Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface

TL;DR

This paper introduces a geometric deep learning approach to accurately predict neonatal post-menstrual age from white matter cortical surface data, outperforming traditional methods.

Contribution

It compares multiple neural network architectures on geometric data for neonatal age prediction, demonstrating superior accuracy with less than one week mean error.

Findings

Mean prediction error less than one week.

Effective use of geometric deep learning architectures.

Validated on a large neonatal dataset.

Abstract

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.