Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes

Liu, Gang1,2; Wu, Jiewei3,4; Dang, Chao1; Tan, Shuangquan1; Peng, Kangqiang5; Guo, Yaomin1; Xing, Shihui1; Xie, Chuanmiao5; Zeng, Jinsheng1; Tang, Xiaoying3

2021-11-01

10.1177/15459683211054178

NEUROREHABILITATION AND NEURAL REPAIR   影响因子和分区

1545-9683

1552-6844

Background. Neuroimaging biomarkers are valuable predictors of motor improvement after stroke, but there is a gap between published evidence and clinical usage. Objective. In this work, we aimed to investigate whether machine learning techniques, when applied to a combination of baseline whole brain volumes and clinical data, can accurately predict individual motor outcome after stroke. Methods. Upper extremity Fugl-Meyer Assessments (FMA-UE) were conducted 1 week and 12 weeks, and structural MRI was performed 1 week, after onset in 56 patients with subcortical infarction. Proportional recovery model residuals were employed to assign patients to proportional and poor recovery groups (34 vs 22). A sophisticated machine learning scheme, consisting of conditional infomax feature extraction, synthetic minority over-sampling technique for nominal and continuous, and bagging classification, was employed to predict motor outcomes, with the input features being a combination of baseline whole brain volumes and clinical data (FMA-UE scores). Results. The proposed machine learning scheme yielded an overall balanced accuracy of 87.71% in predicting proportional vs poor recovery outcomes, a sensitivity of 93.77% in correctly identifying poor recovery outcomes, and a ROC AUC of 89.74%. Compared with only using clinical data, adding whole brain volumes can significantly improve the classification performance, especially in terms of the overall balanced accuracy (from 80.88% to 87.71%) and the sensitivity (from 92.23% to 93.77%). Conclusions. Experimental results suggest that a combination of baseline whole brain volumes and clinical data, when equipped with appropriate machine learning techniques, may provide valuable information for personalized rehabilitation planning after subcortical infarction.

acute subcortical infarction machine learning T1-weighted image whole brain volume clinical data

SCI

英语

通讯

National Key R&D Program of China["2017YFC1307500","2017YFC0112404"] ; National Natural Science Foundation of China[62071210,81600998,81601522,81501546,81771137,81971103] ; Shenzhen Basic Research Program[JCYJ20190809120205578] ; High-level University Fund[G02236002] ; Key-Area Research and Development Program of Guangdong Province[2018B030340001] ; Natural Science Foundation of Guangdong Province["2016A030310132","2021A1515010600"] ; Sun Yat-sen University Clinical Research 5010 Program[2018001] ; Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases[2020B1212060017] ; Southern China International Cooperation Base for Early Intervention and Functional Rehabilitation of Neurological Diseases["2015B050501003","2020A0505020004"]

Neurosciences & Neurology ; Rehabilitation

Clinical Neurology ; Rehabilitation

WOS:000715261800001

SAGE PUBLICATIONS INC

NEUROSCIENCE & BEHAVIOR

Web of Science

1.Sun Yat Sen Univ, Affiliated Hosp 1, Dept Neurol,Natl Key Clin Dept & Key Discipline N, Guangdong Prov Key Lab Diag & Treatment Major Neu, Guangzhou, Peoples R China
2.Guangdong HongKong Macao Greater Bay Area Ctr Bra, Guangzhou, Peoples R China
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China
4.Sun Yat Sen Univ, Sch Elect & Informat Technol, Guangzhou, Peoples R China
5.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, Dept Med Imaging, State Key Lab Oncol Southern China,Canc Ctr, Guangzhou, Peoples R China

Liu, Gang,Wu, Jiewei,Dang, Chao,et al. Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes[J]. NEUROREHABILITATION AND NEURAL REPAIR,2021.

Liu, Gang.,Wu, Jiewei.,Dang, Chao.,Tan, Shuangquan.,Peng, Kangqiang.,...&Tang, Xiaoying.(2021).Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes.NEUROREHABILITATION AND NEURAL REPAIR.

Liu, Gang,et al."Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes".NEUROREHABILITATION AND NEURAL REPAIR (2021).