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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by funding from the EPSRC Centre for Doctoral Training in Smart Medical Imaging (EP/S022104/1) to Diego Fajardo-Rojas, from the Wellcome/EPSRC Centre for Medical Engineering[WT203148/Z/16/Z], a UKRI FLF [MR/T018119/1], and DFG Heisenberg funding through the High Tech Agenda Bavaria [502024488] to Jana Hutter, and from the NIHR Advanced Fellowship [NIHR3016640] and the MRC grant [MR/W019469/1] to Lisa Story. The funders had no role in software design, data collection or analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The data set used for this work comprises clinical records, MR data, and parameters manually extracted from ultrasound from 313 singleton pregnancies, acquired as part of 124 four ethically approved studies: 14/LO/1169 (Placenta Imaging Project, Fulham Research Ethics Committee, approval received September 23, 2016), 19-SS-0032 126 (Inflammation study in pregnancy, South East Scotland Ethics Committee, approval received March 7, 2019), 21/WA/0075 (Congenital Heart Imaging Programme, Wales Research Ethics Committee, approval received March 8, 2021), and 21/SS/0082 (Individualised Risk prediction of adverse neonatal outcome in pregnancies that deliver preterm using advanced MRI techniques and machine learning, South East Scotland Ethics Committee, approval received March 2022). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data code and data are available online at https://github.com/dfajardorojas/ml-for-preterm-birth-