Attention-guided deep learning for gestational age prediction using fetal brain MRI
Liyue Shen, Jimmy Zheng, Edward H. Lee, Katie Shpanskaya, Emily McKenna, Mahesh G. Atluri, Dinko Plasto, Courtney Mitchell, Lillian M. Lai, Carolina V. Guimaraes, Hisham Dahmoush, Jane Chueh, Safwan S. Halabi, John M. Pauly, Lei Xing, Kun Lu, Özgür Öztekin, Beth M. Kline‐Fath, Kristen W. Yeom
Abstract
Abstract Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R 2 score of 0.945, mean absolute error of 6.7 days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39 weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R 2 scores of 0.81–0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.