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Author: search.filters.author.Titijaroonroj, TaravichetSubject: search.filters.subject.Artificial intelligence

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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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    LymphoNet: A Deep Learning for Lymph Node Detection from Histological Image
    (2024-01-01)
    Uthatham, Ason
    ;
    Yodrabum, Nutcha
    ;
    Sinmaroeng, Chanya
    ;
    Chaikangwan, Irin
    ;
    Titijaroonroj, Taravichet
    Identifying lymph nodes within a lymph node flap is crucial for precise lymph node quantification. Even when observed under a microscope, this task is known for being extremely challenging and susceptible to misidentification. Histopathology is the most reliable method for detecting lymph nodes in histopathological slides, but it is a very time-consuming and labor-intensive technique. In particular, the anatomical intricacy and significant clinical implications of the submental lymph node flap model require precise identification to ensure an effective count. Emerging deep learning techniques have shown promising capabilities for automating such meticulous tasks, potentially enhancing diagnostic efficacy and accuracy. This paper proposed LymphoNet, a deep learning model for automated detection of submental lymph nodes in histopathological slides, aiming to enhance diagnostics and reduce labor. We compared LymphoNet's performance with other models. LymphoNet demonstrated the performance, accurately identifying lymph nodes with high precision and recall, and achieving a strong F1 score. It effectively identified lymph node regions, minimizing false positives, as evidenced by a low Mean Absolute Error with non-lymph node tissues. This accuracy is necessary for lymph node flap studies in lymphedema treatment, promising to accelerate histological analysis and support pathologists and anatomists. In conclusion, LymphoNet represents a significant advancement in histopathological examination, offering precise lymph node detection that could become an essential tool for surgical planning in lymphedema management, enhancing study efficiency and treatment outcomes.