Semi-Supervised Diffusion Model for Brain Age Prediction

Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and clinical-grade data. To enhance their performance, we employed a semi-supervised diffusion model, obtaining a 0.90(p<0.01) correlation between chronological and predicted age on clinical-grade T1w MR images. This outperformed standard non-generative methods. Furthermore, the prediction's produced by our model were significantly associated with survival duration (r=0.24, p<0.01) in Amyotrophic Lateral Sclerosis. Thus, our approach demonstrates the value of diffusion-based architectures for the task of brain age prediction.