Deep learning for automatically predicting early haematoma expansion in Chinese patients

Stroke and Vascular Neurology

article

Jia-wei Zhong¹  Yu-jia Jin¹  Zai-jun Song¹  Bo Lin²  Xiao-hui Lu³  Fang Chen⁴  Lu-sha Tong¹ 

[1] Department of Neurology , Zhejiang University School of Medicine Second Affiliated Hospital;College of Computer Science and Technology , Zhejiang University;State Key Laboratory of Fluid Power and Mechatronic Systems , Zhejiang University School of Mechanical Engineering;Department of Computer Science and Engineering , Nanjing University of Aeronautics and Astronautics

关键词: technology;    CT;    haemorrhage;   

DOI  :  10.1136/svn-2020-000647

学科分类：社会科学、人文和艺术（综合）

来源: BMJ Publishing Group

【 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.

【 授权许可】

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