Deep Learning-Based MR Imaging for Analysis of Relation between Cerebrospinal Fluid Variation and Communicating Hydrocephalus after Decompressive Craniectomy for Craniocerebral Injury

• Published in: Scientific programming Vol. 2022; pp. 1 - 9
• Main Authors: Mao, Yangming, Shen, Zhouming, Wang, Jun, Zhu, Haifeng, Yu, Zhengyong, Chen, Xiang, Cheng, Hua
• Format: Journal Article
• Language: English
• Published: New York Hindawi 15.04.2022; John Wiley & Sons, Inc
• Subjects: Artificial intelligence; Artificial neural networks; Brain damage; Cerebrospinal fluid; Classification; Communication; Data analysis; Data imaging; Deep learning; Diagnosis; Disease; Efficiency; Hydrocephalus; Image classification; Image reconstruction; Injury analysis; Injury prevention; Intracranial pressure; Machine learning; Magnetic resonance imaging; Medical diagnosis; Medical imaging; Patients; Physicians; Principal components analysis; Signal processing; Tomography

The aim of this study was to analyze the relationship between cerebrospinal fluid variation and hydrocephalus through deep learning-based (DL-based) magnetic resonance imaging (MRI), in order to improve the postoperative recovery of craniocerebral injury (CI) patients and reduce the deterioration af...