Analysis of Trichoscopic Images Using Deep Neural Networks for the Diagnosis and Activity Assessment of Alopecia Areata – A Retrospective Study

September 2025 in “ JDDG Journal der Deutschen Dermatologischen Gesellschaft ”

Raffaele Dante Caposiena, V P Orlova, Nicola di Meo, Iris Zalaudek

alopecia areata trichoscopic images deep neural networks diagnosis activity assessment scalp diseases artificial intelligence AA AI

TLDR AI can accurately diagnose and assess alopecia areata using scalp images.

This study developed a deep learning framework to aid in diagnosing alopecia areata (AA) and assessing its activity level using trichoscopic images. The retrospective analysis involved distinguishing AA from other scalp diseases and healthy controls, achieving an overall accuracy of 88.92% and an F1 score of 88.17% in identifying AA. Additionally, the model assessed AA activity levels with an accuracy of 83.33% and an F1 score of 83.36%. The findings suggest that artificial intelligence can enhance the accuracy of AA diagnosis and staging, potentially improving patient care.

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4 citations , January 2024 in “JEADV. Journal of the European Academy of Dermatology and Venereology/Journal of the European Academy of Dermatology and Venereology”

Baricitinib and ritlecitinib are recommended for severe alopecia areata, with other treatments available off-label.

research Treatments for Alopecia Areata: A Network Meta-Analysis

October 2023 in “The Cochrane library”

The medicine baricitinib was found to notably improve hair regrowth in alopecia areata, but more research is needed on its side effects and other treatments.

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5 citations , October 2023 in “International Journal on Recent and Innovation Trends in Computing and Communication”

The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.

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The new model improves Alopecia Areata classification accuracy to 93.1%.

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August 2023 in “Skin Research and Technology”

Measuring bald patch size can help grade hair loss severity, with photograph-based evaluation being more reliable.

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5 citations , June 2023 in “Engineering Technology & Applied Science Research”

The AI model accurately classifies Alopecia Areata with 96.94% accuracy.

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2 citations , June 2023 in “Skin Research and Technology”

Yellow dots and short vellus hairs are the most common signs of Alopecia Areata (AA), and trichoscopy can help diagnose AA and track treatment progress.

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The new deep learning system can accurately recognize hair loss conditions with a 95.11% success rate.

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15 citations , May 2023

A new CNN model can detect Alopecia Areata with 98% accuracy.

research Trichoscopy: An Update

8 citations , February 2023 in “Actas Dermo-Sifiliográficas”

Dermatologists should learn trichoscopy to better diagnose hair loss conditions.

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9 citations , February 2023

The model accurately detects alopecia areata with 84.3% accuracy.

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The machine learning model accurately detected hair loss and scalp diseases using processed images.

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The AI system HairComb accurately scores hair loss severity, matching dermatologist assessments.

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Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.

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The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.

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A deep learning model accurately predicts male hair loss types using scalp images.

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The article simplifies trichoscopy terms and offers a new diagnostic flowchart.

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JAK inhibitors show promise for treating alopecia areata, but more research is needed.

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Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.

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Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.

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The system can automatically classify scalp conditions with 85% accuracy.

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Fibrosing alopecia in a pattern distribution is a hair loss condition often confused with other types, requiring early treatment but usually not resulting in significant hair regrowth.

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A new tool can predict hair regrowth in patchy alopecia areata patients.

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Trichoscopy is a useful tool for diagnosing hair and scalp issues, but doctors must be careful to avoid mistakes.

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Trichoscopy is useful for diagnosing and monitoring hair and scalp conditions over time.

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Trichoscopy helps diagnose and monitor alopecia areata by looking at a combination of specific hair and scalp features.

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Researchers created a fast, accurate computer program to measure hair loss in alopecia areata patients.

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Trichoscopy is a quick, cost-effective tool for diagnosing different hair loss conditions.

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Trichoscopy is a useful tool for diagnosing different hair and scalp diseases without surgery.

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