Abstract
Prediction of Alzheimer’s disease before the onset of symptoms is an important clinical challenge, as it offers the potential for earlier intervention to interrupt disease progression before the development of dementia symptoms, as well as spur new prevention and treatment avenues. In this work, we propose a model that learns how to predict Alzheimer’s disease ahead of time from structural Magnetic Resonance Imaging (sMRI) data. The contributions of this work are two-fold: (i) We use the latent variables learned by our model to visualize areas of the brain, which contribute to confident decisions. Our model appears to be focusing on specific areas of the neocortex, cerebellum, and brainstem, which are known to be clinically relevant. (ii) There are various ways in which disease might evolve from a patient’s current physiological state. We can leverage the latent variables in our model to capture the uncertainty over possible future patient outcomes. It can help identify and closely monitor people who are at a higher risk of disease, despite the current lack of clinical indications.
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Notes
- 1.
Each MRI is associated with the label of the next MRI of the same patient.
- 2.
All the MRIs corresponding to a patient in the training set lie in the training set only.
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Acknowledgement
This work was generously funded by Healthy Brains for Healthy Lives (HBHL) through CFREF grant. We would also like to thank Koustuv Sinha for useful discussions, comments and reviews of the manuscript.
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Basu, S., Wagstyl, K., Zandifar, A., Collins, L., Romero, A., Precup, D. (2019). Early Prediction of Alzheimer’s Disease Progression Using Variational Autoencoders. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_23
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DOI: https://doi.org/10.1007/978-3-030-32251-9_23
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