The Intersections of Artificial Intelligence, Brain Imaging Tools and Diagnostics for Neurodegenerative Diseases

Authors

  • Anish Jayaraj Evergreen Valley High School

DOI:

https://doi.org/10.47611/jsrhs.v12i3.5077

Keywords:

Artificial Intelligence, Machine Learning, Neurodegenerative Diseases, Cognition, Brain Imaging Technology, MRI, PET, Parkinson's Disease, Alzheimer's Disease, Huntington's Disease, Ethics

Abstract

This paper explores the fascinating topics of artificial intelligence (AI), brain imaging tools, and diagnostics for neurodegenerative diseases. By examining the advancements in AI algorithms and their integration with cutting-edge brain imaging technologies, this publication displays the potential of these approaches to revolutionize the diagnoses and understanding of neurodegenerative disorders. Through an analysis of recent studies, this paper highlights the significant progress made by utilizing AI-powered tools, enhancing the accuracy, efficiency, and early detection of conditions such as Alzheimer's, Parkinson's, and Huntington's diseases. Ultimately, this research underscores the transformative role that AI and brain imaging can play in the field of neurodegenerative disease diagnostics, paving the way for improved patient care and better outcomes.

In addition to exploring the current state of AI and brain imaging tools in neurodegenerative disease diagnostics, my paper also dives into the potential future applications and challenges in this rapidly evolving field. It discusses the ethics of AI-driven diagnostic methods, emphasizing the importance of ensuring patient privacy, informed consent, and equitable access to these technologies. 

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References or Bibliography

Davenport, Franca, et al. “Neurodegenerative Disease of the Brain: A Survey of Interdisciplinary Approaches.” Journal of The Royal Society Interface, vol. 20, no. 198, 2023, https://doi.org/10.1098/rsif.2022.0406.

Dixit, Shriniket, et al. “A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis.” Electronics, vol. 12, no. 4, 2023, p. 783, https://doi.org/10.3390/electronics12040783.

Drzezga, Alexander, et al. “Potential Clinical Applications of PET/MR Imaging in Neurodegenerative Diseases.” Journal of Nuclear Medicine, vol. 55, no. Supplement 2, 2014, https://doi.org/10.2967/jnumed.113.129254.

Emre Kazim 1, et al. “A High-Level Overview of AI Ethics.” Patterns, 10 Sept. 2021, www.sciencedirect.com/science/article/pii/S2666389921001574.

Ghaffari Laleh, Narmin, et al. “Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer.” Clinical Cancer Research, vol. 29, no. 2, 2022, pp. 316–323, https://doi.org/10.1158/1078-0432.ccr-22-0390.

Shuo Chen a, et al. “Recent Progress and Perspectives on SB2SE3-Based Photocathodes for Solar Hydrogen Production via Photoelectrochemical Water Splitting.” Journal of Energy Chemistry, 9 Sept. 2021, www.sciencedirect.com/science/article/abs/pii/S2095495621004940.

Silva-Spínola, Anuschka, et al. “The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence.” MDPI, 29 Jan. 2022, www.mdpi.com/2227-9059/10/2/315.

Ter Haar Romeny, Bart M. “Introduction to Artificial Intelligence in Medicine.” Artificial Intelligence in Medicine, 2022, pp. 75–97, https://doi.org/10.1007/978-3-030-64573-1_27.

Tory O. Frizzell a b, et al. “Artificial Intelligence in Brain MRI Analysis of Alzheimer’s Disease over the Past 12 Years: A Systematic Review.” Ageing Research Reviews, 28 Mar. 2022, www.sciencedirect.com/science/article/abs/pii/S1568163722000563.

Published

08-31-2023

How to Cite

Jayaraj, A. (2023). The Intersections of Artificial Intelligence, Brain Imaging Tools and Diagnostics for Neurodegenerative Diseases. Journal of Student Research, 12(3). https://doi.org/10.47611/jsrhs.v12i3.5077

Issue

Section

HS Review Projects