Aims:Bleeding from gastroesophageal varices(GEV)is a medical emergency associated with high mortality.We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography(2D-SWE)of the...Aims:Bleeding from gastroesophageal varices(GEV)is a medical emergency associated with high mortality.We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography(2D-SWE)of the liver and spleen to precisely assess the risk of GEV and high-risk GEV(HRV).Methods:This was a multicenter,prospective study conducted from October 2020 to September 2022 across 12 hospitals in China.Patients with compensated advanced chronic liver disease(cACLD)were enrolled,with informed consent obtained.A total of 1136 liver stiffness measurement(LSM)images and 1042 spleen stiffness measurement(SSM)images generated by 2D SWE.Weleveraged deep learning methods to uncover associations between image features and patient risk;in this manner,we constructed models to predict GEV and HRV.Results:A multimodality deep learning risk prediction(DLRP)model was constructed to assess GEV and HRV based on LSM and SSM images and clinical information.Validation analysis revealed that the area under the curve(AUC)values of DLRP were 0.91 for GEV(95%confidence interval[CI],0.90-0.93,p<0.05)and 0.88 for HRV(95%CI,0.86-0.89,p<0.01),which were significantly and robustly better than those of canonical risk indicators,including the values of LSM(0.63 and 0.68 for GEV and HRV)andSSM(0.75for both GEV andHRV).Moreover,the DLRP model outperformed the model using individual parameters.In HRV prediction,the 2D-SWE SSM images(0.75)were more informative than LSM(0.68,p<0.01).Conclusion:Our DLRP model shows excellent performance in predicting GEV and HRV,outperforming the canonical risk indicators LSM and SSM.Additionally,the 2D-SWE SSM images provided more information and thus better accuracy in HRV prediction than the LSM images.展开更多
基金funded by the Key Research and Development Program of Jiangsu Province(No.BE2023767a)The Fundamental Research Fund of Southeast University(No.3290002303A2)+5 种基金Changjiang Scholars Talent Cultivation Project of Zhongda Hospital of Southeast University(No.2023YJXYYRCPY03)Research Personnel Cultivation Programme of Zhongda Hospital Southeast University(No.CZXM-GSP-RC125,CZXM-GSP-RC119)China Postdoctoral Science Foundation(No.2024M750461)National Natural Science Foundation of China(No.82402413)Natural Science Foundation of Jiangsu Province(No.BK20241681)National Natural Science Foundation of China(No.62061160369).
文摘Aims:Bleeding from gastroesophageal varices(GEV)is a medical emergency associated with high mortality.We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography(2D-SWE)of the liver and spleen to precisely assess the risk of GEV and high-risk GEV(HRV).Methods:This was a multicenter,prospective study conducted from October 2020 to September 2022 across 12 hospitals in China.Patients with compensated advanced chronic liver disease(cACLD)were enrolled,with informed consent obtained.A total of 1136 liver stiffness measurement(LSM)images and 1042 spleen stiffness measurement(SSM)images generated by 2D SWE.Weleveraged deep learning methods to uncover associations between image features and patient risk;in this manner,we constructed models to predict GEV and HRV.Results:A multimodality deep learning risk prediction(DLRP)model was constructed to assess GEV and HRV based on LSM and SSM images and clinical information.Validation analysis revealed that the area under the curve(AUC)values of DLRP were 0.91 for GEV(95%confidence interval[CI],0.90-0.93,p<0.05)and 0.88 for HRV(95%CI,0.86-0.89,p<0.01),which were significantly and robustly better than those of canonical risk indicators,including the values of LSM(0.63 and 0.68 for GEV and HRV)andSSM(0.75for both GEV andHRV).Moreover,the DLRP model outperformed the model using individual parameters.In HRV prediction,the 2D-SWE SSM images(0.75)were more informative than LSM(0.68,p<0.01).Conclusion:Our DLRP model shows excellent performance in predicting GEV and HRV,outperforming the canonical risk indicators LSM and SSM.Additionally,the 2D-SWE SSM images provided more information and thus better accuracy in HRV prediction than the LSM images.
