Department of Radiology, Kobe University School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe, Hyogo, 650-0017, Japan.
Eur Radiol. 2021 Jun;31(6):3775-3782. doi: 10.1007/s00330-021-07915-9. Epub 2021 Apr 14.
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).
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 (ρc) and a Bland-Altman plot. The ρc was assessed with McBride's definition.
The ρc of the model prediction was substantial (ρc = 0.964), but the ρc of the BPD prediction was moderate (ρc = 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).
Deep learning can accurately predict gestational age from fetal brain MR acquired after the first trimester.
• The prediction of gestational age using ultrasound is accurate in the first trimester but becomes inaccurate as gestational age increases. • Deep learning can accurately predict gestational age from fetal brain MRI acquired in the second and third trimester. • Prediction of gestational age by deep learning may have benefits for prenatal care in pregnancies that are underserved during the first trimester.
评估一种深度学习模型，以预测早孕期后胎儿大脑 MRI 获得的胎龄，与双顶间径（BPD）相比。
我们的机构审查委员会批准了这项回顾性研究，共纳入了 184 名胎儿的 184 次 T2 加权 MRI 采集（平均胎龄：29.4 周），这些胎儿在 2014 年 1 月至 2019 年 6 月期间进行了 MRI。参考标准胎龄基于末次月经和早孕超声测量。深度学习模型使用来自 126 个训练病例和 29 个验证病例的 T2 加权图像进行训练。其余 29 例作为测试数据，通过模型和 BPD 测量来估计胎儿年龄。使用林氏一致性相关系数（ρc）和 Bland-Altman 图评估估计胎龄与参考标准之间的关系。使用 McBride 的定义评估 ρc。
模型预测的 ρc 很高（ρc=0.964），但 BPD 预测的 ρc 适中（ρc=0.920）。模型和 BPD 预测都随着胎龄的增加与参考标准有更大的差异。然而，模型预测的上限（2.45 周）明显短于 BPD（5.62 周）。
深度学习可以从早孕期后胎儿大脑 MRI 中准确预测胎龄。
超声对胎龄的预测在早孕时准确，但随着胎龄的增加变得不准确。
深度学习可以从孕中期和孕晚期胎儿大脑 MRI 中准确预测胎龄。
深度学习对胎龄的预测可能有益于在早孕时服务不足的妊娠的产前护理。
