Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Abstract

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Computational Intelligence in Healthcare

“…Based on results emerging in the present analysis, we can conclude that our ensemble classification framework, based on DTI features, is effective to improve HC/AD classification performances, and that ensembles including FA and MD are the best performing, confirming their role in the literature as most effective DTI measures for AD detection [57][58][59][60]. Moreover, although artificial data balancing attenuates the benefits of ensemble learning, the ensemble-based strategy generates significant improvements in the classification sensitivity and accuracy with respect to the general concatenation of all features into a high-dimensional vector.…”

The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review

“…An additional systematic review was identified through checking the list of the included reviews. In total, 15 systematic reviews were included in the current review [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] .…”

Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia

“…Several studies have proposed machine learning approaches for the classification of MCI and AD ( Gray et al, 2012 ; Wee et al, 2012 ; Nozadi et al, 2018 ; Billeci et al, 2020 ; Shi and Liu, 2020 ; Syaifullah et al, 2020 ; Sheng et al, 2022 ). These have utilized either one or combination of neuropsychological test scores, neuroimaging, and biofluids.…”