Correlation analysis between facial feature-based traditional Chinese medicine inspection of spirit classification and Beck Depression Inventory score
10.1016/j.dcmed.2025.06.002
- VernacularTitle:基于面部特征的中医望神分类与贝克抑郁量表评分的相关性分析
- Author:
Shan LU
;
Xubo SHANG
;
Dong YANG
;
Junfeng YAN
;
Xiaoye WANG
- Publication Type:Journal Article
- Keywords:
Traditional Chinese medicine inspection of spirit classification;
Severity grade of depression;
Facial feature analysis;
ResNet landmark extraction;
Association rule mining;
Clinical intelligent diagnosis [DOI] 10.1016/j.dcmed.2025.06.002
- From:
Digital Chinese Medicine
2025;8(2):147-162
- CountryChina
- Language:English
-
Abstract:
[Objective] To determine the correlation between traditional Chinese medicine (TCM) inspection of spirit classification and the severity grade of depression based on facial features, offering insights for intelligent intergrated TCM and western medicine diagnosis of depression. [Methods] Using the Audio-Visual Emotion Challenge and Workshop (AVEC 2014) public dataset on depression, which conclude 150 interview videos, the samples were classified according to the TCM inspection of spirit classification: Deshen (得神, presence of spirit), Shaoshen (少神, insufficiency of spirit), and Shenluan (神乱, confusion of spirit). Meanwhile, based on Beck Depression Inventory-II (BDI-II) score for the severity grade of depression, the samples were divided into minimal (0 – 13, Q1), mild (14 – 19, Q2), moderate (20 – 28, Q3), and severe (29 – 63, Q4). Sixty-eight landmarks were extracted with a ResNet-50 network, and the feature extracion mode was stadardized. Random forest and support vectior machine (SVM) classifiers were used to predict TCM inspection of spirit classification and the severity grade of depression, respectively. A Chi-square test and Apriori association rule mining were then applied to quantify and explore the relationships. [Results] The analysis revealed a statistically significant and moderately strong association between TCM spirit classification and the severity grade of depression, as confirmed by a Chi-square test (χ2 = 14.04, P = 0.029) with a Cramer’s V effect size of 0.243. Further exploration using association rule mining identified the most compelling rule: “moderate depression (Q3) → Shenluan”. This rule demonstrated a support level of 5%, indicating this specific co-occurrence was present in 5% of the cohort. Crucially, it achieved a high Confidence of 86%, meaning that among patients diagnosed with Q3, 86% exhibited the Shenluan pattern according to TCM assessment. The substantial Lift of 2.37 signifies that the observed likelihood of Shenluan manifesting in Q3 patients is 2.37 times higher than would be expected by chance if these states were independent—compelling evidence of a highly non-random association. Consequently, Shenluan emerges as a distinct and core TCM diagnostic manifestation strongly linked to Q3, forming a clinically significant phenotype within this patient subgroup. [Conclusion] Automated facial analysis can serve as a common lens for TCM and western psychological assessments align in the diagnosis of depression. The inspection of spirit decline trajectory parallels worsening depression, supporting early screening and stratified intervention, and providing a reference for the intelligent assistance of integrated TCM and western medicine in the diagnosis of depression.
- Full text:20250713231445553612025-lushan.pdf
