State-of-the-Art AI Approaches for Alzheimer’s Disease Detection from Brain MRI: A Systematic Literature Review

Abstract

The increasing occurrence of many non-transmittable diseases, such as adult-onset dementia disorders and neurodegenerative diseases, is correlated with the rapid aging of populations, particularly in the industrialized western world. A correct diagnosis made early on is essential to promote appropriate treatments, such as treatment and preventive measures. A method that is frequently used for the diagnosis of neurological illnesses is conventional magnetic resonance imaging, or MRI. There is mounting evidence that using artificial intelligence (AI) techniques in conjunction with magnetic resonance imaging (MRI) can significantly enhance the precision of dementia diagnosis across various subtypes. Numerous investigations have been carried out to look at aberrant brain structure conditions and to identify Alzheimer's and dementia disorders utilizing features taken from medical images. Based on these findings, it is critical to identify Alzheimer's and dementia patients at an early stage and to treat them with the right care. To make this diagnosis, high-quality magnetic resonance (MR) images are required. However, it also results in reduced spatial coverage and longer scanning and identification times, even if it produces high-quality images. In this setting, the discipline of biomedical image processing has experienced significant growth and has evolved into an interdisciplinary study area including numerous fields.
Among the most used types of data are images of the brain. In applications related to neurology, AI may help physicians make better decisions. For AI to be more effectively used in the brain, there are still important problems that need to be resolved. Building understandable AI algorithms and compiling extensive data are crucial to achieving this goal.