TCM Constitution Identification

Traditional diagnostic methods in Traditional Chinese Medicine (TCM)—the four diagnostic approaches—rely heavily on individual practitioner experience. Consistency in constitution assessment for the same patient across different practitioners is only 65–70%. Although constitution questionnaires such as the CCMQ provide a degree of standardization, they still suffer from significant subjectivity and limited quantification.

A complete traditional constitution identification process typically takes 30–45 minutes, making it difficult to meet the efficiency demands of modern healthcare. In remote and underserved regions, the shortage of qualified TCM practitioners leads to uneven service coverage.

In addition, many TCM diagnostic descriptors—such as “sallow complexion” or “thick, greasy tongue coating”—lack objective and quantifiable standards. This limitation constrains the modernization of TCM constitution theory and its international acceptance.

Multimodal Data Acquisition Framework
A standardized, multimodal data collection system is established, covering tongue imaging, facial imaging, voice signals, and infrared thermography. Unified device parameters and controlled acquisition environments ensure consistency and reliability of data quality.

AI Algorithm and Model Development
An intelligent tongue analysis system is developed with quantitative indicators for tongue color, coating color, and tongue morphology. For facial feature recognition, multi-channel feature extraction and cascaded regression–based keypoint localization methods are applied to achieve facial alignment and image normalization, providing a reliable foundation for feature quantification.

Multimodal Data Fusion Platform
Multi-source data—including tongue images, facial features, voice signals, and infrared thermography—are integrated to emulate the holistic TCM principle of “combined analysis of the four diagnostic methods.” Through both feature-level fusion and decision-level fusion, the platform significantly improves identification accuracy and robustness.