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<p>Accurately differentiating scaly erythematous rashes among psoriasis, eczema, and dermatophytosis remains a clinical challenge, particularly for non-dermatologists. This study aimed to develop and evaluate deep learning models using macroscopic clinical images to classify these conditions and compare their performance with that of non-specialists. A total of 2940 images were sourced from public datasets, the Siriraj Dermatology databank, and newly collected images from Thai participants. Among sixteen evaluated models, the Swin demonstrated the best performance and interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that the model focused on clinically relevant lesion features. Most importantly, in a pilot comparison, the Swin outperformed non-specialists in diagnostic accuracy. However, given the limited sample size of 30 images and 30 evaluators, these results should be interpreted as exploratory. Future studies with larger datasets and diverse clinician cohorts are warranted to confirm these findings and to support clinical integration.</p>

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Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Nutcha Yodrabum,
  • Chanisada Wongpraparut,
  • Taravichet Titijaroonroj,
  • Leena Chularojanamontri,
  • Sumanas Bunyaratavej,
  • Narumol Silpa-archa,
  • Chayada Chaiyabutr,
  • Thanapon Noraset,
  • Teerapat Paringkarn,
  • Thrit Hutachoke,
  • Prameyuda Watchirakaeyoon,
  • Pantaree Kobkurkul,
  • Sirin Apichonbancha,
  • Praveena Chiowchanwisawakit

摘要

Accurately differentiating scaly erythematous rashes among psoriasis, eczema, and dermatophytosis remains a clinical challenge, particularly for non-dermatologists. This study aimed to develop and evaluate deep learning models using macroscopic clinical images to classify these conditions and compare their performance with that of non-specialists. A total of 2940 images were sourced from public datasets, the Siriraj Dermatology databank, and newly collected images from Thai participants. Among sixteen evaluated models, the Swin demonstrated the best performance and interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that the model focused on clinically relevant lesion features. Most importantly, in a pilot comparison, the Swin outperformed non-specialists in diagnostic accuracy. However, given the limited sample size of 30 images and 30 evaluators, these results should be interpreted as exploratory. Future studies with larger datasets and diverse clinician cohorts are warranted to confirm these findings and to support clinical integration.