed that topical caffeine preparations appear safe and effective against hair loss, but most studies had low-quality evidence due to design flaws, indicating the need for better-designed trials before drawing definitive conclusions.