Deep Learning–Based Classification of Early Occlusal Caries from QLF Fluorescence Images: A Conceptual Validation Study

作者：Eun-Ha Jung · 发表于：Korean Journal of Clinical Dental Hygiene · 年份：2026 · DOI：10.12972/kjcdh.2026.14.1.6

Objective: This study explored the preliminary feasibility of automatically classifying earlystage occlusal caries using deep learning applied to QLF fluorescence images. We examined whether the QS-Occlusal classification system could be implemented within an artificial intelligence framework by learning fluorescence-derived image patterns. Methods: QLF images were acquired from a single extracted tooth and classified into four QS-Occlusal stages (QS 0–3) according to fluorescence loss and red fluorescence characteristics. A total of 100 images were included; 83 were used for training and 17 for evaluation. A DenseNet-201 convolutional neural network pretrained on ImageNet was fine-tuned for four-class classification. Model performance was evaluated using confusion matrix analysis, accuracy, precision, recall, and F1-score. Results: Most samples were correctly classified in the confusion matrix. Agreement was highest for QS 0 and QS 3, while moderate misclassification was observed between QS 1 and QS 2, likely due to overlapping fluorescence patterns in early enamel lesions. Overall performance metrics indicate stable and reliable classification. Conclusions: Within the limitations of a single-tooth, proofof-concept dataset, this study demonstrates the feasibility of deep learning–based QS-Occlusal classification using QLF images. This approach has the potential to support standardized assessment of early occlusal caries and provides a foundation for further validation using ...