Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
Abstract 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...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02843-2 |
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| author | Athanasios Kallipolitis Konstantinos Moutselos Argyriοs Zafeiriou Stelios Andreadis Anastasia Matonaki Thanos G. Stavropoulos Ilias Maglogiannis |
| author_facet | Athanasios Kallipolitis Konstantinos Moutselos Argyriοs Zafeiriou Stelios Andreadis Anastasia Matonaki Thanos G. Stavropoulos Ilias Maglogiannis |
| author_sort | Athanasios Kallipolitis |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-a6f77a7f3fce4535814c6d6510730236 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-a6f77a7f3fce4535814c6d65107302362025-01-12T12:26:24ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-0125111710.1186/s12911-024-02843-2Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature reviewAthanasios Kallipolitis0Konstantinos Moutselos1Argyriοs Zafeiriou2Stelios Andreadis3Anastasia Matonaki4Thanos G. Stavropoulos5Ilias Maglogiannis6Department of Digital Systems, University of PiraeusDepartment of Digital Systems, University of PiraeusDepartment of Digital Systems, University of PiraeusPfizer Center for Digital InnovationPfizer Center for Digital InnovationPfizer Center for Digital InnovationDepartment of Digital Systems, University of PiraeusAbstract 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.https://doi.org/10.1186/s12911-024-02843-2Skin image analysisBenign skin lesionsDermatitisAlopecia AreataVitiligoMachine learning |
| spellingShingle | Athanasios Kallipolitis Konstantinos Moutselos Argyriοs Zafeiriou Stelios Andreadis Anastasia Matonaki Thanos G. Stavropoulos Ilias Maglogiannis Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review BMC Medical Informatics and Decision Making Skin image analysis Benign skin lesions Dermatitis Alopecia Areata Vitiligo Machine learning |
| title | Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review |
| title_full | Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review |
| title_fullStr | Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review |
| title_full_unstemmed | Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review |
| title_short | Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review |
| title_sort | skin image analysis for detection and quantitative assessment of dermatitis vitiligo and alopecia areata lesions a systematic literature review |
| topic | Skin image analysis Benign skin lesions Dermatitis Alopecia Areata Vitiligo Machine learning |
| url | https://doi.org/10.1186/s12911-024-02843-2 |
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