Kallipolitis Athanasios, Moutselos Konstantinos, Zafeiriou Argyriοs, Andreadis Stelios, Matonaki Anastasia, Stavropoulos Thanos G, Maglogiannis Ilias
Department of Digital Systems, University of Piraeus, Piraeus, Greece.
Pfizer Center for Digital Innovation, Thessaloniki, Greece.
BMC Med Inform Decis Mak. 2025 Jan 8;25(1):10. 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.
白癜风、斑秃、特应性皮炎和淤积性皮炎是常见的皮肤疾病，它们在诊断和评估方面存在挑战。皮肤图像分析是一种很有前景的非侵入性方法，可用于皮肤疾病的客观自动检测和定量评估。本综述按照系统评价和Meta分析的首选报告项目指南，对应用于这些良性皮肤疾病的计算机视觉技术分析进行了系统的文献检索。该综述研究了用于疾病检测的深度学习架构和用于分割、特征提取及分类任务的图像处理算法。它还侧重于实际应用，强调定量疾病评估，以及针对每种情况的各种计算机视觉方法的性能，同时突出它们的优势和局限性。最后，该综述指出需要带有精心策划注释的疾病特定数据集，并建议朝着无监督或自监督方法的未来方向发展。此外，研究结果强调了开发准确的自动疾病严重程度评分计算工具对于改善皮肤科基于机器学习的监测和诊断的重要性。试验注册：不适用。
