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

Authors :
Cheng, Xinpeng
Zhang, Wei
Wu, Menglu
Jiang, Nan
Guo, Zhenni
Leng, Xinyi
Song, Jianing
Jin, Hang
Sun, Xin
Zhang, Fuliang
Qin, Jing
Yan, Xiuli
Cai, Zhenyu
Luo, Ying
Yang, Yi
Liu, Jia
Source :
Physiological Measurement. Jul2021, Vol. 42 Issue 7, p1-10. 10p.
Publication Year :
2021

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09673334
Volume :
42
Issue :
7
Database :
Academic Search Index
Journal :
Physiological Measurement
Publication Type :
Academic Journal
Accession number :
151700897
Full Text :
https://doi.org/10.1088/1361-6579/ac10ab