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Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders
Alzheimer’s disease (AD) is the most common form of neurodegenerative disorder. The prodromal phase of AD is mild cognitive impairment (MCI). The capacity to predict the transitional phase from MCI to AD represents a challenge for the scientific community. The adoption of artificial intelligence (AI...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10533011/ https://www.ncbi.nlm.nih.gov/pubmed/37763152 http://dx.doi.org/10.3390/jpm13091386 |
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author | Formica, Caterina Bonanno, Lilla Giambò, Fabio Mauro Maresca, Giuseppa Latella, Desiree Marra, Angela Cucinotta, Fabio Bonanno, Carmen Lombardo, Marco Tomarchio, Orazio Quartarone, Angelo Marino, Silvia Calabrò, Rocco Salvatore Lo Buono, Viviana |
author_facet | Formica, Caterina Bonanno, Lilla Giambò, Fabio Mauro Maresca, Giuseppa Latella, Desiree Marra, Angela Cucinotta, Fabio Bonanno, Carmen Lombardo, Marco Tomarchio, Orazio Quartarone, Angelo Marino, Silvia Calabrò, Rocco Salvatore Lo Buono, Viviana |
author_sort | Formica, Caterina |
collection | PubMed |
description | Alzheimer’s disease (AD) is the most common form of neurodegenerative disorder. The prodromal phase of AD is mild cognitive impairment (MCI). The capacity to predict the transitional phase from MCI to AD represents a challenge for the scientific community. The adoption of artificial intelligence (AI) is useful for diagnostic, predictive analysis starting from the clinical epidemiology of neurodegenerative disorders. We propose a Machine Learning Model (MLM) where the algorithms were trained on a set of neuropsychological, neurophysiological, and clinical data to predict the diagnosis of cognitive decline in both MCI and AD patients. Methods: We built a dataset with clinical and neuropsychological data of 4848 patients, of which 2156 had a diagnosis of AD, and 2684 of MCI, for the Machine Learning Model, and 60 patients were enrolled for the test dataset. We trained an ML algorithm using RoboMate software based on the training dataset, and then calculated its accuracy using the test dataset. Results: The Receiver Operating Characteristic (ROC) analysis revealed that diagnostic accuracy was 86%, with an appropriate cutoff value of 1.5; sensitivity was 72%; and specificity reached a value of 91% for clinical data prediction with MMSE. Conclusion: This method may support clinicians to provide a second opinion concerning high prognostic power in the progression of cognitive impairment. The MLM used in this study is based on big data that were confirmed in enrolled patients and given a credibility about the presence of determinant risk factors also supported by a cognitive test score. |
format | Online Article Text |
id | pubmed-10533011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105330112023-09-28 Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders Formica, Caterina Bonanno, Lilla Giambò, Fabio Mauro Maresca, Giuseppa Latella, Desiree Marra, Angela Cucinotta, Fabio Bonanno, Carmen Lombardo, Marco Tomarchio, Orazio Quartarone, Angelo Marino, Silvia Calabrò, Rocco Salvatore Lo Buono, Viviana J Pers Med Article Alzheimer’s disease (AD) is the most common form of neurodegenerative disorder. The prodromal phase of AD is mild cognitive impairment (MCI). The capacity to predict the transitional phase from MCI to AD represents a challenge for the scientific community. The adoption of artificial intelligence (AI) is useful for diagnostic, predictive analysis starting from the clinical epidemiology of neurodegenerative disorders. We propose a Machine Learning Model (MLM) where the algorithms were trained on a set of neuropsychological, neurophysiological, and clinical data to predict the diagnosis of cognitive decline in both MCI and AD patients. Methods: We built a dataset with clinical and neuropsychological data of 4848 patients, of which 2156 had a diagnosis of AD, and 2684 of MCI, for the Machine Learning Model, and 60 patients were enrolled for the test dataset. We trained an ML algorithm using RoboMate software based on the training dataset, and then calculated its accuracy using the test dataset. Results: The Receiver Operating Characteristic (ROC) analysis revealed that diagnostic accuracy was 86%, with an appropriate cutoff value of 1.5; sensitivity was 72%; and specificity reached a value of 91% for clinical data prediction with MMSE. Conclusion: This method may support clinicians to provide a second opinion concerning high prognostic power in the progression of cognitive impairment. The MLM used in this study is based on big data that were confirmed in enrolled patients and given a credibility about the presence of determinant risk factors also supported by a cognitive test score. MDPI 2023-09-15 /pmc/articles/PMC10533011/ /pubmed/37763152 http://dx.doi.org/10.3390/jpm13091386 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Formica, Caterina Bonanno, Lilla Giambò, Fabio Mauro Maresca, Giuseppa Latella, Desiree Marra, Angela Cucinotta, Fabio Bonanno, Carmen Lombardo, Marco Tomarchio, Orazio Quartarone, Angelo Marino, Silvia Calabrò, Rocco Salvatore Lo Buono, Viviana Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders |
title | Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders |
title_full | Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders |
title_fullStr | Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders |
title_full_unstemmed | Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders |
title_short | Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders |
title_sort | paving the way for predicting the progression of cognitive decline: the potential role of machine learning algorithms in the clinical management of neurodegenerative disorders |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10533011/ https://www.ncbi.nlm.nih.gov/pubmed/37763152 http://dx.doi.org/10.3390/jpm13091386 |
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