Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI

Preterm birth is associated with significant mortality and a risk for lifelong morbidity. The complex multifactorial aetiology hampers accurate prediction and thus optimal care. A pipeline consisting of bespoke machine learning methods for data imputation, feature selection, and regression models to predict gestational age (GA) at birth was developed and evaluated from comprehensive multi-modal morphological and functional fetal MRI data from 176 control cases and 67 preterm birth cases. The GA at birth predictions were classified into term and preterm categories and their accuracy, sensitivity, and specificity were reported. An ablation study was performed to further validate the design of the pipeline. The pipeline achieves an R2 score of 0.51 and a mean absolute error of 2.22 weeks. It also achieves a 0.88 accuracy, 0.86 sensitivity, and 0.89 specificity, outperforming previous classification efforts in the literature. The predominant features selected by the pipeline include cervical length and various placental T2* values. The confluence of fast, motion-robust and multi-modal fetal MRI techniques and machine learning prediction allowed the prediction of the gestation at birth. This information is essential for any pregnancy. To the best of our knowledge, preterm birth had only been addressed as a classification problem in the literature. Therefore, this work provides a proof of concept. Future work will increase the cohort size to allow for finer stratification within the preterm birth cohort. Author summaryPreterm birth is defined as the birth of a baby before the 37th week of pregnancy. It poses a serious risk to the life of a newborn and it is associated with a variety of severe lifelong health problems. Currently, the causes of preterm birth are not completely understood and therefore predicting when a baby will be born prematurely remains a challenging problem. Fetal MRI is an imaging technique that can provide detailed information about the development of the fetus and it is used to support the care of pregnancies at high-risk of preterm birth. Our work combines machine learning techniques with fetal MRI to predict gestational age at birth. The ability to predict this information is crucial for providing adequate care and effective delivery planning. The main contribution of our study is demonstrating that it is possible to make use of all the information obtained from fetal MRI to estimate the delivery date of a baby. To the best of our knowledge, this is the first study to combine machine learning with such a rich data set to produce these important predictions.