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Author: search.filters.author.Chiowchanwisawakit, PraveenaHas files: No

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    Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema
    (2026-12-01)
    Yodrabum, Nutcha
    ;
    Wongpraparut, Chanisada
    ;
    Titijaroonroj, Taravichet
    ;
    Chularojanamontri, Leena
    ;
    Bunyaratavej, Sumanas
    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.
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    Validation of students blended learning course experience in Thai medical education
    (2025-10-01)
    Wilaphan, Kotchaphan
    ;
    Chiowchanwisawakit, Praveena
    ;
    Songkram, Noawanit
    Blended learning is a widely used method in education to promote active learning and enhance students’ learning outcomes. Therefore, evaluating the quality of blended learning courses requires an effective model for benchmarking, which can improve student satisfaction and is crucial for quality assurance in higher education. This study aimed to validate and examine student’s blended learning course experience in Thai medical education using a quantitative research design. A total of 560 medical students from a large medical school in Thailand participated. Data analysis was conducted using Confirmatory Factor Analysis (CFA). The findings indicate that components and indicators were: (1) general skills with six indicators, (2) online sessions with five indicators, (3) clear goals and standards with four indicators, (4) good teaching with six indicators, (5) appropriate assessment with four indicators, and (6) appropriate workload with three indicators. The second order CFA demonstrated that the student blended learning course experience model had an acceptable fit with χ<sup>2</sup> (249) = 1.148, p =.056, RMSEA =.016, RMR =.005, SRMR =.05, GFI =.995, AGFI =.995, NFI =.961, and CFI =.995. These findings could contribute to the development of guidelines for designing a medicine bachelor’s degree curriculum that incorporates blended learning methods.