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Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
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Deep learning model for predicting gestational age after the first trimester using fetal MRI.

Yasuyuki Kojita1, Hidetoshi Matsuo2, Tomonori Kanda2

  • 1Department of Radiology, Kobe University School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan. y.kojita10312@gmail.com.

European Radiology
|April 14, 2021
PubMed
Summary

A new deep learning model accurately predicts fetal gestational age from brain MRI after the first trimester. This method shows improved accuracy compared to traditional biparietal diameter measurements, especially later in pregnancy.

Keywords:
BrainDeep learningFetusGestational agePregnancy

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Fetal Medicine

Background:

  • Ultrasound-based gestational age prediction is accurate in the first trimester but declines in accuracy later in pregnancy.
  • Accurate gestational age estimation is crucial for optimal prenatal care and management.

Purpose of the Study:

  • To evaluate a deep learning model for predicting gestational age using fetal brain MRI.
  • To compare the deep learning model's accuracy against biparietal diameter (BPD) measurements.

Main Methods:

  • Retrospective study of 184 fetal brain MRIs (mean gestational age 29.4 weeks).
  • Deep learning model trained and tested on T2-weighted MRI data.
  • Gestational age reference standard from first-trimester ultrasound and last menstrual period.
  • Comparison using Lin's concordance correlation coefficient (ρc) and Bland-Altman plots.

Main Results:

  • Deep learning model achieved substantial agreement (ρc = 0.964) vs. moderate for BPD (ρc = 0.920).
  • Both methods showed increased prediction differences with advancing gestational age.
  • The deep learning model demonstrated a significantly smaller upper limit of prediction error (2.45 weeks) compared to BPD (5.62 weeks).

Conclusions:

  • Deep learning accurately predicts gestational age from fetal brain MRI acquired in the second and third trimesters.
  • This AI-driven approach offers potential benefits for prenatal care in pregnancies with limited early-trimester data.