Attention-guided deep learning for gestational age prediction using fetal brain MRI

Liyue Shen, Jimmy Zheng*, Edward H. Lee, Katie Shpanskaya, Emily S. 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, Quin Lu, Ozgur Oztekin, Beth M. Kline-Fath, Kristen W. Yeom

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

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 R2 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 R2 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.

Original languageEnglish (US)
Article number1408
JournalScientific reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

ASJC Scopus subject areas

  • General

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