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

This systematic literature review explores the use of skin image analysis for the detection and quantitative assessment of vitiligo, alopecia areata, and dermatitis. It highlights the potential of computer vision techniques, including deep learning architectures and image processing algorithms, for segmenting, extracting features, and classifying these skin conditions. The review emphasizes the importance of quantitative disease assessment and evaluates the performance of various computer vision approaches, noting their strengths and limitations. It calls for the development of disease-specific datasets with curated annotations and suggests future research directions, such as unsupervised or self-supervised methods. The findings stress the need for accurate, automated tools to calculate disease severity scores, which could enhance machine learning-based monitoring and diagnosis in dermatology.