Influences of Magnetic Resonance Imaging Superresolution Algorithm-Based Transition Care on Prognosis of Children with Severe Viral Encephalitis.

Journal: Computational and mathematical methods in medicine

Published Date: Jan 1, 2022

Abstract

OBJECTIVE: Its goal was to see how convolutional neural network- (CNN-) based superresolution (SR) technology magnetic resonance imaging- (MRI-) assisted transition care (TC) affected the prognosis of children with severe viral encephalitis (SVE) and how effective it was.

Authors

Yan Wang

College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
Yan Zhang

Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China.
Ling Su

Department of Infusion Room of Emergency, Children's Hospital of Nanjing Medical University, Nanjing, 210000 Jiangsu Province, China.

Keywords

Algorithms Child Deep Learning Encephalitis, Viral Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Prognosis Quality of Life Transitional Care

External Resources

View on PubMed Access via DOI PubMed (35756412)

Influences of Magnetic Resonance Imaging Superresolution Algorithm-Based Transition Care on Prognosis of Children with Severe Viral Encephalitis.

Abstract

Authors

Keywords

External Resources

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