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.

Syndrome differentiation and treatment is a traditional Chinese medicine （TCM）-featured concept and method in diagnosis and treatment， which needs to be combined with the intelligent diagnosis of TCM in the future. At present， the intelligent diagnosis of TCM has gradually changed from the simple data-driven primary intelligence to the knowledge-driven advanced intelligence that integrates professional knowledge. Although syndrome element differentiation breaks down the elements of TCM diagnostic knowledge to form syndrome elements， which makes the original vague knowledge information more specific， the relationship between syndrome elements is not well classified and sorted out， resulting in the lack of hierarchical relationship and connection between syndrome elements， and thereby causing obstacles to the application of intelligent technology. Based on the understanding of etiology and pathogenesis from TCM， this paper concretized the syndrome element set of etiology， disease location and disease nature in TCM syndrome differentiation and treatment， and formed a knowledge framework system of syndrome elements with clear logical relationship， clear hierarchy and good explanation， thereby providing certain reference for realizing knowledge-driven advanced intelligence syndrome differentiation in the future.

Image-based intelligent diagnosis represents a trending research direction in the field of tongue diagnosis in traditional Chinese medicine(TCM).In recent years,machine learning techniques,including convolutional neural networks(CNNs)and Transformers,have been widely used in the analysis of medical images,such as computed tomography(CT)and nucle-ar magnetic resonance imaging(MRI).These techniques have significantly enhanced the effi-ciency and accuracy of decision-making in TCM practices.Advanced artificial intelligence(AI)technologies have also provided new opportunities for the research and development of medical equipment and TCM tongue diagnosis,resulting in improved standardization and in-telligence of the tongue diagnostic procedures.Although traditional image analysis methods could transform tongue images into scientific and analyzable data,recognizing and analyzing images that capture complicated tongue features such as tooth-marked tongue,tongue spots and prickles,fissured tongue,variations in coating thickness,tongue texture(curdy and greasy),and tongue presence(peeled coating)continues posing significant challenges in con-temporary tongue diagnosis research.Therefore,the employment of machine learning tech-niques in the analysis of tongue shape and texture features as well as their applications in TCM diagnosis is the focus of this study.In the study,both traditional and deep learning im-age analysis techniques were summarized and analyzed to figure out their value in predicting disease risks by observing the tongue shapes and textures,aiming to open a new chapter for the development and application of AI in TCM tongue diagnosis research.In short,the com-bination of TCM tongue diagnosis and AI technologies,will not only enhance the scientific basis of tongue diagnosis but also improve its clinical applicability,thereby advancing the modernization of TCM diagnostic and therapeutic practices.

Objective To investigate the correlation between tongue and pulse indicators and the out-come of live birth in patients undergoing frozen-thawed embryo transfer(FET),as well as the association between these indicators and patients'endocrine parameters. Methods This study was conducted at Reproductive Medicine Center,Shuguang Hospital Af-filiated to Shanghai University of Traditional Chinese Medicine,Shanghai,China,from March 8,2021 to January 5,2022.Patients undergoing FET were divided into live birth and non-live birth groups according to their live birth outcome.The differences between the endocrine pa-rameters[basic follicle stimulating hormone(b FSH),basic luteinizing hormone(b LH),basic estradiol(b E2),basic progesterone(b P),basal endometrial thickness,follicle stimulating hormone(FSH)on endometrial transition day,luteinizing hormone(LH)on endometrial transition day,estradiol(E2)on endometrial transition day,progesterone(P)on endometrial transition day,and endometrial thickness on endometrial transition day]and the tongue and pulse indicators[tongue body(TB)-L,TB-a,TB-b,tongue coating(TC)-L,TC-a,TC-b,perAll,perPart,h1,h4,h5,t1,h1/t1,and h4/h1]of patients in the two groups were analyzed,with the correlation between these variables analyzed as well using Spearman's correlation coefficient.Multivariate logistic regression was employed to identify the influential factors in the live birth prediction models across various datasets,including Model 1 consisting of endocrine indica-tors only,Model 2 solely consisting of tongue and pulse indicators,and Model 3 consisting of both tongue,pulse,and endocrine indicators,as well as to evaluate efficacy of the models de-rived from different datasets. Results This study included 78 patients in live birth group and 144 patients in non-live birth group.Compared with non-live birth group,live birth group exhibited higher levels of TB-L(P=0.01)and TB-a(P=0.04),while demonstrated lower levels of b FSH(P=0.01),perAll(P=0.04),and h4/h1(P=0.03).The Spearman's correlation coefficient analysis revealed statisti-cally significant correlation(P<0.05)between TB-L,TB-b,TC-L,TC-b,perAll,perPart,h4,h5,t1,h1/t1 and b FSH,b LH,basal endometrial thickness,LH on endometrial transition day,E2 on endometrial transition day,P on endometrial transition day,and endometrial thickness on endometrial transition day in live birth group.The multivariate logistic regression analysis showed that the prediction Model 3 for live birth outcome[area under the curve(AUC):0.917,95%confidence interval(CI):0.863-0.971,P<0.001]surpassed the Model 1(AUC:0.698,95%CI:0.593-0.803,P=0.001),or the Model 2(AUC:0.790,95%CI:0.699-0.880,P<0.001).The regression equations for the live birth outcomes,integrating tongue and pulse indicators with endocrine parameters,included the following measures:FSH on endometrial transition day[odds ratio(OR):0.523,P=0.025],LH on endometrial transition day(OR:1.277,P=0.029),TB-L(OR:2.401,P=0.001),perPart(OR:1.018,P=0.013),h1(OR:0.065,P=0.021),t1(OR:4.354,P=0.024),and h4/h1(OR:0.018,P=0.016). Conclusion In infertility patients undergoing FET,there exists a correlation between tongue and pulse indicators and endocrine parameters.The corporation of tongue and pulse indica-tors significantly improved the predictive capability of the model for live birth outcomes.Specifically,tongue and pulse indicators such as TB-L,perPart,h1,t1,and h4/h1 exhibited a discernible correlation with the ultimate live birth outcomes.

Objective To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer. Methods From July 1,2020 to March 31,2022,clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hos-pital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Ex-amination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chi-nese Medicine,respectively.We obtained tongue images from patients with benign lung nod-ules and lung cancer using the TFDA-1 digital tongue diagnosis instrument,and analyzed these images with the TDAS V2.0 software.The extracted indicators included color space pa-rameters in the Lab system for both the tongue body(TB)and tongue coating(TC)(TB/TC-L,TB/TC-a,and TB/TC-b),textural parameters[TB/TC-contrast(CON),TB/TC-angular second moment(ASM),TB/TC-entropy(ENT),and TB/TC-MEAN],as well as TC parameters(perAll and perPart).The bivariate correlation of TB and TC features was analyzed using Pearson's or Spearman's correlation analysis,and the overall correlation was analyzed using canonical correlation analysis(CCA). Results Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values.Simple correlation analysis indi-cated that the correlation of TB-L with TC-L,TB-b with TC-b,and TB-b with perAll in lung cancer group was higher than that in benign nodules group.Moreover,the correlation of TB-a with TC-a,TB-a with perAll,and the texture parameters of the TB(TB-CON,TB-ASM,TB-ENT,and TB-MEAN)with the texture parameters of the TC(TC-CON,TC-ASM,TC-ENT,and TC-MEAN)in benign nodules group was higher than lung cancer group.CCA further demon-strated a strong correlation between the TB and TC parameters in lung cancer group,with the first and second pairs of typical variables in benign nodules and lung cancer groups indicat-ing correlation coefficients of 0.918 and 0.817(P<0.05),and 0.940 and 0.822(P<0.05),re-spectively. Conclusion Benign lung nodules and lung cancer patients exhibited differences in correla-tion in the L,a,and b values of the TB and TC,as well as the perAll value of the TC,and the texture parameters(TB/TC-CON,TB/TC-ASM,TB/TC-ENT,and TB/TC-MEAN)between the TB and TC.Additionally,there were differences in the overall correlation of the TB and TC be-tween the two groups.Objective tongue diagnosis indicators can effectively assist in the diag-nosis of benign lung nodules and lung cancer,thereby providing a scientific basis for the ear-ly detection,diagnosis,and treatment of lung cancer.

Objective To develop a Traditional Chinese Medicine(TCM)syndrome diagnosis scale suitable for evaluating the overall health status of participants in long-term simulated weightlessness experiments,and preliminarily evaluate its reliability and validity.Methods Based on the understanding of the pathogenesis of TCM in different stages of long-term manned spaceflight,a scale was developed through literature research and expert consultation.Test-retest reliability and internal consistency coefficient were used to test the reliability of the scale.Item analysis and factor principal component analysis were used to evaluate the validity of the scale,and a rotating component matrix was used to analyze the correlation between each item and dimension using 36 volunteers in a 90 days head down bed rest experiment as the research subjects.Results The scale contains eight dimensions and 57 items.The reliability analysis showed that the correlation coefficient of the total score of the scale for the first and second tests of 30 participants was 0.889,indicating high stability of the scale.The Cronbach coefficient of the scale was 0.934,indicating very good internal consistency.The split half reliability after correction was 0.858,indicating a high reliability of the scale.The validity analysis showed that the difference between the high score group and the low score one for each of the 57 items was statistically significant(P<0.01),and the Pearson correlation coefficient between each item and the total score of the scale was greater than 0.4(P<0.001).The cumulative contribution rate of 8 common factors extracted using principal component factor analysis was 55.293%,and the results of rotating component matrix analysis,after applying TCM theory and expert feedback,had a coincidence of up to 87.5%with the initial 8 dimensions,indicating good structural validity of the scale.Conclusion The scale has good reliability and validity,and can diagnose TCM syndromes.It is suitable for long-term simulated weightlessness experiment and provides an objective and quantitative new method for evaluating the subjective feelings of participants.

Methods: From September 3, 2018, to March 23, 2024, participants with essential hypertension (receiving antihypertensive medication treatment, hypertension group) and normal blood pressure (control group) were recruited from the Cardiology Department of Shanghai Hospital of Traditional Chinese Medicine, the Coronary Care Unit of Shanghai Tenth People's Hospital, the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, and the Gaohang Community Health Service Center. This study employed the propensity score matching (PSM) method to reduce study participants selection bias. Spectral information in the facial visible light spectrum of the subjects was collected using a flame spectrometer, and the spectral chromaticity values were calculated using the equal-interval wavelength method. The study analyzed the differences in spectral reflectance across various facial regions, including the entire face, forehead, glabella, nose, jaw, left and right zygomatic regions, left and right cheek regions as well as differences in parameters within the Lab color space between the two subject groups. Feature selection was conducted using least absolute shrinkage and selection operator (LASSO) regression, followed by the application of various machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and eXtreme Gradient Boosting (XGB). The reduced-dimensional dataset was split in a 7 : 3 ratio to establish a classification and assessment model for facial coloration related to primary hypertension. Additionally, model fusion techniques were applied to enhance the predictive power. The performance of the models was evaluated using metrics including the area under the curve (AUC) and accuracy. Shapley Additive exPlanations (SHAP) was used to interpret the outcomes of the models. Results: A total of 114 participants were included in both hypertension and control groups. Reflectance analysis across the entire face and eight predefined areas revealed that the hypertensive group exhibited significantly higher reflectance of corresponding color light in the blue-violet region (P < 0.05) and a lower reflectance in the red region (P < 0.05) compared with control group. Analysis of Lab color space parameters across the entire face and eight predefined areas showed that hypertensive group had significantly lower a and b values than control group (P < 0.05). LASSO regression analysis identified a total of 18 facial color features that were highly correlated with hypertension, including the a values of the chin and the right cheek, the reflectance at 380 nm and at 780 nm of the forehead. The results of the multi-model classification showed that the RF classification model was the most effective, with an AUC of 0.74 and an accuracy of 0.77. The combined model of RF + LR + SVM outperformed a single model in their classification performance, achieving an AUC of 0.80 and an accuracy of 0.76. SHAP model visualization results indicated that the top three contributors to ideal prediction results based on the characteristics from the facial spectrum were the reflectance at 380 nm across the entire face and of the nose as well as the a value of the chin. Conclusion Within the same age group, patients with essential hypertension exhibited significant and regular changes in facial color and facial spectral reflectance parameters after the administration of antihypertensive drugs. Furthermore, facial reflectance indicators, such as the overall reflectance at 380 nm and the a value of the chin, could offer valuable references for clinically assessing the drug efficacy and health status of patients with essential hypertension.