A review of artificial intelligence methods for Alzheimer's disease diagnosis: Insights from neuroimaging to sensor data analysis

Abstract

Alzheimer's disease is the most common cause of dementia, gradually impairing memory, intellectual, learning, and organizational capacities. An individual's capacity to perform fundamental daily tasks is greatly impacted. This review examines the advancements in diagnosing Alzheimer's 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 Alzheimer's 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.