From Pixels to Prognosis: AI-Driven Insights into Neurodegenerative Diseases

Main Article Content

Ali Ganjizadeh, M.D. Yujia Wei, M.D., Ph.D. Bradley J. Erickson, M.D., Ph.D.

Abstract

Neurodegenerative diseases pose significant challenges in diagnosis and management due to their progressive nature and overlapping clinical presentations. Recent advancements in artificial intelligence, particularly machine learning, and deep learning techniques, have shown promising results in improving the diagnostic accuracy and evaluation of these conditions. This review explores the cutting-edge applications of these techniques in the diagnosis and evaluation of Parkinson's Disease, Multiple System Atrophy, Dementia with Lewy Bodies, and Progressive Supranuclear Palsy since they share similar clinical presentation in their initial period. By examining the latest research and advancements, we highlight the potential of these AI-driven approaches to revolutionize the field of neurodegenerative disease management, enhance diagnostic accuracy, enable early intervention, and ultimately improve patient outcomes.

Keywords: Neurodegenerative Diseases, Artificial Intelligence, Machine Learning, Deep Learning, Diagnostic Accuracy, Parkinson's Disease, Multiple System Atrophy, Dementia with Lewy Bodies, Progressive Supranuclear Palsy, Early Intervention, Patient Outcomes, AI-Driven Diagnosis

Article Details

How to Cite
GANJIZADEH, Ali; WEI, Yujia; ERICKSON, Bradley J.. From Pixels to Prognosis: AI-Driven Insights into Neurodegenerative Diseases. Medical Research Archives, [S.l.], v. 12, n. 6, june 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/5512>. Date accessed: 22 july 2024. doi: https://doi.org/10.18103/mra.v12i6.5512.
Section
Review Articles

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