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 

机构地区：[1]Department of Neurology,Zhejiang University School of Medicine Second Affiliated Hospital,Hangzhou,China [2]College of Computer Science and Technology,Zhejiang University,Hangzhou,China [3]State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University School of Mechanical Engineering,Hangzhou,China [4]Department of Computer Science and Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,China

出　　处：《Stroke & Vascular Neurology》2021年第4期610-614,I0067-I0072,共11页卒中与血管神经病学（英文）

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

摘　　要：Background and purpose Early haematoma expansion is determinative in predicting outcome of intracerebral haemorrhage(ICH)patients.The aims of this study are to develop a novel prediction model for haematoma expansion by applying deep learning model and validate its prediction accuracy.Methods Data of this study were obtained from a prospectively enrolled cohort of patients with primary supratentorial ICH from our centre.We developed a deep learning model to predict haematoma expansion and compared its performance with conventional non-contrast CT(NCCT)markers.To evaluate the predictability of this model,it was also compared with a logistic regression model based on haematoma volume or the BAT score.Results A total of 266 patients were finally included for analysis,and 74(27.8%)of them experienced early haematoma expansion.The 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,respectively.While 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 model.Moreover,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.

关 键 词：PATIENTS EXPANSION predicting 

分 类 号：R743[医药卫生—神经病学与精神病学]