Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review

• Published in: BMC medical informatics and decision making Vol. 25; no. 1; pp. 10 - 17
• Main Authors: Kallipolitis, Athanasios, Moutselos, Konstantinos, Zafeiriou, Argyriοs, Andreadis, Stelios, Matonaki, Anastasia, Stavropoulos, Thanos G., Maglogiannis, Ilias
• Format: Journal Article
• Language: English
• Published: London BioMed Central 08.01.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Alopecia; Alopecia Areata; Alopecia Areata - diagnostic imaging; Analysis; Annotations; Atopy; Automation; Baldness; Benign skin lesions; Classification; Computer vision; Datasets; Deep Learning; Dermatitis; Dermatitis - diagnostic imaging; Dermatology; Diagnosis; Diagnostic imaging; Disease detection; Health Informatics; Humans; Image analysis; Image processing; Image segmentation; Inflammation; Information Systems and Communication Service; Lesions; Literature reviews; Machine learning; Management of Computing and Information Systems; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Methods; Neural networks; Skin; Skin diseases; Skin image analysis; Systematic Review; Vitiligo; Vitiligo - diagnostic imaging; Greece; Vitiligo; Alopecia Areata; Dermatitis; Skin image analysis; Benign skin lesions; Machine learning
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02843-2

Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and automated detection as well as quantitative assessment of skin diseases. This review provides a systematic literature search regarding the analysis of computer vision techniques applied to these benign skin conditions, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The review examines deep learning architectures and image processing algorithms for segmentation, feature extraction, and classification tasks employed for disease detection. It also focuses on practical applications, emphasizing quantitative disease assessment, and the performance of various computer vision approaches for each condition while highlighting their strengths and limitations. Finally, the review denotes the need for disease-specific datasets with curated annotations and suggests future directions toward unsupervised or self-supervised approaches. Additionally, the findings underscore the importance of developing accurate, automated tools for disease severity score calculation to improve ML-based monitoring and diagnosis in dermatology. Trial registration Not applicable.