Deep learning for automatically predicting early haematoma expansion in Chinese patients

作     者：Jia-wei Zhong Yu-jia Jin Zai-jun Song Bo Lin Xiao-hui Lu Fang Chen Lu-sha Tong 

作者机构：Department of NeurologyZhejiang University School of Medicine Second Affiliated HospitalHangzhouChina College of Computer Science and TechnologyZhejiang UniversityHangzhouChina State Key Laboratory of Fluid Power and Mechatronic SystemsZhejiang University School of Mechanical EngineeringHangzhouChina Department of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina 

出 版 物：《Stroke & Vascular Neurology》 (卒中与血管神经病学（英文）)

年 卷 期：2021年第6卷第4期

页      面：610-614,I0067-I0072页

核心收录：

学科分类：1002[医学-临床医学] 100204[医学-神经病学] 10[医学] 

基　　金：This study was supported by the National Natural Science Foundation of China(NSFC 81971155) 

主　　题：patients expansion predicting 

摘      要：Background and purpose Early haematoma expansion is determinative in predicting outcome of intracerebral haemorrhage(ICH)*** aims of this study are to develop a novel prediction model for haematoma expansion by applying deep learning model and validate its prediction *** Data of this study were obtained from a prospectively enrolled cohort of patients with primary supratentorial ICH from our *** developed a deep learning model to predict haematoma expansion and compared its performance with conventional non-contrast CT(NCCT)*** evaluate the predictability of this model,it was also compared with a logistic regression model based on haematoma volume or the BAT *** A total of 266 patients were finally included for analysis,and 74(27.8%)of them experienced early haematoma *** deep learning model exhibited highest C statistic as 0.80,compared with 0.64,0.65,0.51,0.58 and 0.55 for hypodensities,black hole sign,blend sign,fluid level and irregular shape,*** the C statistics for swirl sign(0.70;p=0.211)and heterogenous density(0.70;p=0.141)were not significantly higher than that of the deep learning ***,the predictive value for the deep learning model was significantly superior to that of the logistic model of haematoma volume(0.62;p=0.042)and the BAT score(0.65;p=0.042).Conclusions Compared with the conventional NCCT markers and BAT predictive model,the deep learning algorithm showed superiority for predicting early haematoma expansion in ICH patients.