Abstract Objective: This study aimed to develop a machine learning model for the diagnosis of chronic renal failure (CRF) with spleen–kidney qi deficiency (SKQD) syndrome by investigating facial chromatic biomarkers based on traditional Chinese medicine (TCM) theory. Materials and Methods: This cross-sectional study analyzed TCM-defined facial regions in 303 patients with CRF (166 SKQD and 137 non-SKQD) and 25 healthy controls using active appearance models and RGB/Lab color spaces. Key chromatic features were selected using Chi-square automatic interaction detection (CHAID) decision trees to construct a multibranch deep convolutional neural network (CNN), nomogram, and hybrid model. The model performances were compared using the DeLong test. Results: The SKQD group showed a higher glomerular filtration rate, albumin level, and facial R-value with a lower serum creatinine level (all P 0.05). Conclusions: Facial chromatic biomarkers effectively differentiated SKQD in patients with CRF when analyzed using TCM-defined regions. The superior performance of the CNN validates AI-driven TCM diagnostics as a clinically actionable tool for precise syndrome classification.
Yang et al. (Mon,) studied this question.