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

Journal: Scientific reports
PMID: 35082346

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 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 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.

Authors

  • Liyue Shen
    Department of Radiation Oncology, Stanford University, Stanford, California.
  • Jimmy Zheng
    Department of Radiology, School of Medicine Stanford University, 725 Welch Rd MC 5654, Palo Alto, CA, 94305, USA.
  • Edward H Lee
    Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, H3630, Stanford, CA, 94305.
  • Katie Shpanskaya
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Emily S McKenna
    Department of Radiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA.
  • Mahesh G Atluri
    Department of Radiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA.
  • Dinko Plasto
    Department of Radiology, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA.
  • Courtney Mitchell
    Department of Radiology, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA.
  • Lillian M Lai
    Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.
  • Carolina V Guimaraes
    Department of Radiology, Texas Children's Hospital, Houston, Texas; Department of Radiology, Stanford University, Stanford, California.
  • Hisham Dahmoush
    Department of Radiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA.
  • Jane Chueh
    Department of Obstetrics and Gynecology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA.
  • Safwan S Halabi
  • John M Pauly
    Department of Electrical Engineering, Stanford University, Stanford, California, USA.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Quin Lu
    Philips Healthcare North America, Gainesville, USA.
  • Ozgur Oztekin
    Department of Neuroradiology, Bakırçay University, Çiğli Education and Research Hospital, İzmir, Turkey.
  • Beth M Kline-Fath
    Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
  • Kristen W Yeom
    Department of Radiology, Stanford University, Stanford, California, United States of America.

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