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Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily li...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761739/ https://www.ncbi.nlm.nih.gov/pubmed/35046766 http://dx.doi.org/10.3389/fnins.2021.744190 |
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author | Pena, Danilo Suescun, Jessika Schiess, Mya Ellmore, Timothy M. Giancardo, Luca |
author_facet | Pena, Danilo Suescun, Jessika Schiess, Mya Ellmore, Timothy M. Giancardo, Luca |
author_sort | Pena, Danilo |
collection | PubMed |
description | Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI. |
format | Online Article Text |
id | pubmed-8761739 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87617392022-01-18 Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion Pena, Danilo Suescun, Jessika Schiess, Mya Ellmore, Timothy M. Giancardo, Luca Front Neurosci Neuroscience Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI. Frontiers Media S.A. 2022-01-03 /pmc/articles/PMC8761739/ /pubmed/35046766 http://dx.doi.org/10.3389/fnins.2021.744190 Text en Copyright © 2022 Pena, Suescun, Schiess, Ellmore, Giancardo and the Alzheimer’s Disease Neuroimaging Initiative. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Pena, Danilo Suescun, Jessika Schiess, Mya Ellmore, Timothy M. Giancardo, Luca Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion |
title | Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion |
title_full | Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion |
title_fullStr | Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion |
title_full_unstemmed | Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion |
title_short | Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion |
title_sort | toward a multimodal computer-aided diagnostic tool for alzheimer’s disease conversion |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761739/ https://www.ncbi.nlm.nih.gov/pubmed/35046766 http://dx.doi.org/10.3389/fnins.2021.744190 |
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