Journal of Angiotherapy
Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
  1. You are here:
  2. Home
  3. Journals
  4. Biological Sciences
RESEARCH ARTICLE   (Open Access)
Contents Vol 8 (8)

Exploring Angiogenesis and Artificial Intelligence in Alzheimer’s Disease: Implications for Early Detection and Therapeutic Strategies

S. Alagumuthukrishnan 1* , Ganga Sanuvala 1, Bibiana Jenifer J 2, M.J.D Ebinezer 3, B Veda Vidhya 4, Srithar S 5

+ Author Affiliations

Journal of Angiotherapy 8 (8) 1-10 https://doi.org/10.25163/angiotherapy.889851

Submitted: 17 June 2024 Revised: 10 August 2024  Published: 11 August 2024 

Abstract

Background: Alzheimer’s disease (AD) presents a significant global health challenge, affecting millions and imposing substantial socioeconomic burdens, with healthcare costs exceeding US$100 billion annually. The disease's complexity is underscored by its slow progression and the pivotal role of amyloid plaques in its pathology. Despite advancements in understanding AD, critical questions regarding the mechanisms of amyloid plaque development and their relationship with neuroinflammation and angiogenesis remain. Methods: This study employed advanced imaging techniques and artificial intelligence (AI) to investigate the interplay between amyloid beta (Aβ) accumulation, neuroinflammation, and angiogenesis in AD. Magnetic resonance imaging (MRI) facilitated detailed assessments of brain structure alterations, while machine learning models enhanced diagnostic accuracy by analyzing imaging data and identifying patterns linked to disease progression. Results: The findings revealed a significant association between Aβ deposition and increased angiogenesis, contributing to neurovascular dysfunction and exacerbating neuroinflammation. AI-driven analyses demonstrated improved diagnostic capabilities, detecting early changes in brain structure associated with mild cognitive impairment (MCI) and AD. Specifically, convolutional neural networks (CNNs) such as AlzheimerNet achieved remarkable accuracy in distinguishing AD from other neurodegenerative conditions. Conclusion: This research underscores the multifaceted nature of AD, highlighting the critical roles of amyloid plaques, neuroinflammation, and angiogenesis. The integration of AI and advanced imaging modalities offers promising avenues for early diagnosis and intervention, potentially transforming patient management strategies. Continued exploration of these pathways may yield effective therapeutic targets to mitigate the progression of AD and enhance patient outcomes.

Keywords: Alzheimer’s Disease, Angiogenesis, Artificial Intelligence, Early Detection, Neuroinflammation

References

Ahmad, A., Patel, V., Xiao, J., & Khan, M. M. (2020). The role of neurovascular system in neurodegenerative diseases. Molecular neurobiology, 57, 4373-4393. https://doi.org/10.1007/s12035-020-02023-z

Wu, A., Lee, D., & Xiong, W. C. (2023). Lactate metabolism, signaling, and function in brain development, synaptic plasticity, angiogenesis, and neurodegenerative diseases. International Journal of Molecular Sciences, 24(17), 13398. https://doi.org/10.3390/ijms241713398

Fang, Y. C., Hsieh, Y. C., Hu, C. J., & Tu, Y. K. (2023). Endothelial dysfunction in neurodegenerative diseases. International journal of molecular sciences, 24(3), 2909. https://doi.org/10.3390/ijms24032909

Lehrer, S., & Rheinstein, P. H. (2022). Vaccination reduces risk of Alzheimer’s disease, Parkinson’s disease, and other neurodegenerative disorders. Discovery medicine, 34(172), 97.

Yu, X., Ji, C., & Shao, A. (2020). Neurovascular unit dysfunction and neurodegenerative disorders. Frontiers in neuroscience, 14, 334. https://doi.org/10.3389/fnins.2020.00334

Fasoli, S., Bettin, I., Montioli, R., Fagagnini, A., Peterle, D., Laurents, D. V., & Gotte, G. (2021). Dimerization of human angiogenin and of variants involved in neurodegenerative diseases. International Journal of Molecular Sciences, 22(18), 10068. https://doi.org/10.3390/ijms221810068

Bowirrat, A. (2022). Immunosenescence and aging: neuroinflammation is a prominent feature of Alzheimer’s disease and is a likely contributor to neurodegenerative disease pathogenesis. Journal of personalized medicine, 12(11), 1817. https://doi.org/10.3390/jpm12111817

Abduljawad, A. A., Elawad, M. A., Elkhalifa, M. E. M., Ahmed, A., Hamdoon, A. A. E., Salim, L. H. M., ... & Bungau, S. (2022). Alzheimer’s disease as a major public health concern: Role of dietary saponins in mitigating neurodegenerative disorders and their underlying mechanisms. Molecules, 27(20), 6804. https://doi.org/10.3390/molecules27206804

Literature Review References

Wang, X., Jiao, B., Liu, H., Wang, Y., Hao, X., Zhu, Y., ... & Shen, L. (2022). Machine learning based on Optical Coherence Tomography images as a diagnostic tool for Alzheimer's disease. CNS Neuroscience & Therapeutics, 28(12), 2206-2217. https://doi.org/10.1111/cns.13963

Koga, S., Ikeda, A., & Dickson, D. W. (2022). Deep learning-based model for diagnosing Alzheimer's disease and tauopathies. Neuropathology and Applied Neurobiology, 48(1), e12759. https://doi.org/10.1111/nan.12759

Khaliq, F., Oberhauser, J., Wakhloo, D., & Mahajani, S. (2023). Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders. Neural Regeneration Research, 18(6), 1235-1242. https://doi.org/ 10.4103/1673-5374.355982

Sun, Q., Ni, J., Wei, M., Long, S., Li, T., Fan, D., ... & Tian, J. (2022). Plasma β-amyloid, tau, neurodegeneration biomarkers and inflammatory factors of probable Alzheimer’s disease dementia in Chinese individuals. Frontiers in Aging Neuroscience, 14, 963845. https://doi.org/10.3389/fnagi.2022.963845

Huseby, C. J., Delvaux, E., Brokaw, D. L., & Coleman, P. D. (2022). Blood transcript biomarkers selected by machine learning algorithm classify neurodegenerative diseases including Alzheimer’s disease. Biomolecules, 12(11), 1592. https://doi.org/10.3390/biom12111592

Bhatele, K. R., Jha, A., Kapoor, K., & Tiwari, D. (2022). Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases. Cognitive Neurodynamics, 16(6), 1361-1377. https://doi.org/10.1007/s11571-022-09787-1

kaggle.com, https://www.kaggle.com/code/hyunseokc/detecting-early-alzheimer-s#2.A-DATASET-DESCRIPTION

PDF
Abstract
Export Citation

View Dimensions


View Plumx


View Altmetric




Save
0
Citation
496
View

Share