数字中医药（英文）2026，Vol.9Issue(3)：387-396,10.DOI:10.1016/j.dcmed.2026.08.006
基于舌象高光谱技术构建慢性胃炎患者脾胃湿热证诊断模型
Developing a diagnostic model for spleen-stomach damp-heat syndrome in chronic gastritis patients using hyperspectral tongue imaging
摘要
Abstract
Objective To construct a diagnostic model for spleen-stomach damp-heat syndrome in pa-tients with chronic gastritis(CG)based on hyperspectral tongue imaging data,and to investi-gate the diagnostic value of hyperspectral tongue imaging in identifying spleen-stomach damp-heat syndrome in CG patients. Methods Using the prototype V3.0 image-spectral data collection device,intelligent tongue surface observations were conducted on CG patients at The Mingyi Hall of The Second Affili-ated Hospital of Anhui University of Chinese Medicine between September 4,2024 and Octo-ber 29,2025.Patients were categorized into spleen-stomach damp-heat syndrome and non-spleen-stomach damp-heat syndrome groups,and randomly assigned to a training set and an internal validation set at a ratio of 7:3.Following preprocessing and segmentation of the tongue images,wavelength-specific spectral features were extracted.The least absolute shrinkage and selection operator(LASSO)algorithm was applied for dimensionality reduc-tion and feature selection of hyperspectral tongue features,and a diagnostic model was con-structed based on the selected features.Pearson correlation analysis was performed to assess the correlation between the diagnostic model and clinical data,including tongue and pulse manifestations,symptoms,gastroscopic findings,and lifestyle habits.The diagnostic perfor-mance of the model was evaluated using the area under the receiver operating characteristic(ROC)curve(AUC),sensitivity,specificity,accuracy,positive predictive value(PPV),and neg-ative predictive value(NPV).Five-fold cross-validation was performed on the internal valida-tion set to calculate the mean AUC.Bootstrap calibration curves and decision curve analysis(DCA)based on 500 resamples were performed to evaluate model calibration and clinical net benefit. Results A total of 383 CG patients were enrolled and classified into the spleen-stomach damp-heat syndrome group(n=219)and the non-spleen-stomach damp-heat syndrome group(n=164).The LASSO algorithm identified nine hyperspectral tongue features for inclu-sion in the diagnostic model.The diagnostic model was significantly correlated with 13 clini-cal variables(P<0.05).It was positively correlated with yellow greasy coating,and negatively correlated with thin white coating,yellow coating,and scanty or absent coating(P<0.001).It was positively correlated with slippery and rapid pulse,and negatively correlated with thready and rapid pulse and wiry pulse(P<0.001).Diagnostic model was positively correlated with male sex,alcohol consumption,smoking,chronic atrophic gastritis(CAG)diagnosed by gas-troscopy,heaviness of the body,and sticky stools(P<0.05,P<0.01,or P<0.001).Model vali-dation yielded an AUC of 0.777,an accuracy of 0.704 3,a sensitivity of 0.741 9,a specificity of 0.660 4,a PPV of 0.718 8,and an NPV of 0.686 3 in the internal validation set.Five-fold cross-validation produced a mean AUC of 0.764.The Hosmer-Lemeshow goodness-of-fit test indi-cated that χ2=5.423 8 and P=0.711 5.Bootstrap DCA based on 500 resamples demonstrated,within the high-risk threshold range of 0.16-0.80,the model provided greater net benefit than either full therapy or no treatment. Conclusion This study developed a diagnostic model for spleen-stomach damp-heat syn-drome among CG patients based on tongue hyperspectral data which showed promising pre-dictive performance,and the model was correlated with the traditional Chinese medicine(TCM)characteristics of spleen-stomach damp-heat syndrome,providing new evidence for the objective diagnosis of this syndrome.关键词
慢性胃炎/脾胃湿热证/舌象/高光谱/诊断模型Key words
Chronic gastritis/Spleen-stomach damp-heat syndrome/Tongue image/Hyperspectral/Diagnostic model引用本文复制引用
姜楠,徐慧,程斌,赵建根,董昌武..基于舌象高光谱技术构建慢性胃炎患者脾胃湿热证诊断模型[J].数字中医药（英文）,2026,9(3):387-396,10.基金项目
Anhui Province Science and Technology Major Project(202303a07020008),Anhui Province Clinical and Transla-tional Research Special Project(202304295107020107),and Open Competition Project of Anhui University of Chinese Medicine(2024CXMMTCM002). （202303a07020008）
