Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the cortical surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.

翻译：对新生儿年龄的准确估计对于测量神经发育、医学和成长结果至关重要。在本文中,我们提出一种新的方法,利用新生儿白质皮质表面的几何深学习技术,在扫描时预测月经后年龄。我们使用并比较了多种专门的神经网络结构,这些结构利用对皮质表面的不同几何表示来预测年龄;我们比较了MeshCNN、Pointnet++、GregCNN和体积基准。数据集是发展人类连接项目(DHCP)的一部分,是健康和早产的新生儿群。我们评估了我们对650个科目(727个扫描仪)和PA的处理办法,范围从27周到45周不等。我们的结果显示对估计的PA的准确预测,平均误差不到一周。