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

arXiv: https://arxiv.org/abs/2008.06098 · HTML: https://mlsanity.com/paper/2008.06098/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes](https://mlsanity.com/paper/2403.07536/) (2024)
- [A Deep Generative Model of Neonatal Cortical Surface Development](https://mlsanity.com/paper/2206.07542/) (2022)
- [Surface Vision Mamba: Leveraging Bidirectional State Space Model for Efficient Spherical Manifold Representation](https://mlsanity.com/paper/2501.14679/) (2025)
- [Future Unruptured Intracranial Aneurysm Growth Prediction using Mesh Convolutional Neural Networks](https://mlsanity.com/paper/2207.13518/) (2022)
- [Attention Maps in 3D Shape Classification for Dental Stage Estimation with Class Node Graph Attention Networks](https://mlsanity.com/paper/2509.07581/) (2025)
- [SurfAge-Net: A Hierarchical Surface-Based Network for Interpretable Fine-Grained Brain Age Prediction](https://mlsanity.com/paper/2602.06994/) (2026)

## Abstract (arXiv)

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.