A prediction of hematoma expansion in hemorrhagic patients using a novel dual-modal machine learning strategy

血肿 情态动词 计算机科学 医学 人工智能 放射科 脑出血 人工神经网络 算法 外科 材料科学 格拉斯哥昏迷指数 高分子化学
作者
Xinpeng Cheng,Wei Zhang,Meng Wu,Nan Jiang,Guo Z,Xinyi Leng,Jia Ning Song,Hang Jin,Xin Sun,Fu-Liang Zhang,Jing Qin,Xiaofeng Yan,Zhenyu Cai,Ying Luo,Yi Yang,Jia Liu
出处
期刊:Physiological Measurement [IOP Publishing]
卷期号:42 (7): 074005-074005 被引量:2
标识
DOI:10.1088/1361-6579/ac10ab
摘要

Objective.Hematoma expansion is closely associated with adverse functional outcomes in patients with intracerebral hemorrhage (ICH). Prediction of hematoma expansion would therefore be of great clinical significance. We therefore attempted to predict hematoma expansion using a dual-modal machine learning (ML) strategy which combines information from non-contrast computed tomography (NCCT) images and multiple clinical variables.Approach.We retrospectively identified 140 ICH patients (57 with hematoma expansion) with 5616 NCCT images of hematoma (2635 with hematoma expansion) and 10 clinical variables. The dual-modal ML strategy consists of two steps. The first step is to derive a mono-modal predictor from a deep convolutional neural network using solely NCCT images. The second step is to achieve a dual-modal predictor by combining the mono-modal predictor with 10 clinical variables to predict hematoma growth using a multi-layer perception network.Main results. For the mono-modal predictor, the best performance was merely 69.5% in accuracy with solely the NCCT images, whereas the dual-modal predictor could boost the accuracy greatly to be 86.5% by combining clinical variables.Significance.To our knowledge, this is the best performance from using ML to predict hematoma expansion. It could be potentially useful as a screening tool for high-risk patients with ICH, though further clinical tests would be necessary to show its performance on a larger cohort of patients.
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