  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.
