Enhancing vitiligo stage diagnosis through a reliable multimodal model with uncertainty calibration

Vitiligo is a common dermatological disease featuring hypopigmentation. Accurate staging of vitiligo is crucial for enhancing treatment efficacy. However, traditional diagnostic methods, which rely on physicians' subjective judgments, are time-consuming, labor-intensive, and prone to misdiagnosis. Recently, AI-powered multimodal dermatological classification models have demonstrated significant potential in this area. But the credibility of these models at the decision-making stage is an area that requires further refinement. This study proposes a multimodal disease staging diagnostic model with uncertainty calibration to analyze multimodal samples from three stages of vitiligo. The model innovatively extracts feature information from various modalities and transforms it into a Dirichlet distribution to assess sample uncertainty. Then, the Dempster—Shafer theory is used to fuse evidence from different modalities, generating a final diagnostic result and an uncertainty score. Additionally, an uncertainty—based loss function is designed. And by using an uncertainty threshold method, the model can detect high—uncertainty samples that require additional judgment, effectively reducing the risk of misdiagnosis and missed diagnosis. Experimental results show that this model outperforms existing methods in terms of accuracy, precision, recall, and F1—score. Anomaly detection and noise—resistance experiments verify the model's robustness in handling unknown and noisy data. This model offers a new approach for AI-assisted vitiligo diagnosis, which can assist doctors in making more accurate diagnostic decisions, contribute to improving treatment efficiency.