Skin Image Analysis for Detection and Quantitative Assessment of Dermatitis, Vitiligo, and Alopecia Areata Lesions: A Systematic Literature Review

January 2025 in “ BMC Medical Informatics and Decision Making ”

Athanasios Kallipolitis, Κωνσταντίνος Μούτσελος, Argyriοs Zafeiriou, Stelios Andreadis, Anastasia Matonaki, Thanos G. Stavropoulos, Ilias Maglogiannis

alopecia areata vitiligo dermatitis skin image analysis deep learning image processing algorithms computer vision quantitative disease assessment machine learning disease severity scores

TLDR Computer vision techniques can help detect and assess skin conditions like vitiligo, alopecia areata, and dermatitis.

This systematic literature review examines the application of machine learning (ML) and computer vision (CV) techniques in analyzing skin images for detecting and assessing conditions like dermatitis, vitiligo, and alopecia areata (AA). The review analyzed 434 papers, including 46 studies, highlighting the potential of ML and CV to improve diagnosis and monitoring by providing automated, reliable outcomes. For AA, methods like AloNet and HairComb use CNN-based segmentation, achieving high accuracy rates, such as 96.94% in classification tasks. The review discusses the integration of severity scores like SALT, EASI, and VASI into ML approaches, emphasizing the need for automated tools for accurate score calculation. Despite challenges like data scarcity and image quality variability, the study underscores the potential of these technologies to enhance dermatological assessments and clinical workflows.

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Research cited in this study

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research Multi-Class Support Vector Machine Classification for Detecting Alopecia Areata and Scalp Diseases

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.

research Ensemble of Pre-Learned Deep Learning Model and an Optimized LSTM for Alopecia Areata Classification

7 citations , October 2023 in “Journal of Intelligent & Fuzzy Systems”

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

August 2023 in “Skin Research and Technology”

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

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

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

research Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition

May 2023 in “Indian journal of science and technology”

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

research Prediction of Alopecia Areata Using CNN

15 citations , May 2023

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

research Alopecia Areata: An Updated Review for 2023

91 citations , May 2023 in “Journal of Cutaneous Medicine and Surgery”

Alopecia Areata affects 2% globally, with treatments like essential oils, garlic, and JAK inhibitors showing promise, but more research is needed.

research Investigative Guidelines for Alopecia Areata

66 citations , May 2011 in “Dermatologic therapy”

Guidelines help design better trials to compare alopecia areata treatments.

Skin Image Analysis for Detection and Quantitative Assessment of Dermatitis, Vitiligo, and Alopecia Areata Lesions: A Systematic Literature Review

Research cited in this study

research Multi-Class Support Vector Machine Classification for Detecting Alopecia Areata and Scalp Diseases

research Ensemble of Pre-Learned Deep Learning Model and an Optimized LSTM for Alopecia Areata Classification

research Two-Dimensional Planimetry for Alopecia Areata Severity Evaluation Compared With Severity of Alopecia Tool: A Pilot Study

research AB-MTE Deep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata

research Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition

research Prediction of Alopecia Areata Using CNN

research Alopecia Areata: An Updated Review for 2023

research FRCNN Based Deep Learning for Identification and Classification of Alopecia Areata

research Automating Hair Loss Labels for Universally Scoring Alopecia from Images

research An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach

research Alopecia Areata: An Update on Etiopathogenesis, Diagnosis, and Management

research Classification Framework for Healthy Hair and Alopecia Areata: A Machine Learning Approach

research Pre-Trained Classification of Scalp Conditions Using Image Processing

research Quantifying Alopecia Areata via Texture Analysis to Automate the SALT Score Computation

research Investigative Guidelines for Alopecia Areata