Modernizing Tongue Diagnosis: AI Integration With Traditional Chinese Medicine for Precise Health Evaluation

Abstract

The integration of traditional Chinese medicine (TCM) diagnostics with modern artificial intelligence (AI) techniques has emerged as a promising approach to enhance the objectivity and accuracy of disease assessment. Tongue diagnosis, a non-invasive and unique TCM practice, plays a critical role in evaluating health status but is often limited by the subjective judgment of practitioners. This study addresses these limitations by developing an intelligent tongue diagnosis system using the Cv-Swin Transformer architecture. The system processes a diverse dataset of 5,365 tongue images, classifying them into ten categories based on TCM diagnostic standards. Key findings indicate that the Cv-Swin Transformer model achieves an average accuracy of 87.37% in tongue image classification, demonstrating superior performance compared to traditional models. The system effectively captures complex tongue features related to various diseases, providing precise health assessments and personalized treatment recommendations. This research represents a significant advancement in integrating AI with TCM, offering a robust tool for objective diagnostics and supporting the modernization of traditional practices.

Topics & Concepts