Prediction of Motor Outcome of Stroke Patients Using a Deep Learning Algorithm with Brain MRI as Input Data

Clinical Neurology: Research Article

Shin H.Kim J.K.Choo Y.J.Choi G.S.a· Chang M.C.c

Author affiliations

aDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Republic of Korea
bDepartment of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea
cDepartment of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu, Republic of Korea

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Article / Publication Details

First-Page Preview

Abstract of Clinical Neurology: Research Article

Received: March 10, 2022
Accepted: May 22, 2022
Published online: June 23, 2022

Number of Print Pages: 7
Number of Figures: 2
Number of Tables: 4

ISSN: 0014-3022 (Print)
eISSN: 1421-9913 (Online)

For additional information: https://www.karger.com/ENE

Abstract

Background: Deep learning techniques can outperform traditional machine learning techniques and learn from unstructured and perceptual data, such as images and languages. We evaluated whether a convolutional neural network (CNN) model using whole axial brain T2-weighted magnetic resonance (MR) images as input data can help predict motor outcomes of the upper and lower limbs at the chronic stage in stroke patients. Methods: We collected MR images taken at the early stage of stroke in 1,233 consecutive stroke patients. We categorized modified Brunnstrom classification (MBC) scores of ≥5 and functional ambulatory category (FAC) scores of ≥4 at 6 months after stroke as favorable outcomes in the upper and lower limbs, respectively, and MBC scores of <5 and FAC scores of <4 as poor outcomes. We applied a CNN to train the image data. Of the 1,233 patients, 70% (863 patients) were randomly selected for the training set and the remaining 30% (370 patients) were assigned to the validation set. Results: In the prediction of upper limb motor function on the validation dataset, the area under the curve (AUC) was 0.768, and for lower limb motor function, the AUC was 0.828. Conclusion: We showed that a CNN model trained using whole-brain axial T2-weighted MR images of stroke patients would help predict upper and lower limb motor function at the chronic stage.

© 2022 S. Karger AG, Basel

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First-Page Preview

Abstract of Clinical Neurology: Research Article

Received: March 10, 2022
Accepted: May 22, 2022
Published online: June 23, 2022

Number of Print Pages: 7
Number of Figures: 2
Number of Tables: 4

ISSN: 0014-3022 (Print)
eISSN: 1421-9913 (Online)

For additional information: https://www.karger.com/ENE

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