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A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features

Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overc...

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Autores principales: Castellazzi, Gloria, Cuzzoni, Maria Giovanna, Cotta Ramusino, Matteo, Martinelli, Daniele, Denaro, Federica, Ricciardi, Antonio, Vitali, Paolo, Anzalone, Nicoletta, Bernini, Sara, Palesi, Fulvia, Sinforiani, Elena, Costa, Alfredo, Micieli, Giuseppe, D'Angelo, Egidio, Magenes, Giovanni, Gandini Wheeler-Kingshott, Claudia A. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7300291/
https://www.ncbi.nlm.nih.gov/pubmed/32595465
http://dx.doi.org/10.3389/fninf.2020.00025
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author Castellazzi, Gloria
Cuzzoni, Maria Giovanna
Cotta Ramusino, Matteo
Martinelli, Daniele
Denaro, Federica
Ricciardi, Antonio
Vitali, Paolo
Anzalone, Nicoletta
Bernini, Sara
Palesi, Fulvia
Sinforiani, Elena
Costa, Alfredo
Micieli, Giuseppe
D'Angelo, Egidio
Magenes, Giovanni
Gandini Wheeler-Kingshott, Claudia A. M.
author_facet Castellazzi, Gloria
Cuzzoni, Maria Giovanna
Cotta Ramusino, Matteo
Martinelli, Daniele
Denaro, Federica
Ricciardi, Antonio
Vitali, Paolo
Anzalone, Nicoletta
Bernini, Sara
Palesi, Fulvia
Sinforiani, Elena
Costa, Alfredo
Micieli, Giuseppe
D'Angelo, Egidio
Magenes, Giovanni
Gandini Wheeler-Kingshott, Claudia A. M.
author_sort Castellazzi, Gloria
collection PubMed
description Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.
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spelling pubmed-73002912020-06-26 A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features Castellazzi, Gloria Cuzzoni, Maria Giovanna Cotta Ramusino, Matteo Martinelli, Daniele Denaro, Federica Ricciardi, Antonio Vitali, Paolo Anzalone, Nicoletta Bernini, Sara Palesi, Fulvia Sinforiani, Elena Costa, Alfredo Micieli, Giuseppe D'Angelo, Egidio Magenes, Giovanni Gandini Wheeler-Kingshott, Claudia A. M. Front Neuroinform Neuroscience Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations. Frontiers Media S.A. 2020-06-11 /pmc/articles/PMC7300291/ /pubmed/32595465 http://dx.doi.org/10.3389/fninf.2020.00025 Text en Copyright © 2020 Castellazzi, Cuzzoni, Cotta Ramusino, Martinelli, Denaro, Ricciardi, Vitali, Anzalone, Bernini, Palesi, Sinforiani, Costa, Micieli, D'Angelo, Magenes and Gandini Wheeler-Kingshott. http://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
Castellazzi, Gloria
Cuzzoni, Maria Giovanna
Cotta Ramusino, Matteo
Martinelli, Daniele
Denaro, Federica
Ricciardi, Antonio
Vitali, Paolo
Anzalone, Nicoletta
Bernini, Sara
Palesi, Fulvia
Sinforiani, Elena
Costa, Alfredo
Micieli, Giuseppe
D'Angelo, Egidio
Magenes, Giovanni
Gandini Wheeler-Kingshott, Claudia A. M.
A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features
title A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features
title_full A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features
title_fullStr A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features
title_full_unstemmed A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features
title_short A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features
title_sort machine learning approach for the differential diagnosis of alzheimer and vascular dementia fed by mri selected features
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7300291/
https://www.ncbi.nlm.nih.gov/pubmed/32595465
http://dx.doi.org/10.3389/fninf.2020.00025
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