Artificial Intelligence for Early Detection of Alzheimer's Disease: Integrating Neuroimaging, Clinical Data, and Biomarker

AbstractFinding Alzheimer’s disease early is one of the biggest challenges in modern neurology.During the preclinical stage or the Mild Cognitive Impairment (MCI) phase, early detection isvery important to begin treatment as soon as possible. Traditional methods often miss subtlebrain changes that occur long before clear memory loss appears. Recent advances in ArtificialIntelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), havesignificantly improved the ability to analyse complex brain scans such as MRI and PET. Thisstudy explores the use of data-driven models to detect early signs of Alzheimer’s disease thatmay not be visible through routine clinical examination. We found that combining multipledata sources through multimodal techniques significantly improves prediction accuracycompared to using a single data source. The results indicate that AI systems are transitioningfrom research tools to clinically valuable decision-support systems for early Alzheimer’sdiagnosis.