<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Alzheimer’s disease is the most common form of dementia that can cause a brain neurological disorder with progressive memory loss as a result of brain cell damage. Prevention and treatment of disease is a key challenge in today’s aging society. Accurate diagnosis of Alzheimer’s disease plays an important role in patient management, especially in the early stages of the disease, because awareness of risk allows patients to undergo preventive measures even before irreversible brain damage occurs. Over the years, techniques such as statistical modeling or machine learning algorithms have been used to improve understanding of this condition. The objective of the work is the study of the methods of detection and progression of Alzheimer’s disease through artificial intelligence techniques that have been proposed in the last three years.</p>
<p>The methodology used was based on the search, selection, review, and analysis of the state of the art and the most current articles published on the subject. The most representative works were analyzed, which allowed proposing a taxonomic classification of the studied methods and on this basis a possible solution strategy was proposed within the framework of the project developed by the Cuban Center for Neurosciences based on the conditions more convenient in terms of cost and effectiveness and the most current trends based on the use of artificial intelligence techniques.</p>
</abstract>

Alzheimer’s disease is the most common form of dementia that can cause a brain neurological disorder with progressive memory loss as a result of brain cell damage. Prevention and treatment of disease is a key challenge in today’s aging society. Accurate diagnosis of Alzheimer’s disease plays an important role in patient management, especially in the early stages of the disease, because awareness of risk allows patients to undergo preventive measures even before irreversible brain damage occurs. Over the years, techniques such as statistical modeling or machine learning algorithms have been used to improve understanding of this condition. The objective of the work is the study of the methods of detection and progression of Alzheimer’s disease through artificial intelligence techniques that have been proposed in the last three years.
The methodology used was based on the search, selection, review, and analysis of the state of the art and the most current articles published on the subject. The most representative works were analyzed, which allowed proposing a taxonomic classification of the studied methods and on this basis a possible solution strategy was proposed within the framework of the project developed by the Cuban Center for Neurosciences based on the conditions more convenient in terms of cost and effectiveness and the most current trends based on the use of artificial intelligence techniques.

Update on the Study of Alzheimer’s Disease Through Artificial Intelligence Techniques

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Alzheimer’s disease is the most common form of dementia that can cause a brain neurological disorder with progressive memory loss as a result of brain cell...

Update on the Study of Alzheimer’s Disease Through Artificial Intelligence Techniques

Pubblicato online: 26 gen 2024

Pagine: 51 - 60

Ricevuto: 11 gen 2023

Accettato: 18 feb 2023

DOI: https://doi.org/10.14313/jamris/2-2023/15

Parole chiave
Alzheimer disease, Detection, Progression, Artificial intelligence, Deep learning

© 2023 Eduardo Garea-Llano, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Update on the Study of Alzheimer’s Disease Through Artificial Intelligence Techniques

Pubblicato online: 26 gen 2024

Pagine: 51 - 60

Ricevuto: 11 gen 2023

Accettato: 18 feb 2023

DOI: https://doi.org/10.14313/jamris/2-2023/15

Parole chiaveAlzheimer disease, Detection, Progression, Artificial intelligence, Deep learning

© 2023 Eduardo Garea-Llano, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Alzheimer disease, Detection, Progression, Artificial intelligence, Deep learning