Real-world prediction of preclinical Alzheimer's disease with a deep generative model.

Abstract

Amyloid positivity is an early indicator of Alzheimer's disease and is necessary to determine the disease. In this study, a deep generative model is utilized to predict the amyloid positivity of cognitively normal individuals using proxy measures, such as structural MRI scans, demographic variables, and cognitive scores, instead of invasive direct measurements. Through its remarkable efficacy in handling imperfect datasets caused by missing data or labels, and imbalanced classes, the model outperforms previous studies and widely used machine learning approaches with an AUROC of 0.8609. Furthermore, this study illuminates the model's adaptability to diverse clinical scenarios, even when feature sets or diagnostic criteria differ from the training data. We identify the brain regions and variables that contribute most to classification, including the lateral occipital lobes, posterior temporal lobe, and APOE ϵ4 allele. Taking advantage of deep generative models, our approach can not only provide inexpensive, non-invasive, and accurate diagnostics for preclinical Alzheimer's disease, but also meet real-world requirements for clinical translation of a deep learning model, including transferability and interpretability.

• Authors: Uiwon Hwang, Sung-Woo Kim, Dahuin Jung, SeungWook Kim, Hyejoo Lee, Sang Won Seo, Joon-Kyung Seong, Sungroh Yoon, Alzheimer’s Disease Neuroimaging Initiative
• Journal: Artificial intelligence in medicine (Artif Intell Med) Vol. 144 Pg. 102654 (10 2023) ISSN: 1873-2860 [Electronic] Netherlands
• PMID: 37783547 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.

Topics

• Humans
• Alzheimer Disease (diagnostic imaging, genetics)
• Cognitive Dysfunction (diagnosis)
• Brain (diagnostic imaging)
• Magnetic Resonance Imaging
• Machine Learning