Dual-scale fusion vision transformer model for vocal cord leukoplakia risk stratification: A multicenter study.

OBJECTIVE

This study aimed to develop an artificial intelligence (AI) system for accurate three-tier risk stratification of vocal cord leukoplakia (VCL), with particular focus on distinguishing high-risk lesions (severe dysplasia and carcinoma in situ) from low-risk and malignant lesions, while enhancing diagnostic performance among clinicians with limited experience.

METHODS

This retrospective multicenter study analyzed 8510 laryngoscopic images from 743 patients across three tertiary hospitals in China. A dual-scale Vision Transformer (ViT) architecture was constructed, integrating multi-scale feature analysis with cross-attention fusion mechanisms. The model was rigorously evaluated through internal-external validation and a prospective reader study involving 12 endoscopists of varying expertise.

RESULTS

The AI system demonstrated superior performance in three-tier classification, achieving F1-scores of 0.883 (95% CI: 0.869-0.896) and 0.861 (95% CI: 0.844-0.878) for high-risk lesion identification in internal and external validation, respectively. AI assistance significantly improved junior endoscopists' sensitivity from 67.1% to 81.4% (P < 0.001), effectively narrowing diagnostic performance disparities with senior experts. The system maintained robust generalizability across institutions, with 90.5% accuracy under heterogeneous imaging protocols.

CONCLUSION

The proposed AI framework provides a clinically effective solution for reliable VCL risk stratification and reduces diagnostic variability between clinicians. Validated across multiple centers, this dual-scale ViT approach establishes a novel paradigm for laryngoscopic diagnosis and holds significant potential to standardize diagnostic workflows in resource-limited settings.