Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer’s Disease

Article type: Review Article

Authors: Etekochay, Maudlyn O.^a | Amaravadhi, Amoolya Rao^b | González, Gabriel Villarrubia^c | Atanasov, Atanas G.^{d; e} | Matin, Maima^e | Mofatteh, Mohammad^f | Steinbusch, Harry Wilhelm^g | Tesfaye, Tadele^h | Praticò, Domenico^{i; *}

Affiliations: [a] PinnacleCare Intl. Baltimore, MD, USA | [b] Internal Medicine, Malla Reddy Institute of Medical Sciences, Jeedimetla, Hyderabad, India | [c] Expert Systems and Applications Laboratory (ESALAB), Faculty of Science, University of Salamanca, Salamanca, Spain | [d] Department of Biotechnology and Nutrigenomics, Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland | [e] Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria | [f] School of Medicine, Dentistry, and Biomedical Sciences, Queen’s University Belfast, Belfast, UK | [g] Department of Cellular and Translational Neuroscience, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Netherlands | [h] CareHealth Medical Practice, Jimma Road, Addis Ababa, Ethiopia | [i] Alzheimer’s Center at Temple, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA

Correspondence: [*] Correspondence to: Domenico Praticò, MD, FCPP, Alzheimer’s Center at Temple, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA. E-mail: [email protected].

Abstract: Alzheimer’s disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.

Keywords: Alzheimer’s disease, artificial intelligence, biomarker, machine learning, neuroimaging

DOI: 10.3233/JAD-231135

Journal: Journal of Alzheimer's Disease, vol. 99, no. 1, pp. 1-20, 2024