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

Simple item page
dc.contributor.authorYodrabum, Nutcha
dc.contributor.authorWongpraparut, Chanisada
dc.contributor.authorTitijaroonroj, Taravichet
dc.contributor.authorChularojanamontri, Leena
dc.contributor.authorBunyaratavej, Sumanas
dc.contributor.authorSilpa-archa, Narumol
dc.contributor.authorChaiyabutr, Chayada
dc.contributor.authorNoraset, Thanapon
dc.contributor.authorParingkarn, Teerapat
dc.contributor.authorHutachoke, Thrit
dc.contributor.authorWatchirakaeyoon, Prameyuda
dc.contributor.authorKobkurkul, Pantaree
dc.contributor.authorApichonbancha, Sirin
dc.contributor.authorChiowchanwisawakit, Praveena
dc.date.accessioned2026-08-06T10:56:36Z
dc.date.available2026-08-06T10:56:36Z
dc.date.issued2026-12-01
dc.description.abstractAccurately 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.
dc.identifier.citationScientific Reports, 16(1), 2026
dc.identifier.doi10.1038/s41598-025-29562-6
dc.identifier.issn20452322
dc.identifier.other2-s2.0-105026713927
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18378
dc.sourceScientific Reports
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectDermatophytosis
dc.subjectEczema
dc.subjectPsoriasis
dc.titleComparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema
dc.typeArticle

Files

Collections

Research