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
”
Research cited in this study
34 / 34 results
research European Expert Consensus Statement on the Systemic Treatment of Alopecia Areata
Baricitinib and ritlecitinib are recommended for severe alopecia areata, with other treatments available off-label.
research Treatments for Alopecia Areata: A Network Meta-Analysis
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.
research Multi-Class Support Vector Machine Classification for Detecting Alopecia Areata and Scalp Diseases
The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.
research Ensemble of Pre-Learned Deep Learning Model and an Optimized LSTM for Alopecia Areata Classification
The new model improves Alopecia Areata classification accuracy to 93.1%.
research Two-Dimensional Planimetry for Alopecia Areata Severity Evaluation Compared With Severity of Alopecia Tool: A Pilot Study
Measuring bald patch size can help grade hair loss severity, with photograph-based evaluation being more reliable.
research AB-MTE Deep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata
The AI model accurately classifies Alopecia Areata with 96.94% accuracy.
research Trichoscopy Patterns in Alopecia Areata: A Systematic Review and Meta-Analysis
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.
research Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition
The new deep learning system can accurately recognize hair loss conditions with a 95.11% success rate.
research Prediction of Alopecia Areata Using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
research Trichoscopy: An Update
Dermatologists should learn trichoscopy to better diagnose hair loss conditions.
research FRCNN Based Deep Learning for Identification and Classification of Alopecia Areata
The model accurately detects alopecia areata with 84.3% accuracy.
research Hair and Scalp Disease Detection Using Machine Learning and Image Processing
The machine learning model accurately detected hair loss and scalp diseases using processed images.
research Automating Hair Loss Labels for Universally Scoring Alopecia from Images
The AI system HairComb accurately scores hair loss severity, matching dermatologist assessments.
research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks
Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.
research An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach
The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.
research Deep Learning-Based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
A deep learning model accurately predicts male hair loss types using scalp images.
research Update on Trichoscopy: Integration of Terminology by Systematic Approach and Proposal of a Diagnostic Flowchart
The article simplifies trichoscopy terms and offers a new diagnostic flowchart.
research Alopecia Areata: An Update on Etiopathogenesis, Diagnosis, and Management
JAK inhibitors show promise for treating alopecia areata, but more research is needed.
research Classification Framework for Healthy Hair and Alopecia Areata: A Machine Learning Approach
Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.
research Common Causes of Hair Loss: Clinical Manifestations, Trichoscopy, and Therapy
The document concludes that understanding and treating hair loss requires recognizing its various types and using appropriate diagnostic tools and treatments.
research Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue
Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.
research Pre-Trained Classification of Scalp Conditions Using Image Processing
The system can automatically classify scalp conditions with 85% accuracy.
research Fibrosing Alopecia in a Pattern Distribution
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.
research Alopecia Areata Predictive Score: A New Trichoscopy-Based Tool to Predict Treatment Outcome in Patients with Patchy Alopecia Areata
A new tool can predict hair regrowth in patchy alopecia areata patients.
research Trichoscopy Tips
Trichoscopy is a useful tool for diagnosing hair and scalp issues, but doctors must be careful to avoid mistakes.
research Dynamic Trichoscopy: Advancements in Hair and Scalp Examination
Trichoscopy is useful for diagnosing and monitoring hair and scalp conditions over time.
research Trichoscopy of Alopecia Areata: An Update
Trichoscopy helps diagnose and monitor alopecia areata by looking at a combination of specific hair and scalp features.
research Nail Changes in Alopecia Areata: An Update and Review
Nail problems are common in people with alopecia areata, often leading to cosmetic and functional issues, but more research is needed for treatment guidelines.
research Quantifying Alopecia Areata via Texture Analysis to Automate the SALT Score Computation
Researchers created a fast, accurate computer program to measure hair loss in alopecia areata patients.
research Evaluation and Diagnosis of the Hair Loss Patient
Trichoscopy is a quick, cost-effective tool for diagnosing different hair loss conditions.
research Trichoscopy
Trichoscopy is a useful tool for diagnosing different hair and scalp diseases without surgery.
research Hair and Scalp Dermatoscopy
Dermatoscopy is useful for identifying different hair and scalp conditions and can reduce the need for biopsies.
research Trichoscopy for Common Hair Loss Diseases: Algorithmic Method for Diagnosis
The conclusion is that an algorithm using trichoscopy helps diagnose different types of hair loss but may need updates and a biopsy if results are unclear.
research TrichoScan: A Novel Tool for the Analysis of Hair Growth In Vivo
TrichoScan is a reliable method for measuring hair growth and is useful for assessing hair loss treatments.
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A user created a web app called TrichoMetrics to track hair loss using photos and metrics like density and thinning. The app supports tracking treatments like Minoxidil and finasteride, with features for setting baselines, comparing progress, and logging treatment adherence.
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The conversation discusses the use of trichoscopy to predict hair loss patterns and inform treatment approaches. Treatments mentioned include minoxidil, finasteride, and dutasteride, with varying effectiveness based on gender-specific hair loss patterns.
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A dermatologist checked for hereditary hair loss using blood tests and plans to perform a trichogram. The user had slightly low Vitamin D3 levels, and the trichogram may help confirm androgenetic alopecia.
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The post discusses using ImageJ software to objectively track hair regrowth progress. The user is treating Androgen alopecia with dutasteride and minoxidil.
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