Biomedical image analysis of facial signs: comparative assessment and interpretation of artificial intelligence models for TCM constitutional phenotyping in a young adult cohort

The ancient TCM adage 'You zhu nei, bi xing zhu wai'—internal constitution manifests externally—has been a diagnostic pillar for millennia. However, translating this inherently subjective knowledge into exact, quantifiable measurements has proven difficult. This study aims to bridge this gap by developing an objective AI model for TCM constitutional phenotyping. We acknowledge that while expert consensus serves as the current clinical gold standard, it remains inherently subjective and theory-laden. Our AI model is positioned as an objective, quantifiable auxiliary tool to reduce diagnostic variability, rather than acting as a validator of TCM theory itself. To narrow this big gap between old idea and new data science, we have built and proved one clear to be seen AI model thanks to applying Biomedical Images into numbers about Chinese Herbal Plants. We work with a rigorously curated cohort of 1,157 subjects specifically limited to 18-24-year-old young adults due to the homogeneity of the sample and extract 21 different facial features from standardized images and evaluate four different machine learning models SVM, MLP, Random Forest, and XGBoost. To solve the widespread problem of class imbalances in the medical dataset, we used SMOTE (synthetic minority oversampling technique) on only the training set so as not to compromise the test data integrity. And the result is quite revealing, of all the model I've used right now, it was the XGBoost is the best model. This model can give me a accuracy for validation dataset is 76.72%. But it's not just in terms of performance metrics, we looked into what kind of errors. It turns out that these mistakes weren't errant computer problems, rather a reflection of the degree of relatedness “balanced” and “deficient” had biologically as confirmed by the t-SNE plot. Additionally, the SHAP interpretability analysis also showed that the models decision-making was in exact accordance with the clinicians experience, and identified biologically plausible cues like the zygomatic dullness being a good indication of Yang deficiency, and the saturation of the lips being a good indication of Yin deficiency. These results support that the digital approach that we've employed here picks up real pathophysiologic signals, giving us a good, data-informed lens to go back and revisit and adjust TCM diagnosis in the era of precision medecine. However, all conclusions are strictly limited to this homogeneous young adult cohort and require multi-center, cross-age validation for broader application.