Multicentre study, Not applicable, Retrospective, Dementia, Computer Applications-General, MR, Neuroradiology brain, Computer applications, Artificial Intelligence, Artificial Intelligence and Machine Learning
S. Kaliyugarasan, A. Lundervold, A. S. Lundervold; Bergen/NO
As the trained model is able to accurately predict the chronological age very fast, it could potentially be incorporated into clinical routine through PACS systems and established postprocessing tools.
We are currently working to incorporate techniques from “explainable AI” into our setup. As CNNs extract image-features automatically, model interpretation is challenging. However, by using techniques like regression activation maps we aim to provide a better understanding of model predictions, and also potentially uncover features (brain MRI-derived biomarkers) related to abnormal aging.
Acknowledgments: Our work was supported by the Trond Mohn Research Foundation, grant number BFS2018TMT07. Data collection was performed by ADNI, AIBL, CC-359, HCP, IXI, OASIS, PPMI, SALD and YALE.