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

Xinpeng Cheng, Wei Zhang, Menglu Wu, Nan Jiang, Zhenni Guo, Xinyi Leng, Jianing Song, Hang Jin, Xin Sun, Fuliang Zhang, Jing Qin, Xiuli Yan, Zhenyu Cai, Ying Luo, Yi Yang, Jia Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article number074005
JournalPhysiological Measurement
Volume42
Issue number7
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Computed tomography
  • Hematoma
  • Intracerebral hemorrhage
  • Machine learning
  • Prediction

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

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