Artificial intelligence in vascular and mixed dementia: a comprehensive review

Abstract

Abstract Vascular dementia (VaD) and mixed dementia present significant challenges in diagnosis and treatment due to their complex pathophysiology and overlap with other forms of dementia. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as powerful tools in addressing these challenges, offering new avenues for early detection, differential diagnosis, and personalised treatment strategies. This comprehensive review examines the applications of AI and ML in the context of VaD and mixed dementia, encompassing various aspects such as neuroimaging analysis, biomarker identification, and predictive modelling. The article explores the potential of AI-driven neuroimaging techniques, including structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and functional MRI, in detecting vascular lesions, white matter abnormalities, and brain connectivity patterns characteristic of VaD and mixed dementia. Furthermore, it highlights the role of ML algorithms in integrating multimodal data from genetics, neuroimaging, and clinical assessments to develop robust diagnostic and prognostic models. The review also discusses the challenges and limitations associated with implementing AI in clinical settings, such as data quality, interpretability, and ethical considerations. By synthesising the latest research findings and providing a critical analysis of current methods, this review aims to guide researchers and clinicians in leveraging the potential of AI and ML for improved patient care and advancing our understanding of vascular and mixed dementia.

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