Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?
Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer’s disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/11/8/1473 |
| _version_ | 1797524208882810880 |
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| author | Carlo Fabrizio Andrea Termine Carlo Caltagirone Giulia Sancesario |
| author_facet | Carlo Fabrizio Andrea Termine Carlo Caltagirone Giulia Sancesario |
| author_sort | Carlo Fabrizio |
| collection | DOAJ |
| description | Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer’s disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies. |
| first_indexed | 2024-03-10T08:53:09Z |
| format | Article |
| id | doaj.art-f670473092de4264a0804b08e5d07259 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T08:53:09Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-f670473092de4264a0804b08e5d072592023-11-22T07:21:00ZengMDPI AGDiagnostics2075-44182021-08-01118147310.3390/diagnostics11081473Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?Carlo Fabrizio0Andrea Termine1Carlo Caltagirone2Giulia Sancesario3Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, ItalyLaboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, ItalyDepartment of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, ItalyBiobank, IRCCS Santa Lucia Foundation, 00179 Rome, ItalyDecades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer’s disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.https://www.mdpi.com/2075-4418/11/8/1473Alzheimer’s diseasediagnosismachine learningartificial intelligence |
| spellingShingle | Carlo Fabrizio Andrea Termine Carlo Caltagirone Giulia Sancesario Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge? Diagnostics Alzheimer’s disease diagnosis machine learning artificial intelligence |
| title | Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge? |
| title_full | Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge? |
| title_fullStr | Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge? |
| title_full_unstemmed | Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge? |
| title_short | Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge? |
| title_sort | artificial intelligence for alzheimer s disease promise or challenge |
| topic | Alzheimer’s disease diagnosis machine learning artificial intelligence |
| url | https://www.mdpi.com/2075-4418/11/8/1473 |
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