Neural Network for Neonatal Brain Age Prediction from Structural MR Images

This study explored the application of artificial neural networks in predicting age of the neonatal brain based on the structural magnetic resonance images (MRI). We implemented regression, a classical method for predicting continuous values using supervised learning, based on the fastMONAI library. We tested the performance of the implemented neural network on the T
1
and T
2
weigthed neonatal brain MRI from the publicly available dHCP (Developing Human Connectome Project) dataset. Research was conducted on data subsets consisting of 10, 100, and 200 T
1
and T
2
weigthed images. Additionally, we explored the effect of using a constant learning rate versus automatically finding the optimal learning rate. Finally, we performed training, validation, and testing of the algorithm with the optimal parameters. The obtained results on the test data subset achieved an error of less than 1.5 weeks of age.