Using convolutional neural network to analyze brain MRI images for predicting functional outcomes of stroke.

Autor: Lai YL; Department of Physical Medicine & Rehabilitation, China Medical University Hsinchu Hospital, Hsinchu, Taiwan.; Institute of Physical Therapy, China Medical University, Taichung, Taiwan., Wu YD; Institute of Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan., Yeh HJ; Department of Physical Medicine and Rehabilitation, Taipei Hospital, Ministry of Health and Welfare, Taipei, Taiwan.; Institute of Public Health, National Yang Ming Chiao Tung University, Taipei, Taiwan., Wu YT; Department of Physical Medicine & Rehabilitation, Taitung Hospital, Ministry of Health and Welfare, Taitung, Taiwan., Tsai HY; Qingpu Residential Physiotherapy Clinic, Taoyuan, Taiwan., Chen JC; Institute of Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. george@nycu.edu.tw.; Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. george@nycu.edu.tw.; Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. george@nycu.edu.tw.; Catholic Mercy Hospital, Catholic Mercy Medical Foundation, Hsinchu County 303, Taiwan. george@nycu.edu.tw.; Medical Device Innovation & Translation Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. george@nycu.edu.tw.
Jazyk: angličtina
Zdroj: Medical & biological engineering & computing [Med Biol Eng Comput] 2022 Oct; Vol. 60 (10), pp. 2841-2849. Date of Electronic Publication: 2022 Aug 02.
DOI: 10.1007/s11517-022-02636-7
Abstrakt: Nowadays, the physicians usually predict functional outcomes of stroke based on clinical experiences and big data, so we wish to develop a model to accurately identify imaging features for predicting functional outcomes of stroke patients. Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to predict functional outcomes after 28-day hospitalization. A total of 44 individuals (24 men and 20 women) were recruited from Taoyuan General Hospital and China Medical University Hsinchu Hospital to enroll in the study. Based on "modified Rankin Scale (mRS)" and "National Institutes of Health Stroke Scale (NIHSS)" assessments, men, women, and mixed men and women were trained separately to evaluate the differences of the results, and we have shown that VGG-16 demonstrated high accuracy in predicting the functional outcomes of stroke patients. The new deep-learning approach has provided an automated decision support system for personalized recommendations and treatments, assisting the physicians to predict functional outcomes of stroke patients in clinical practice.
(© 2022. International Federation for Medical and Biological Engineering.)
Databáze: MEDLINE
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