AUTHOR=Li Kaiwei , Qiu Zehong , Li Jialing , Deng Feilin , Zou Kun , Xu Yihua , Huang Chen , Wang Ran , Yu Zhaoji , Chen Yuzhi , Zhang Yingxuan , Liu Zhuoliang , Chen Si , Su Zhenning , Liu Xiaojing , Wu Haiwang , Wu Xiaozhen , Yang Lilin , Huang Yanxi , Luo Songping , Zhou Wu , Gao Jie 
  
TITLE=Deep learning approach for objective differentiation of kidney deficiency syndrome in reproductive age females: a tongue-face fusion model
  
JOURNAL=Frontiers in Physiology
  
VOLUME=Volume 16 - 2025
  
YEAR=2025
  
URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1701545
  
DOI=10.3389/fphys.2025.1701545
  
ISSN=1664-042X
  
ABSTRACT=BackgroundKidney deficiency syndrome (KDS) is the predominant syndrome associated with gynecological reproductive system diseases in traditional Chinese medicine (TCM). However, the diagnostic method is influenced by the subjective experience of doctors, which leads to the ambiguity in differentiation of KDS and poor effect for corresponding treatment.ObjectiveTo explore an objective syndrome differentiation method for KDS in females of reproductive age through machine learning technique.MethodsWe proposed a new deep learning method for the objective differentiation of KDS in females of reproductive age. First, we simultaneously acquired 376 pairs of tongue and facial images. We divided them into a Kidney deficiency syndrome (KDS, n = 182) group and a Non-Kidney deficiency syndrome (NKDS, n = 194) group. Then, we employed two parallel DenseNet structures to extract deep features from tongue and facial images. We further used a deep supervised network strategy to better stabilize the fusion of the two deep features. We used 5-fold cross-validation to evaluate the performance by six indicators: accuracy, precision, recall, F1 score, receiver operating characteristic (ROC) and area under the curve (AUC). Finally, external validation was conducted on an independent test set consisting of 130 patients with a 1:1 ratio of KDS to NKDS cases.ResultsThe model based on tongue images, facial images, and the tongue-face fusion model achieved AUCs of 71.45% ± 6.39%, 89.60% ± 3.33%, and 92.08% ± 4.51%, respectively, with the highest value observed in the fusion model. In external validation, the tongue-face fusion model attained an AUC of 83.53%.ConclusionThe deep learning network model with tongue-face fusion can effectively differentiate KDS.