Predicting Gestational Age at Birth in the Context of Preterm Birth Using Comprehensive Fetal MRI Acquisitions

The accurate prediction of preterm birth is a clinically crucial but challenging problem due to its complex aetiology. In this work, data from fetal anatomical and functional multi-organ MRI acquisitions are used to train Random Forests and Support Vector Machines to predict gestational age at delivery. These predictions are classified as 'term' or 'preterm'. The model with highest sensitivity, a Random Forest, achieved 0.85 sensitivity, 0.81 accuracy, 0.8 specificity, 1.99 weeks Mean Absolute Error, and 0.58 R2 score. This work proves the potential of Machine Learning models trained on anatomical and functional MRI data to predict gestational age at delivery.