• Zhiri Tang, Yiqin Zhu, Xin Lu, Dengjun Wu, Xinlin Fan, Junjun Shen, and Limin Xiao.
• Department of Neurosurgery, the First Affiliated Hospital of Nanchang University, Jiangxi, P.R. China; Department of Electronic Science and Technology, School of Physics and Technology, Wuhan University, Wuhan, P.R. China.
• World Neurosurg. 2022 Sep 1; 165: e128e136e128-e136.

ObjectivesWe aimed to predict hematoma expansion in intracerebral hemorrhage (ICH) patients by using the deep learning technique.MethodsWe retrospectively collected data from ICH patients treated between May 2015 and May 2019. Head computed tomography (CT) scans were performed at admission, and 6 hours, 24 hours, and 72 hours after admission. CT scans were mandatory when neurologic deficits occurred. Univariate and multivariate analyses were conducted to illustrate the association between clinical variables and hematoma expansion. Convolutional neural network (CNN) was adopted to predict hematoma expansion based on brain CT slices. In addition, 5 machine learning methods, including support vector machine, multi-layer perceptron, naive Bayes, decision tree, and random forest, were also performed to predict hematoma expansion based on clinical variables for comparisons.ResultsA total of 223 patients were included. It was revealed that patients' older age (odds ratio [95% confidence interval]: 1.783 [1.417-1.924]), cerebral hemorrhage and breaking into the ventricle (2.524 [1.291-1.778]), coagulopathy (2.341 [1.677-3.454]), and baseline National Institutes of Health Stroke Scale (1.545 [1.132-3.203]) and Glasgow Coma Scale scores (0.782 [0.432-0.918]) independently associated with hematoma expanding. After 4-5 epochs, the CNN framework was well trained. The average sensitivity, specificity, and accuracy of CNN prediction are 0.9197, 0.8837, and 0.9058, respectively. Compared with 5 machine learning methods based on clinical variables, CNN can also achieve better performance.ConclusionsMore than 90% of hematomas with or without expansion can be precisely classified by deep learning technology within this study, which is better than other methods based on clinical variables only. Deep learning technology could favorably predict hematoma expansion from non-contrast CT scan images.Copyright © 2022 Elsevier Inc. All rights reserved.

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