Brain age prediction models have succeeded in predicting clinical outcomes in
neurodegenerative diseases, but can struggle with tasks involving faster
progressing diseases and low quality data. To enhance their performance, we
employ a semi-supervised diffusion model, obtaining a 0.83(p<0.01) correlation
between chronological and predicted age on low quality T1w MR images. This was
competitive with state-of-the-art non-generative methods. Furthermore, the
predictions produced by our model were significantly associated with survival
length (r=0.24, p<0.05) in Amyotrophic Lateral Sclerosis. Thus, our approach
demonstrates the value of diffusion-based architectures for the task of brain
age prediction.

我们采用半监督扩散模型，在低质量的 T1w MR 图像上获得了年龄的预测结果与实际年龄之间 0.83（p<0.01）的相关性，这竞争力超过了最先进的非生成方法。此外，我们模型的预测结果与肌萎缩侧索硬化的生存期长度有显著相关性 (r=0.24, p<0.05)，从而显示了扩散模型在脑龄预测任务中的价值。