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.

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

The knowledge graph has the characteristic of visualizing structural knowledge and has gradually become a research hotspot in the field of TCM diagnosis and treatment.This article reviewed the development trend and construction techniques of knowledge graph,summarized its application status in the field of TCM diagnosis and treatment,and provided a comprehensive review from the aspects of basic knowledge of TCM,inheritance of experience of renowned doctors,TCM question answering systems,and exploration of auxiliary decision-making.It also summarized the problems of knowledge graph technology in solving the informationization and intelligence process of TCM,and discussed and prospect the research and application directions of knowledge graph in the field of informationization and intelligence of TCM based on the characteristics of TCM knowledge.

Objective To study the tongue manifestation of patients with different syndromes in non-small cell lung cancer(NSCLC)and the correlation between tongue characteristics of different syndromes and tumor markers and coagulation indicators.Methods Totally 497 patients with NSCLC were grouped according to syndrome differentiation,and the differences in tongue characteristics of different syndromes were compared.Bivariate correlation analysis was used to study the correlation between tongue characteristics and serum tumor markers and coagulation indicators in patients with NSCLC of different syndromes.Results Compared with healthy people of different syndromes,in TB-a,yin deficiency and phlegm-heat syndrome>healthy group>qi-yin deficiency syndrome>spleen deficiency and phlegm-dampness syndrome>lung stagnation and phlegm-stasis syndrome(P<0.001).In TB-L,healthy group>spleen deficiency and phlegm-dampness syndrome>qi-yin deficiency syndrome>lung stagnation and phlegm-stasis syndrome>yin deficiency and phlegm-heat syndrome(P<0.001).In TB-b,yin deficiency and phlegm-heat syndrome>qi-yin deficiency syndrome>spleen deficiency and phlegm-dampness syndrome>healthy group>lung stagnation and phlegm-stasis syndrome(P<0.001).Yin deficiency and phlegm-heat syndrome had the highest TB-a and the lowest Per-all.Spleen deficiency and phlegm-dampness syndrome had the highest TB-L and Per-all.Lung stagnation and phlegm-stasis syndrome had lower TB-b and TC-b than other groups,lower TB-a than the healthy group,and a high Per-all index(P<0.05).In terms of tumor markers,Per-all in spleen deficiency and phlegm-dampness syndrome was positively correlated with Ca199,Ca50 and Ca242(P<0.05).In terms of coagulation indicators,the tongue texture index of lung stagnation and phlegm-stasis syndrome had a high correlation with the coagulation indicator Fg(P<0.01).Conclusion Different TCM syndromes of NSCLC have their own typical tongue characteristics.Tongue manifestations of different syndromes are correlated with tumor markers and coagulation indicators,respectively,which can reflect changes in clinical status.

Objective To investigate the tongue image characteristics of patients in postoperative colorectal cancer and significant tongue image features using a multinomial Logistic regression model.Methods Totally 213 postoperative patients with colorectal cancer were included.TFDA-1 digital tongue diagnostic instrument was used to collect the tongue images of patients,and the statistical differences of tongue image characteristics of patients with qi deficiency syndrome,yin deficiency syndrome,phlegm dampness syndrome and blood stasis syndrome were analyzed.Multiple Logistic regression was used to analyze the different tongue image indexes of different syndrome types.Results In the tongue coating index,perAll had the following distribution order:yin deficiency syndromeqi deficiency syndrome>blood stasis syndrome.In the texture indexes,CON,ENT and MEAN had the following distribution order:phlegm-dampness syndrome>yin deficiency syndrome>qi deficiency syndrome>blood stasis syndrome.ASM had the following distribution order:phlegm-dampness syndromeqi deficiency syndrome>blood stasis syndrome.TB-a had the following distribution order:phlegm-dampness syndromeqi deficiency syndrome>phlegm-dampness syndrome>blood stasis syndrome.TC-L had the following distribution order:blood stasis syndromeqi deficiency syndrome>blood stasis syndrome>phlegm-dampness syndrome.TC-b had the following distribution order:yin deficiency syndrome>qi deficiency syndrome>blood stasis syndrome.In the multinomial Logistic regression model,compared with the qi deficiency syndrome,the probability of being diagnosed with the yin deficiency syndrome increased when TB-L and TC-a decreased.Additionally,the probability of being diagnosed with the phlegm-dampness syndrome increased when TB-b and TC-a decreased and TC-b increased;TB-b and TC-L decreased,principal component 1 of texture index increased,and the probability of syndrome differentiation as blood stasis syndrome was higher.Conclusion The tongue image characteristics of different TCM syndromes in postoperative colorectal cancer patients exhibit certain patterns,which can serve as an objective reference for TCM differentiation and diagnosis.

Objective To analyze the characteristics of facial and tongue chromaticity parameters in patients with gastrointestinal tumors by setting the inspection image characteristics of patients with gastrointestinal tumors as the main research content;To establish an auxiliary diagnostic model for gastrointestinal tumors.Methods One-way ANOVA,t-test,Mann-whitney U test,canonical correlation analysis and Spearman statistical methods were used to analyze the characteristics of inspection image indexes and correlation of tumor markers of the 391 cases in the control group and 359 patients with gastrointestinal tumors.Machine learning methods such as SVM,Random Forest,KNN,Naive Bayes,XG Boost and Ada Boost were used to establish an auxiliary diagnostic model for gastrointestinal tumors.Results In terms of facial indicators,there were differences in F-R,F-G and F-B indicators among the control group,early-stage gastrointestinal cancer patients,and mid-to late-stage gastrointestinal cancer patients,in the comparison of tongue features among,TC-L,TB-L and TB-a of the control group,patients with early gastrointestinal tumors,and patients with intermediate and advanced gastrointestinal tumors showed a gradual downward trend;the AUC of the auxiliary diagnosis model of gastrointestinal tumor disease based on the chromaticity parameters of face tongue image constructed by Ada Boost algorithm was 0.930.Conclusion The auxiliary diagnostic model of gastrointestinal diseases constructed by facial and tongue images has good diagnostic effect,which can provide objective data support for in-depth exploration of the complex relationship between diagnosis and disease.

As a common syndrome type in TCM,blood stasis syndrome is diagnosed based on the four diagnostic methods of TCM,with inspection providing especially intuitive information.In recent years,with the advancement of objectification studies on the four diagnostic methods,inspection techniques for blood stasis syndrome have gradually transitioned from qualitative to quantitative analysis.This article reviewed recent progress in modern research on multi-region inspection for blood stasis syndrome,including facial complexion,tongue characteristics,sublingual collateral vessels,and microcirculation signs.Multi-region inspection technology has progressively established standardized acquisition protocols integrated with artificial intelligence technology,achieving a transition from qualitative to quantitative analysis.These region-specific data demonstrate clear associations with cardiovascular and metabolic diseases,supporting diagnostic objectification of blood stasis syndrome.However,further efforts remain necessary to expand clinical samples,integrate macro-micro data,standardize quantitative criteria,and establish collaborative diagnostic criteria for precise syndrome differentiation.

Objective To analyze the characteristics of facial and tongue chromaticity parameters in patients with gastrointestinal tumors by setting the inspection image characteristics of patients with gastrointestinal tumors as the main research content;To establish an auxiliary diagnostic model for gastrointestinal tumors.Methods One-way ANOVA,t-test,Mann-whitney U test,canonical correlation analysis and Spearman statistical methods were used to analyze the characteristics of inspection image indexes and correlation of tumor markers of the 391 cases in the control group and 359 patients with gastrointestinal tumors.Machine learning methods such as SVM,Random Forest,KNN,Naive Bayes,XG Boost and Ada Boost were used to establish an auxiliary diagnostic model for gastrointestinal tumors.Results In terms of facial indicators,there were differences in F-R,F-G and F-B indicators among the control group,early-stage gastrointestinal cancer patients,and mid-to late-stage gastrointestinal cancer patients,in the comparison of tongue features among,TC-L,TB-L and TB-a of the control group,patients with early gastrointestinal tumors,and patients with intermediate and advanced gastrointestinal tumors showed a gradual downward trend;the AUC of the auxiliary diagnosis model of gastrointestinal tumor disease based on the chromaticity parameters of face tongue image constructed by Ada Boost algorithm was 0.930.Conclusion The auxiliary diagnostic model of gastrointestinal diseases constructed by facial and tongue images has good diagnostic effect,which can provide objective data support for in-depth exploration of the complex relationship between diagnosis and disease.