Exploring the Role of Natural Learning Processing in Alzheimer's Disease Research and Prediction DOI

Yusra Ashfaque Ali,

P. N. Pathak, Nitu Dogra

et al.

Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 419 - 432

Published: June 28, 2024

Alzheimer's disease (AD) is a neurodegenerative disorder causing memory loss, cognitive decline, and behavioral changes, affecting over 35 million globally. Early detection crucial but challenging due to subtle symptoms, lack of biomarkers, stigma, symptom variability. diagnosis enables management, access treatments slowing progression, enhancing cognition, improving quality life. It facilitates planning for safety reduces caregiver burden through support services. Moreover, it increases understanding the informed decision-making. benefits healthcare systems by optimizing resource allocation patient outcomes. Overall, early identification vital care life patients families. Finding equilibrium between exploiting NLP power in predicting handling sensitive textual data responsibly one important topics dealt with this chapter.

Language: Английский

Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review DOI Creative Commons
Ghazala Hcini, Imen Jdey, Habib Dhahri

et al.

Neural Processing Letters, Journal Year: 2024, Volume and Issue: 56(3)

Published: April 24, 2024

Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides comprehensive analysis the use deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), classification AD using brain imaging data. While previous reviews have covered similar topics, this offers unique perspective by providing detailed comparison CNN ViT classification, highlighting strengths limitations each approach. Additionally, presents an updated thorough most recent studies in field, including latest advancements architectures, training methods, performance evaluation metrics. Furthermore, discusses ethical considerations challenges associated with models such as need interpretability potential bias. By addressing these issues, aims to provide valuable insights future research clinical applications, ultimately advancing field techniques.

Language: Английский

Citations

7
Introduction to Alzheimer's Disease, Biomarkers, and the AI Revolution DOI
Bancha Yingngam

Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23

Published: June 28, 2024

Alzheimer's disease (AD) is a progressive neurodegenerative that results in steady decline cognitive ability and memory function. As society ages, the need for an optimum AD management strategy becomes more important. This chapter analyzes stage-construction etiology escalating symptoms of discovered throughout this review, as well identification barrier to precise diagnosis. The artificial intelligence achieve quicker detection through machine learning, data analytics, predictive modeling also being considered. Therefore, employing AI AD-related studies novel approach enhancing patient outcomes. Proper diagnosis parallel increased probability many parameters one most difficult moments identify. However, use evaluation sensor network technologies big analysis has advanced, preventive instruments can be used. Thus, technology gives humanity hope stop or, at very least, slow down tragedy.

Language: Английский

Citations

1
Global Initiatives and Collaborations in AI for Alzheimer's Disease DOI

A. Chandrashekhar,

Nikhat Parveen,

A. Muthumari

et al.

Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 342 - 355

Published: June 28, 2024

This summary discusses the significance of global initiatives and collaborations in field artificial intelligence (AI) for prognosis remedy Alzheimer's ailment. disease is a debilitating neurodegenerative ailment that affects tens millions people worldwide, its incidence expected to growth with aging populace. AI has emerged as promising tool early detection, correct prognosis, customized treatment disorder. However, fully harness capacity AI, interdisciplinary worldwide projects are critical. abstract sheds mild on modern nation studies sickness, highlighting important thing demanding situations opportunities collaborations. It additionally information sharing open technological know-how advancing emphasizes want moral considerations inside development deployment technologies.

Language: Английский

Citations

1
Exploring the Role of Natural Learning Processing in Alzheimer's Disease Research and Prediction DOI

Yusra Ashfaque Ali,

P. N. Pathak, Nitu Dogra

et al.

Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 419 - 432

Published: June 28, 2024

Alzheimer's disease (AD) is a neurodegenerative disorder causing memory loss, cognitive decline, and behavioral changes, affecting over 35 million globally. Early detection crucial but challenging due to subtle symptoms, lack of biomarkers, stigma, symptom variability. diagnosis enables management, access treatments slowing progression, enhancing cognition, improving quality life. It facilitates planning for safety reduces caregiver burden through support services. Moreover, it increases understanding the informed decision-making. benefits healthcare systems by optimizing resource allocation patient outcomes. Overall, early identification vital care life patients families. Finding equilibrium between exploiting NLP power in predicting handling sensitive textual data responsibly one important topics dealt with this chapter.

Language: Английский

Citations

0