Deep learning model for predicting gestational age after the first trimester using fetal MRI

Objectives To evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to biparietal diameter (BPD). Materials and methods Our Institutional Review Board approved this retrospective study, and a total of 184 T2-weighted MRI acquisitions from 184 fetuses (mean gestational age: 29.4 weeks) who underwent MRI between January 2014 and June 2019 were included. The reference standard gestational age was based on the last menstruation and ultrasonography measurements in the first trimester. The deep learning model was trained with T2-weighted images from 126 training cases and 29 validation cases. The remaining 29 cases were used as test data, with fetal age estimated by both the model and BPD measurement. The relationship between the estimated gestational age and the reference standard was evaluated with Lin's concordance correlation coefficient (rho c) and a Bland-Altman plot. The rho c was assessed with McBride's definition. Results The rho c of the model prediction was substantial (rho c = 0.964), but the rho c of the BPD prediction was moderate (rho c = 0.920). Both the model and BPD predictions had greater differences from the reference standard at increasing gestational age. However, the upper limit of the model's prediction (2.45 weeks) was significantly shorter than that of BPD (5.62 weeks). Conclusions Deep learning can accurately predict gestational age from fetal brain MR acquired after the first trimester.

Automated summary

The study found that a deep learning model can accurately predict gestational age from fetal brain MRI images acquired after the first trimester.