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

Journal: European neurology
Published Date: Jun 23, 2022

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.

Authors

  • Hyunkwang Shin
    Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Republic of Korea.
  • Jeoung Kun Kim
    Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea.
  • Yoo Jin Choo
    Department of Physical Medicine and Rehabilitation, College of Medicine, Yeoungnam University, 317-1, Daemyungdong, Namku, Daegu, 705-717, Republic of Korea.
  • Gyu Sang Choi
    Department of Information & Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk, Korea.
  • Min Cheol Chang
    Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Taegu, Republic of Korea. Electronic address: wheel633@ynu.ac.kr.

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