Colorectal cancer (CRC) poses a significant public health challenge in modern medicine, with treatment selection highly dependent on precise disease classification. While modern medical TNM staging and molecular typing are crucial, they struggle to comprehensively reflect patients' overall functional status and dynamic changes during disease progression. Traditional Chinese Medicine (TCM) syndrome differentiation emphasizes holistic concepts, offering a macro-functional perspective that complements modern classification systems and provides unique insights for personalized treatment. However, the reliance on practitioners' subjective experience—particularly in interpreting tongue and pulse patterns—presents inherent limitations such as poor reproducibility and difficulty in quantifying knowledge transfer, hindering its integration with modern precision medicine. The rise of artificial intelligence (AI) technology, particularly its exceptional capabilities in image recognition and signal processing, offers revolutionary solutions for the objective and quantitative analysis of tongue and pulse patterns. This enables systematic research into their association with colorectal cancer classification.

     AI (CNN) performs precise segmentation and feature extraction on standardized tongue images, converting subjective observations such as tongue body color, morphology, ecchymoses, and tongue coating color, thickness, and greasiness into objective data. Simultaneously, deep learning models conduct in-depth analysis of pulse wave signals collected by the pulse wave analyzer, quantifying characteristics including pulse position, pulse rate, pulse force, and pulse momentum to achieve automated pulse pattern classification.

    Research indicates that AI models can learn and recognize tongue-pulse pattern combinations specific to different syndromes. For instance, the syndrome of internal accumulation of blood stasis and toxins often corresponds to the quantitative characteristics of a dark purple tongue with ecchymoses and a (thready-tight pulse), while the syndrome of spleen-kidney deficiency typically presents with objective indicators such as a pale, swollen tongue with teeth marks and a (deep-thready-fine pulse). This AI-based objective classification method not only assists clinicians in rapid, standardized, and reproducible pattern differentiation while reducing human variability, but also provides precise, quantifiable decision support for formulating integrated Chinese and Western medicine treatment plans. Ultimately, it holds promise for improving patient prognosis and quality of life.

    Research on the objectification of tongue and pulse patterns through artificial intelligence has ushered in a paradigm shift from “experience-driven” to “data-driven” approaches in the pattern differentiation of colorectal cancer within Traditional Chinese Medicine (TCM), demonstrating significant practical value. This methodology effectively translates the macro-level manifestations of traditional TCM into micro-level data that is comprehensible and verifiable within modern medicine, serving as a vital bridge connecting Chinese and Western medical systems. Although current research still faces challenges such as data standardization, model generalization capabilities, and interpretability, continuous technological advancements and the deepening of large-scale, multi-center studies will undoubtedly pave new pathways for precision diagnosis and treatment of colorectal cancer. This will propel the integrated approach of Chinese and Western medicine in treating colorectal cancer to a new level.