MRI-based fetal gestational age estimation using a structure-aware self-supervised network.

objectivesTo develop a structure-aware self-supervised deep learning model that leverages global edge information to reduce gestational age (GA) estimation error from fetal brain MRI and improve clinical reliability. MATERIALS AND

methodsA retrospective collection of 1630 fetal brain coronal T2-weighted MR images from 207 singleton pregnancies (mean GA, 30 ± 8 weeks; median, 34 weeks; range, 22-38 weeks) acquired between January 2019 and July 2023. GA, determined from the last menstrual period and the first-trimester ultrasound, served as the reference standard. Subjects were randomly split into a training set (80%) and an independent test set (20%) on a patient level. Model performance on the test set was evaluated using mean absolute error (MAE) and the coefficient of determination (R²), with 95% confidence intervals obtained by bootstrap resampling.

resultsThe proposed model achieved an MAE of 0.793 weeks (95% CI, 0.574-0.860) and an R² of 0.934 (95% CI, 0.918-0.967) for GA estimation from routine clinical MRI. Predicted GAs showed a strong linear association with the reference GAs (p < 0.001).

conclusionThe proposed structure-aware self-supervised model enables accurate MRI-derived estimation of clinical GA from fetal brain MRI. This approach may complement conventional dating in selected cases; prospective and multi-center validation is required before clinical deployment. KEY POINTS: Question Can fetal brain MRI provide an accurate estimation of clinical gestational age when conventional clinical dating methods are unreliable in mid-to-late pregnancy? Findings In 207 pregnancies, SSFN-Net achieved lower MAE (0.79 weeks) and higher R² than existing deep-learning baselines on fetal brain MRI. Clinical relevance MRI-derived gestational age estimation may complement ultrasound or LMP-based clinical dating in selected cases, providing an additional quantitative reference for prenatal assessment.