Deep learning for automatically predicting early haematoma expansion in Chinese patients
Open Access
- 1 February 2021
- journal article
- research article
- Published by BMJ in Stroke and Vascular Neurology
- Vol. 6 (4), 610-614
- https://doi.org/10.1136/svn-2020-000647
Abstract
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.Keywords
Funding Information
- National Natural Science Foundation of China (81971155)
This publication has 17 references indexed in Scilit:
- Island SignStroke, 2017
- Intensive Blood Pressure Reduction and Spot Sign in Intracerebral HemorrhageJAMA Neurology, 2017
- Black Hole SignStroke, 2016
- Leakage Sign for Primary Intracerebral HemorrhageStroke, 2016
- Blend Sign on Computed TomographyStroke, 2015
- Swirl sign in intracerebral haemorrhage: definition, prevalence, reliability and prognostic valueBMC Neurology, 2012
- Prediction of haematoma growth and outcome in patients with intracerebral haemorrhage using the CT-angiography spot sign (PREDICT): a prospective observational studyThe Lancet Neurology, 2012
- Defining hematoma expansion in intracerebral hemorrhageNeurology, 2011
- CT Angiography “Spot Sign” Predicts Hematoma Expansion in Acute Intracerebral HemorrhageStroke, 2007
- Spontaneous Intracerebral HemorrhageThe New England Journal of Medicine, 2001