Explainable Multimodal Deep Learning Integrating Imaging and Clinical Data for Oral Potentially Malignant Disorder Detection

The paper presents M2-OPMDNet, a multimodal deep learning framework that combines white‑light and autofluorescence intraoral images with structured clinical data to detect oral potentially malignant disorders (OPMDs). Using a prospectively collected real‑world dataset, the model achieved an AUC of 0.952, outperforming unimodal approaches and improving detection of visually subtle lesions. Explainability was provided through SHAP analysis, showing that clinical variables significantly contributed to risk estimation alongside imaging features.

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• multimodal
• safety