Early detection of dementia using artificial intelligence and multimodal features with a focus on neuroimaging: A systematic literature review

ConclusionsThe main findings of the study were that Alzheimer ’s disease and Mild Cognitive Impairment (MCI) are the most researched dementia types in the field; typical choice for dementia detection is Machine Learning (ML) methods; the most popular modalities combination is T1w + Fluorodeoxyglucose - Positron Emission Tomography (FDG-PET); accuracy, sensit ivity and specificity are the main evaluation metrics used by the researchers; k-fold validation is being used the most; Alzheimer’s disease neuroimaging initiative (ADNI) is the most used dataset by researchers; intensity and spacial normalization, skull stripping and segmentation are the most co mmon pre-processing techniques for neuroimages; voxel average intensities are being used the most as features in classification extracted from neuroimages; explainability still persists as one of the main issues in adoption of developed methods in clinical practise; there is a lack of studies on Vas cular dementia, Frontotemporal dementia, Parkinson’s disease and Huntington’s disease.
Source: Health and Technology - Category: Information Technology Source Type: research