Artificial Intelligence for Early Detection of Alzheimer’s Disease: A Comprehensive Review of Algorithms, Data Modalities, and Translational Potential

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disorder that constitutes the leading cause of dementia worldwide. As disease-modifying therapies remain limited, the identification of AD during its earliest stages has become a central priority in both clinical practice and research. Early diagnosis enables timely intervention, improved patient stratification, and optimized management strategies, potentially delaying cognitive decline and enhancing quality of life. However, conventional diagnostic approaches are constrained by subjectivity, invasiveness, high cost, and limited sensitivity to preclinical pathology.In this context, artificial intelligence (AI)—encompassing machine learning (ML) and deep learning (DL) techniques—has emerged as a powerful framework for extracting clinically relevant patterns from complex biomedical data. AI-driven models are capable of integrating high-dimensional, multimodal information derived from neuroimaging, cognitive assessments, speech analysis, and blood-based biomarkers, thereby enabling more precise and earlier detection of Alzheimer’s disease.This review provides a comprehensive and critical synthesis of contemporary AI-based methodologies for early AD diagnosis, with a particular focus on algorithmic design, data modalities, performance metrics, interpretability, and translational feasibility. Seven peer-reviewed studies were systematically analyzed, encompassing traditional ML algorithms, advanced deep neural networks, hybrid architectures, and ensemble learning approaches. The findings indicate that deep learning models—particularly convolutional neural networks applied to MRI and PET imaging—consistently achieve superior diagnostic performance, often exceeding ۹۰% accuracy. Hybrid and multimodal models further enhance robustness and generalizability, while blood-based AI systems demonstrate strong potential for scalable, minimally invasive screening.Despite these advances, significant challenges persist, including dataset heterogeneity, limited external validation, and insufficient model interpretability. This review identifies key methodological limitations, highlights emerging trends, and proposes strategic directions for future research aimed at bridging the gap between algorithmic innovation and real-world clinical implementation.

کلیدواژه ها:

Alzheimer’s disease ، early diagnosis ، artificial intelligence ، machine learning ، deep learning ، multimodal data ، neuroimaging ، biomarkers