Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Abstract

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

Reference theories and future perspectives on robot-assisted rehabilitation in people with neurological conditions: A scoping review and recommendations from the Italian Consensus Conference on Robotics in Neurorehabilitation (CICERONE)

“…In this regard, it is paramount to enforce the synergy between rehabilitation professionals and engineers, by designing shared lines of research and development, both at national and international level. One of the main achievable results of such a collaboration is the possibility of embedding artificial intelligence into home assistant services, exoskeletons, virtual reality applications and also for the management of severe cognitive and mental diseases, such as Alzheimer's disease (Battista, Salvatore, Berlingeri, Cerasa, & Castiglioni, 2020;Fabrizio, Termine, Caltagirone, & Sancesario, 2021;Liu et al, 2018;Seo, Jang, & Lee, 2022). Finally, the future generations of robots should be able to express empathy during the interaction with patients, which represents one of the most challenging edges of technological advancement.…”

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

“…However, until the models are implemented in real-world clinical trials, their validity are called into question. Further studies need to focus on improving the efficiency of AI for AD clinical trials (Seo et al, 2022). For instance, there is a need for greater external validation of the models, a need for greater quantity and quality of AD data, a need for more diversity, and a need for creating AI models based on multimodal clinical data (Acosta et al, 2022) to help bridge the translational gap.…”

Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease

“…Here, the 531 of the 638 sMRI images of AD subjects are selected for the experiment, because the remaining 107 images have no corresponding MMSE values. We explore the frequent item-sets consisting of the neurodegenerative regions in each level (normal: MMSE ∈ [27,30]; mild: MMSE ∈ [21,26]; moderate: MMSE ∈ [10,20]; severe: MMSE ∈ [0, 9]) to analyze the process of neurodegeneration at different stages of AD. As summarized in Table 7, in the early stage of cognitive impairment (MMSE ∈ [27,30]), L.mAmyg and L.NAC have been neurodegenerative in most (over 83%) AD sMRI images.…”

“…[19] The situation might be a reason why little attention has been paid to analyzing patterned pathological progression in AD based on DL models rather than targeting diagnostic classification tasks. [20][21][22][23] Some studies have started to extract atrophy features [24] or patterns [25] from sMRI to derive the brain age. In line with this effort, our recent study [26] has developed a DL model (2-dimensional convolutional neural network) to identify the critical discriminative brain regions for AD recognition.…”