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

Abstract

Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

FDG‐PET markers of heterogeneity and different risk of progression in amnestic MCI

“…FDG‐based brain age prediction showed a better performance than MRI in predicting chronological age, supporting the role of FDG‐PET hypometabolism as a more sensitive measure of aging trajectories 17 . The main challenge for the application of AI in the clinical routine is the overfitting that arises when a model is too dependent on a training dataset and is not able to provide good classifications in new clinical data 74 . Furthermore, the population heterogeneity could affect the training dataset and thus the performance of the algorithms 74 .…”

“… 17 The main challenge for the application of AI in the clinical routine is the overfitting that arises when a model is too dependent on a training dataset and is not able to provide good classifications in new clinical data. 74 Furthermore, the population heterogeneity could affect the training dataset and thus the performance of the algorithms. 74 Our results, disentangling aMCI heterogeneity and providing hypometabolic hallmarks, at single subject level, for each identified subtype, might serve for future diagnostic procedures, for example, innovative computational pipelines.…”

“… 74 Furthermore, the population heterogeneity could affect the training dataset and thus the performance of the algorithms. 74 Our results, disentangling aMCI heterogeneity and providing hypometabolic hallmarks, at single subject level, for each identified subtype, might serve for future diagnostic procedures, for example, innovative computational pipelines.…”

Generation of Conventional 18F-FDG PET Images from 18F-Florbetaben PET Images Using Generative Adversarial Network: A Preliminary Study Using ADNI Dataset

“…AI can also help analyze and interpret complex and large amounts of information from the brain. Most AI-based research in AD focuses on developing AI algorithms for the classification or diagnosis of AD and for developing biomarkers for the early detection of AD [ 39 ]. The current study focused on image-to-image translation using deep learning to decrease the clinical burden associated with obtaining multimodality imaging.…”

Investigating the Potential Impact of Air Pollution on Alzheimer’s Disease and the Utility of Multidimensional Imaging for Early Detection

“…These issues may be resolved by the rapidly developing field of using AI in conjunction with neuroimaging to diagnose AD. AI has the capacity to integrate complicated multimodal data and enhance the precision of biomarker-based testing, and it holds great promise for providing reliable and widely available early AD diagnosis. , STRING and gene ontology techniques are thus employed to evaluate novel gene candidates identified by artificial intelligence in the frontal brain and cerebellum of AD patients . One of the essential techniques to produce input information on the available EEG data and aid in the differentiation of AD, MCI, and HC persons is generative adversarial networks and variational auto-encoder networks .…”