Abstract #3718
Machine learning and the prediction of cerebral ventricular changes in fetuses with ventriculomegaly: a fetal MRI study
Xue Chen1, Zhou Huang2, Yonggang Li2,3,4, Jibin Zhang1, Xiaowen Gu1, and Zhisen Li1
1Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou city, Jiangsu province, 215002, China, 2Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou city, Jiangsu province, 215000, China, 3Institute of Medical Imaging, Soochow University, Suzhou city, Jiangsu province, 215000, China, 4National Clinical Research Center for Hematologic Diseases, the First Affiliated Hospital of Soochow University, Suzhou city, Jiangsu province, 215000, China
Synopsis
Fetal ventriculomegaly (FV) is one of the central nervous system (CNS) major malformations. Prenatal clinical research has seen only a few applications of machine learning. To our knowledge, radiomic machine learning for predicting the change of cerebral ventricular in fetuses with ventriculomegaly has not been reported. We discovered that a combination of clinical characteristics and fetal MRI features could accurately predict postnatal ventricular changes in fetuses with ventriculomegaly. The occipital lobe white matter on the dilated lateral ventricle side may play an important role in the pathophysiological process in FV.
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