Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI
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Computer Science > Machine Learning
arXiv:2606.20172 (cs)
[Submitted on 18 Jun 2026]
Title:Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI
Authors:Diego Fajardo-Rojas, Megan Hall, Daniel Cromb, Mary A. Rutherford, Lisa Story, Emma C. Robinson, Jana Hutter
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Abstract: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 333 control cases and 93 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. Performance was evaluated using stratified 10-fold cross-validation. The pipeline achieves an R2 score of 0.13 and a mean absolute error of 2.74 weeks. It also achieves a 0.77 accuracy, 0.59 sensitivity, and 0.82 specificity across folds. The predominant features selected by the pipeline include cervical length and statistics derived from 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. Our code is available at this https URL.
| Comments: | Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.20172 [cs.LG] |
| (or arXiv:2606.20172v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.20172
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Machine.Learning.for.Biomedical.Imaging. 2026 (2026) |
| Related DOI: | https://doi.org/10.59275/j.melba.2026-f34b
DOI(s) linking to related resources
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Submission history
From: Diego Fajardo Rojas [view email][v1] Thu, 18 Jun 2026 12:39:40 UTC (1,357 KB)
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View a PDF of the paper titled Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI, by Diego Fajardo-Rojas and 6 other authors
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