A review of artificial intelligence methods for Alzheimers disease diagnosis: Insights from neuroimaging to sensor data analysis
Bazarbekov I. Razaque A. Ipalakova M. Yoo J. Assipova Z. Almisreb A.
June 2024Elsevier Ltd
Biomedical Signal Processing and Control
2024#92
Alzheimers disease is the most common cause of dementia, gradually impairing memory, intellectual, learning, and organizational capacities. An individuals capacity to perform fundamental daily tasks is greatly impacted. This review examines the advancements in diagnosing Alzheimers disease (AD) using artificial intelligence (AI) methods and machine learning (ML) algorithms. The review introduces the importance of diagnosing AD accurately and the potential benefits of using AI techniques and machine learning algorithms for this purpose. The review is based on various state-of-the-art data sources including MRI data, PET imaging, EEG and MEG signals, and data from various sensors. The state-of-the-art radiomics approaches are explored to extract a wide range of information from medical images using data-characterization algorithms. These features can show temporal patterns and qualities that are not visible to the human eye. A novel data source (handwriting data) is thoroughly investigated and coupled with AI algorithms for the precise and early detection of cognitive loss associated with Alzheimers disease. The paper discusses research directions, prospects, and future advances, as well as the proposed notion of employing a Robopen with an MPU-9250 sensor connected via Arduino. Finally, the review concludes with a summary of its significant findings and their clinical implications.
Alzheimers disease , Artificial intelligence , Diagnostic methods , Machine learning , Neuroimaging , Sensor data analysis
Text of the article Перейти на текст статьи
Department of Computer Engineering, International Information Technology University, Kazakhstan
Department of Cyber security, information processing and storage, Satbayev University, Almaty, Kazakhstan
Department of Recreation Geography and Tourism Al-Farabi, Kazakh National University, Kazakhstan
Department of Computer Science and Engineering, International University of Sarajevo, Bosnia and Herzegovina
School of Computing, Gachon University, South Korea
Department of Computer Engineering
Department of Cyber security
Department of Recreation Geography and Tourism Al-Farabi
Department of Computer Science and Engineering
School of Computing
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026