Machine learning and the prediction of enlarged lateral ventricular postnatal development trend in fetuses with isolated ventriculomegaly

Motivation: To evaluate the intracranial structures and distinct components (grew matter [GM] and white matter [WM]) adjacent to the occipital horn of the lateral ventricle T2WI radiomics features in healthy fetuses and fetuses with ventriculomegaly (FVs),Goal(s): and to predict postnatal changes in the size of the enlarged lateral ventricle in FVs. Approach: Utilizing WM-radiomics on the affected sides of FVs, the SVM algorithm effectively predicted the changes in ventricle size, Results: as evidenced by the highest area under the curve (AUC) values of 0.771 and 0.738 in both the training and validation sets based on DeLong’s test (all P < 0.05). Impact: An MRI-based occipital WM-radiomics model holds the potential to predict trends in changing ventriculomegaly.The image-based predictive model exhibits applicability in prenatal care. Leveraging image analysis and machine learning techniques may provide further insight into the pathophysiologic features of ventriculomegaly.