@ARTICLE{Suarez-Araujo_Carmen_Paz_Current_2024,
 author={Suarez-Araujo, Carmen Paz and Cabrera-Leon, Ylerm and Fernandez-Lopez, Pablo and Baez, Patricio Garcıa},
 volume={vol. 70},
 number={No 2},
 journal={International Journal of Electronics and Telecommunications},
 pages={277-283},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={Abstract—The prevalence of dementia is expected to increment in the next decades as the elderly population grows and ages. Hence, Alzheimer’s Disease (AD), as the most frequent dementia, will be more problematic from a socioeconomic point of view. Different diagnostic criteria have been proposed by clinicians for the early diagnosis of AD. After discarding the longitudinal and prognosis articles, a selection of articles from the last decade and based on Artificial Neural Networks (ANNs) was collated from the PubMed database, and complemented with researches extracted from others. The latest trends on this field were discovered in these selected articles, which were later discussed. Only articles based whether on shallow ANNs, Deep Learning (DL) or a mix of both were included. The total number of cross-sectional articles that complied with our selection criteria was 154. Convolutional Neural Networks (CNNs) combined with neuroimaging has been the most popular approach, yielding very good performance results. Approaches based on nonneuroimaging techniques, such as gait, genetics, speech and neuropsychological tests, were less common but have their own advantages. Multimodality solutions may become even more prevalent in the near future. Similarly, novel diagnostic criteria will appear and the popularity of currently not-so-common ones will expand. A new proposal emerged from these trends, which is based on ontogenetic ANNs.},
 type={Article},
 title={Current trends on the early diagnosis of Alzheimer’s Disease by means of neural computation methods},
 URL={http://journals.pan.pl/Content/131783/2_4529_Araujo_sk2.pdf},
 doi={10.24425/ijet.2024.149542},
 keywords={Alzheimer’s Disease, Mild Cognitive Impairment, Computer-Aided Diagnosis, Artificial Neural Network, Deep Learning},
}