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

Bazarbekov, Ikram; Razaque, Abdul; Ipalakova, Madina; Yoo, Joon; Assipova, Zhanna; Almisreb, Ali

doi:10.1016/j.bspc.2024.106023

Cited by 4 publications

(2 citation statements)

References 91 publications

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“…Similarly, AI can provide an opportunity to reach these populations by offering remote assessments through digital tools such as chatbots or avatars [ 246 ]. In addition, the use of AI techniques (e.g., machine learning algorithms) may assist clinicians to interpret neuropsychological data, making diagnostic decisions and predicting cognitive outcomes [ 247 , 248 , 249 ]. Ultimately, these novel tools for evaluating and analyzing information can improve personalized assessment and intervention strategies.…”

Section: Future Directionsmentioning

confidence: 99%

Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights

Alzola,

Carnero,

Bermejo-Pareja

et al. 2024

JCM

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Dementia remains an underdiagnosed syndrome, and there is a need to improve the early detection of cognitive decline. This narrative review examines the role of neuropsychological assessment in the characterization of cognitive changes associated with dementia syndrome at different states. The first section describes the early indicators of cognitive decline and the major barriers to their identification. Further, the optimal cognitive screening conditions and the most widely accepted tests are described. The second section analyzes the main differences in cognitive performance between Alzheimer’s disease and other subtypes of dementia. Finally, the current challenges of neuropsychological assessment in aging/dementia and future approaches are discussed. Essentially, we find that current research is beginning to uncover early cognitive changes that precede dementia, while continuing to improve and refine the differential diagnosis of neurodegenerative disorders that cause dementia. However, neuropsychology faces several barriers, including the cultural diversity of the populations, a limited implementation in public health systems, and the adaptation to technological advances. Nowadays, neuropsychological assessment plays a fundamental role in characterizing cognitive decline in the different stages of dementia, but more efforts are needed to develop harmonized procedures that facilitate its use in different clinical contexts and research protocols.

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Section: Future Directionsmentioning

confidence: 99%

Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights

Alzola,

Carnero,

Bermejo-Pareja

et al. 2024

JCM

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“…The band δ (0.5 ÷ 4 Hz) signals slow brain activity linked to cortical damage, θ (4 ÷ 8 Hz) indicates transitions between sleep and wakefulness suggesting potential dysfunctions, α (8 ÷ 12 Hz) is associated with resting states and reflects the alteration of brain organization in AD, and β (12 ÷ 30 Hz) highlights levels of attention and mental activity, which is useful for observing cognitive changes in the patient [32,33]. Finally, the γ rhythm, above 30 Hz, is associated with complex cognitive processes such as object recognition and meaning attribution, and it is mainly detectable in the frontal regions [32][33][34][35][36][37]. Detailed EEG analysis, which includes the observation of specific changes in frequency bands, helps define a neurophysiological profile of AD [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Versaci,

La Foresta

2024

Signals

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Effective management of EEG artifacts is pivotal for accurate neurological diagnostics, particularly in detecting early stages of Alzheimer’s disease. This review delves into the cutting-edge domain of fuzzy logic techniques, emphasizing intuitionistic fuzzy systems, which offer refined handling of uncertainties inherent in EEG data. These methods not only enhance artifact identification and removal but also integrate seamlessly with other AI technologies to push the boundaries of EEG analysis. By exploring a range of approaches from standard protocols to advanced machine learning models, this paper provides a comprehensive overview of current strategies and emerging technologies in EEG artifact management. Notably, the fusion of fuzzy logic with neural network models illustrates significant advancements in distinguishing between genuine neurological activity and noise. This synthesis of technologies not only improves diagnostic accuracy but also enriches the toolset available to researchers and clinicians alike, facilitating earlier and more precise identification of neurodegenerative diseases. The review ultimately underscores the transformative potential of integrating diverse computational techniques, setting a new standard in EEG analysis and paving the way for future innovations in medical diagnostics.

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Local/Global explainability empowered expert-involved frameworks for essential tremor action recognition

Zhang,

Zhu,

et al. 2024

Biomedical Signal Processing and Control

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

Cited by 4 publications

References 91 publications

Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights

Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Local/Global explainability empowered expert-involved frameworks for essential tremor action recognition

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