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Artificial Intelligence Techniques for the effective diagnosis of Alzheimer’s Disease: A Review

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Abstract

Alzheimer's disease (AD) is a progressive and irreversible neurological disorder that leads to memory loss and cognitive decline. It is a prevalent form of dementia among individuals aged 65 and above. Accurate and early diagnosis of AD is of utmost importance. Diagnostic neuroimaging and software techniques have emerged as crucial tools for assessing early-stage dementia. The aim of this study is to provide a comprehensive review of recent research that employs deep learning (DL) techniques for the assessment of dementia, particularly the early stages of AD. The objective is to analyze the current state of research and explore the future directions of this field. The study involves a systematic review of literature that focuses on the utilization of DL techniques in the assessment of dementia and early AD diagnosis. Various datasets commonly used for AD prediction are examined. The study encompasses the discussion of different applications of contemporary AI algorithms in AD detection, as well as their merits, limitations, and performance. The review reveals that DL techniques have shown promise in the detection and diagnosis of AD. The use of DL, particularly in image classification and natural language processing, has demonstrated significant advancements in the field of AI. The study also highlights the potential of AI in AD genetic studies, providing valuable insights into the broad scope of this research.

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Acknowledgements

This work was carried out as part of a funded project from Vision Group on Science and Technology (VGST), India with GRD number: 880.

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  1. Nitte Meenakshi Institute of Technology, Bangalore, 560064, India

    K. Aditya Shastry

  2. M S Ramaiah Institute of Technology, Bangalore, 560054, Karnataka, India

    H. A. Sanjay

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Aditya Shastry, K., Sanjay, H.A. Artificial Intelligence Techniques for the effective diagnosis of Alzheimer’s Disease: A Review. Multimed Tools Appl 83, 40057–40092 (2024). https://doi.org/10.1007/s11042-023-16928-z

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