ObjectiveTo explore the distribution pattern of tongue image characteristics in patients with glucolipid metabolic disorders and its main syndromes. MethodsA total of 841 patients with glucolipid metabolic disorders （disease group）， and 380 healthy subjects （control group） were included. The disease group was classified into three syndrome types： 283 cases of liver depression and spleen deficiency syndrome， 311 cases of phlegm-dampness obstruction syndrome， and 247 cases of qi stagnation and blood stasis syndrome. Tongue image data were collected using the TFDA-1 Tongue Diagnosis Instrument， and the TDAS V3.0 software was used to analyze the color， texture， and morphological features of the tongue body （TB） and tongue coating （TC） in patents with different syndromes of disease group （including lightness （L）， red-green axis （a）， yellow-blue axis （b）， luminance （Y）， difference between red signal and brightness （Cr）， difference between blue signal and brightness （Cb）， contrast （CON）， angular second moment （ASM）， entropy （ENT）， mean value （MEAN）， tongue coating area/tongue surface area （perAll）， and tongue coating area/non-coated area （perPart））. Logistic regression analysis was conducted to identify influencing factors for different syndrome types of glucolipid metabolic disorders. ResultsThe tongue body indicators TB-L， TB-Y， and TB-Cb in the disease group were significantly higher than those in the control group， while TB-a， TB-b， and TB-Cr were significantly lower. The tongue coating indicators TC-L， TC-Y， TC-Cb， perAll， and perPart in the disease group were significantly higher than those in the control group， while TC-a， TC-b， and TC-Cr were significantly lower （P＜0.05）. Comparing with the different syndromes in disease group， the TB-L and TB-Y of the liver depression and spleen deficiency syndrome， and the phlegm-damp obstruction syndrome were higher than those of the qi stagnation and blood stasis syndrome； the TB-a and TB-Cr of the phlegm-damp obstruction syndrome were lower than those of the qi stagnation and blood stasis syndrome； the perAll of the phlegm-damp obstruction syndrome was higher than that of the qi stagnation and blood stasis syndrome （P＜0.05）. In the analysis of the morphological characteristics of tongue signs， more spotted tongue in disease group compared with control group， more teeth-marked tongue in liver depression and spleen deficiency syndrome than the other two syndromes， more greasy coating in phlegm-damp obstruction syndrome， and more stasis spots of tongue in qi stagnation and blood stasis syndrome （P＜0.05）. Logistic regression analysis identified that greasy coating， spotted tongue， stasis spots of tongue， tooth-marked tongue， perAll， and TB-Cb are the influencing factors of liver depression and spleen deficiency syndrome； greasy coating， tooth-marked tongue， TC-Cb， and TC-Cr are the influencing factors of phlegm-damp obstruction syndrome； cracked tongue， stasis spots of tongue， tooth-marked tongue， and TB-Y are the influencing factors of qi stagnation and blood stasis syndrome （P＜0.05）. ConclusionCompared to healthy individuals， patients with glycolipid metabolic disorder have darker tongue color and thicker， greasy tongue coating. Glycolipid metabolic disorder patients of liver depression and spleen deficiency syndrome exhibit a reddish tongue with finer textures and more tooth marks； patients of phlegm-damp obstruction syndrome have lighter tongue coating with a coarser texture and a higher prevalence of greasy coating； patients of qi stagnation and blood stasis syndrome display lower tongue brightness with a higher prevalence of blood stasis spots.

Abstract Traditional Chinese medicine (TCM) auscultation has a long history, and with advancements in equipment and analytical methods, the quantitative analysis of auscultation parameters has determined. However, the complexity and diversity of auscultation, along with variations in devices, analytical methods, and applications, bring challenges to its standardization and deeper application. This review presents the advancements in auscultation equipment and systems, auscultation characteristic parameters, and their application in the diagnosis of pulmonary diseases and syndromes over the past 10 years, while also exploring the progress and challenges of current digital research of auscultation. This review also proposes the establishment of standardized protocols for the collection and analysis of auscultation data, the incorporation of advanced artificial intelligence (AI) auscultation analysis methods, and an exploration of the diagnostic utility of auscultatory features in pulmonary diseases and syndromes, so as to provide more precise decision support for intelligent diagnosis of pulmonary diseases and syndromes

Objective: To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data. Methods: Clinical indicators, echocardiographic data, traditional Chinese medicine (TCM) tongue manifestations, and facial features were collected from patients who underwent coronary computed tomography angiography (CTA) in the Cardiac Care Unit (CCU) of Shanghai Tenth People's Hospital between May 1, 2023 and May 1, 2024. An adaptive weighted multi-modal data fusion (AWMDF) model based on deep learning was constructed to predict the severity of coronary artery stenosis. The model was evaluated using metrics including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic (ROC) curve (AUC). Further performance assessment was conducted through comparisons with six ensemble machine learning methods, data ablation, model component ablation, and various decision-level fusion strategies. Results: A total of 158 patients were included in the study. The AWMDF model achieved excellent predictive performance (AUC = 0.973, accuracy = 0.937, precision = 0.937, recall = 0.929, and F1 score = 0.933). Compared with model ablation, data ablation experiments, and various traditional machine learning models, the AWMDF model demonstrated superior performance. Moreover, the adaptive weighting strategy outperformed alternative approaches, including simple weighting, averaging, voting, and fixed-weight schemes. Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.

Objective: To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features, providing novel insights into the early screening of lung cancer. Methods: This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1, 2019 to December 31, 2024, as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period. The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument, and the facial diagnosis features were extracted from it by deep learning technology. Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics, and the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables. Based on the screened feature variables, four machine learning methods: random forest, logistic regression, support vector machine (SVM), and gradient boosting decision tree (GBDT) were used to establish lung cancer classification models independently. Meanwhile, the model performance was evaluated by indicators such as sensitivity, specificity, F1 score, precision, accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), and the area under the precision-recall curve (AP). Results: A total of 1 275 patients with pulmonary nodules and 1 623 patients with lung cancer were included in this study. After propensity score matching (PSM) to adjust for gender and age, 535 patients were finally included in the pulmonary nodule group and the lung cancer group, respectively. There were significant differences in multiple color space metrics (such as R, G, B, V, L, a, b, Cr, H, Y, and Cb) and texture metrics [such as gray-levcl co-occurrence matrix (GLCM)-contrast (CON) and GLCM-inverse different moment (IDM)] between the two groups of individuals with pulmonary nodules and lung cancer (P < 0.05). To construct a classification model, LASSO regression was used to select 63 key features from the initial 136 facial features. Based on this feature set, the SVM model demonstrated the best performance after 10-fold stratified cross-validation. The model achieved an average AUC of 0.8729 and average accuracy of 0.799 0 on the internal test set. Further validation on an independent test set confirmed the model’s robust performance (AUC = 0.823 3, accuracy = 0.729 0), indicating its good generalization ability. Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components (including color-B-0, wholecolor-Cr, and lipcolor-Cr) were the core factors in the model’s classification decisions, while texture indicators [GLCM-angular second moment (ASM)_2, GLCM-IDM_1, GLCM-CON_1, GLCM-entropy (ENT)_2] played an important auxiliary role. Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas. The various models constructed based on facial image features all demonstrate good performance, indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction, providing a non-invasive and feasible new approach for early lung cancer screening.

Objective To explore the objective indicators of tongue and pulse manifestations in patients with endometriosis diseases by collecting the data of tongue and pulse manifestations of patients with endometriosis diseases by digital tongue and pulse diagnostic instrument. Methods The endometriosis disease group included 72 patients with endometriosis diseases who were treated in Department of Gynecology (Professor ZHANG Tingting),Yueyang Hospital of Integrated Traditional Chinese and Western Medicine from Jun. 1,2021 to Sep. 15,2022. The normal group included 35 healthy adult women recruited by the Physical Examination Center of Shuguang Hospital,Shanghai University of Traditional Chinese Medicine. Results In terms of age,there was no significant difference between the endometriotic disease group and normal group (P>0.05). In terms of tongue color,the values of zhiCon,zhiASM,zhiENT,zhiMEAN,zhiClrB,zhiClrI,zhiClrLa,zhiClrCb,and zhiClrLb in the endometriosis disease group were significantly different from those in the normal group (all P<0.05),while there were no significant differences in the values of zhiClrR,zhiClrG,zhiClrS,zhiClrL,zhiClrY,or zhiClrCr between the 2 groups (all P>0.05). In terms of tongue coating color,the values of taiClrR,taiClrB,taiClrI,taiClrS,taiClrLa,taiClrLb,taiClrCr,and taiClrCb in the endometriosis disease group were significantly different from those in the normal group (all P<0.05),while there were no significant differences in the values of taiClrG,taiClrL,or taiClrY (all P>0.05). In terms of texture and thickness of tongue coating,there were no significant differences in the values of perAll,perPart,taiCon,taiASM,taiENT,or taiMEAN between the 2 groups (all P>0.05). In terms of pulse manifestation,the values of h3,h4,h5,t4,h3/h1,and h4/h1 in the endometriosis disease group were significantly different from those in the normal group (all P<0.05),while there were no significant differences in the values of h1,t,t1,or t5 (all P>0.05). Conclusion There are significant differences in tongue and pulse indicators between patients with endometriosis diseases and healthy individuals. It can provide objective indicators for further research on the pathogenesis changes and clinical differentiation of endometriosis diseases.

The modernization research of TCM tongue diagnosis involves a complex process of acquiring tongue diagnosis images of diseases and syndromes through machine vision and image processing technology,and conducting intelligent diagnosis and analysis.The development of artificial intelligence technology has brought new opportunities for the intelligent research of tongue diagnosis images in TCM.This article systematically reviewed the current status of the development and standard application of TCM tongue diagnosis instruments in the past decade,discussed the latest research progress in deep learning methods including tongue image quality assessment,tongue image segmentation,tongue color classification,tongue image pattern recognition,three-dimensional tongue image reconstruction,and clinical applications.It deeply analyzed the bottleneck problems existing in the current artificial intelligence tongue diagnosis technology,proposed to focus on clinical disease-syndrome combined applications,multi-level expansion,cross-modal fusion to enhance the depth and breadth of tongue diagnosis features,utilize general artificial intelligence methods to enhance the intelligence diagnosis technology of tongue images,innovate and construct a new model of intelligent tongue diagnosis with full-domain information perception,health assessment,and disease-syndrome diagnosis,in order to promote the leapfrog development of intelligent TCM diagnosis and treatment technology.

Objective To analyze the objective tongue diagnosis data characteristics of benign and malignant pulmonary nodules and to establish a lung cancer risk warning model based on the logistic regression method.Methods From July 2020 to March 2022,263 lung cancer patients(lung cancer group)from the Oncology Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,292 benign pulmonary nodules patients(benign pulmonary nodules group)from the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,and 307 healthy individuals(healthy control group)were selected.TFDA-1 digital tongue diagnostic instrument was used to collect tongue images.Objective diagnostic features of the tongue were obtained through feature extraction technology.The distribution characteristics of the tongue indicators of the three groups of subjects were analyzed.A lung cancer warning model was established based on logistic regression method after feature screening,and the performance of the model was evaluated using sensitivity,specificity,accuracy,and AUC.Results The tongue features of patients in benign pulmonary nodules group were similar to those of the healthy control group,while the tongue features of the lung cancer group differed greatly from those of the healthy control group and benign pulmonary nodules group.The tongue features of lung cancer patients were dark and opaque,the tongue body was reddish,and the tongue coating is thin and yellowish with a greasy texture.The accuracy,sensitivity,specificity and AUC of the lung cancer warning model based on tongue image data were 70.09%,69.94%,70.29%and 0.769,respectively.After adding baseline information to the tongue image data set,the models'performance was improved.The accuracy,sensitivity,specificity and AUC of the new model based on tongue and baseline were 77.30%,75.94%,79.15%and 0.812,respectively.Conclusion The statistical characteristics of objective tongue image data between benign pulmonary nodules and lung cancer patients show significant differences.The lung cancer classification model based on objective tongue data performs well,and the objective tongue diagnosis data in TCM can provide reference for the differential diagnosis of benign pulmonary nodules and lung cancer.

Interpreting normal or diseased facial color accurately is the prerequisite for the study of facial color diagnosis technology.However,due to the variety of facial color in clinical practice and physicians may have different understanding as well as interpretation methods of the"five colors",the total rate of facial color interpretation consistency is lower than expected.The application of TCM diagnosis and treatment technology under the background of artificial intelligence puts forward higher requirements for the accuracy of facial color interpretation.This article elucidated the internal logical relationship of"the color of normal status","the color of life"and"the color of death"in Su Wen·Wu Zang Sheng Cheng Pian,explored the facial color interpretation standards,simulated the visual characteristics of"five colors"and designed the standard process of facial color interpretation.Then,the article discussed the limitations of the facial color theory in TCM,and put forward the point of view that the only way for clinical application of facial color diagnosis technology is to establish the expert consensus or guidelines that meet the needs of modern clinical diagnosis and treatment.

Objective To investigate the characteristics and rules of tongue image in patients with chronic fatigue syndrome(CFS)with different fatigue degree.Methods Totally 917 patients with severe chronic fatigue syndrome(severe CFS group),351 patients with mild chronic fatigue syndrome(mild CFS group)and 1216 healthy controls(healthy control group)were enrolled in the physical examination center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine.The tongue image data of subjects in the three groups were collected using TFDA-1 digital tongue and face diagnostic instrument,and the color space indexes of RGB,HSI,Lab and YCrCb were used to analyze the tongue image differences of CFS people with different fatigue degrees and the tongue image features of CFS patients with liver-qi stagnation syndrome,damp-heat stasis syndrome and spleen deficiency syndrome.Results Compared with the healthy control group,the tongue image indexes TB-R,TB-G,TB-B,TB-I,TB-L,TB-Y,TC-H,TC-I,TC-L and TC-Y increased in the severe CFS group;TB-S,TB-a,TC-S,TC-a,TC-Cr decreased(P<0.05).TB-R,TB-G,TB-B,TB-I,TB-L,TB-Y,TC-R,TC-G,TC-B,TC-I,TC-L and TC-Y increased in severe CFS group compared with mild CFS group.TB-H and TB-b increased in mild CFS group compared with healthy control group.The comparison of syndromes in severe CFS group showed that TB-a,TB-Cr,TC-S,TC-a,TC-Cr and TB-S increased in liver-qi stagnation syndrome compared to damp-heat stasis syndrome;TB-G,TB-B,TB-I,TB-L,TB-Y,TB-b,TB-Cb,TC-G,TC-B,TC-H,TC-I,TC-L,TC-Y and perAll decreased(P<0.05).Compared with spleen deficiency syndrome,TB-a,TB-Cr,TB-CON,TB-ENT,TB-MEAN,TC-a,TC-Cr,TC-CON,TC-ENT,TC-MEAN increased in liver-qi stagnation syndrome;TB-ASM,TC-S and TC-ASM decreased(P<0.05).Compared with spleen deficiency syndrome,TB-a,TB-b,TB-Cr,TB-Cb,TB-CON,TB-ENT,TB-MEAN,TC-G,TC-B,TC-H,TC-I,TC-L,TC-a,TC-Y,TC-Cr,TC-CON,TC-ENT,TC-MEAN,perAll increased;TB-ASM,TC-S and TC-ASM decreased(P<0.05).The comparison of mild CFS syndrome showed that there was no statistical significance between liver-qi stagnation syndrome and spleen deficiency syndrome(P>0.05).TB-Cr,TC-a,TC-Cr and perAll increased and TC-S decreased in damp-heat stasis syndrome compared with spleen deficiency syndrome(P<0.05).TB-S,TB-a,TB-Cr,TC-S,TC-a,TC-Cr increased,and TB-G,TB-B,TB-I,TB-Cb,TB-b,TC-b and TC-Cb decreased(P<0.05)in liver-qi stagnation syndrome compared with damp-heat syndrome.The distribution trend of TC-S was as follows:dampness-heat syndrome

Objective To design and construct a syndrome differentiation knowledge graph that integrates TCM facial color diagnosis information;To explore the hidden relationships between the knowledge.Methods The literature data came from ancient classics,textbooks,as well as standard literature related to inspection included in the CNKI,VIP and Wanfang Data from the establishment of the databases to December 31,2022.The clinical data was sourced from 30 elderly individuals who underwent TCM health examinations at the Community Health Service Center in Jiading Industrial Zone,Shanghai in September 2022.Facial image acquisition was performed using TFDA-1 digital tongue and facial diagnostic instrument.By following the steps of knowledge extraction,knowledge fusion and quality assessment to construct a graph,and with the assistance of TCM experts for interpretation,using Access 2019 to integrate qualitative textual data and quantitative objective image digital information,a syndrome differentiation knowledge graph integrating TCM facial diagnosis was designed and completed in the Neo4j graph database.In addition,a method was designed to shift facial diagnosis knowledge from qualitative to quantitative.Results There were a total of 194 nodes under 8 entity term types and 12 entity term labels,as well as 361 relationships under 13 semantic relationships in knowledge graph.The Neo4j graph database provided a visualized TCM facial color diagnosis and differentiation,which could be queried and fed back using Cypher language.Conclusion The knowledge graph constructed based on the theory of TCM facial color diagnosis visually shows the complex correlation between facial color diagnosis and syndrome differentiation diagnosis,with a knowledge representation model that forms qualitative data of image features → semantic relationships → syndrome differentiation diagnosis forms.