From Pixels to Prognosis: AI-Driven Insights into Neurodegenerative Diseases
Main Article Content
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: 21 dec. 2024.
doi: https://doi.org/10.18103/mra.v12i6.5512.
Section
Review Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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4. Brusa L, Ponzo V, Mastropasqua C, et al. Theta burst stimulation modulates cerebellar-cortical connectivity in patients with progressive supranuclear palsy. Brain Stimulat. 2014;7(1): 29-35. doi:10.1016/j.brs.2013.07.003
5. Persely A, Beszedics B, Paloczi K, et al. Analysis of Genetic and MRI Changes, Blood Markers, and Risk Factors in a Twin Pair Discordant of Progressive Supranuclear Palsy. Med Kaunas Lith. 2023;59(10). doi:10.3390/ medicina59101696
6. Wiblin L, Durcan R, Galna B, Lee M, Burn D. Clinical Milestones Preceding the Diagnosis of Multiple System Atrophy and Progressive Supranuclear Palsy: A Retrospective Cohort Study. J Mov Disord. 2019;12(3):177-183. doi:10.14802/jmd.19015
7. Calomino C, Quattrone A, Sarica A, et al. Neuroimaging correlates of postural instability in Progressive Supranuclear Palsy. Parkinsonism Relat Disord. 2023;113:105768. doi:10.1016/j.parkreldis.2023.105768
8. Krismer F, Seppi K, Wenning GK, et al. Abnormalities on structural MRI associate with faster disease progression in multiple system atrophy. Parkinsonism Relat Disord. 2019;58: 23-27. doi:10.1016/j.parkreldis.2018.08.004
9. Wenning GK, Stankovic I, Vignatelli L, et al. The Movement Disorder Society Criteria for the Diagnosis of Multiple System Atrophy. Mov Disord Off J Mov Disord Soc. 2022;37(6): 1131-1148. doi:10.1002/mds.29005
10. Przedborski S, Vila M, Jackson-Lewis V. Series Introduction: Neurodegeneration: What is it and where are we? J Clin Invest. 2003;111(1):3-10. doi:10.1172/JCI17522
11. Yao Z, Wang H, Yan W, et al. Artificial intelligence-based diagnosis of Alzheimer’s disease with brain MRI images. Eur J Radiol. 2023;165:110934. doi:10.1016/j.ejrad.2023.110934
12. Borchert RJ, Azevedo T, Badhwar A, et al. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement. 2023; 19(12):5885-5904. doi:10.1002/alz.13412
13. Kanatani Y, Sato Y, Nemoto S, Ichikawa M, Onodera O. Improving the Accuracy of Diagnosis for Multiple-System Atrophy Using Deep Learning-Based Method. Biology. 2022; 11(7):951. doi:10.3390/biology11070951
14. Smits M. MRI biomarkers in neuro-oncology. Nat Rev Neurol. 2021;17(8):486-500. doi:10.1038/s41582-021-00510-y
15. Fischer CE, Qian W, Schweizer TA, et al. Determining the impact of psychosis on rates of false-positive and false-negative diagnosis in Alzheimer’s disease. Alzheimers Dement Transl Res Clin Interv. 2017;3(3):385-392. doi:10.1016/j.trci.2017.06.001
16. Sekiya H, Koga S, Murakami A, et al. Validation Study of the MDS Criteria for the Diagnosis of Multiple System Atrophy in the Mayo Clinic Brain Bank. Neurology. 2023;101 (24):e2460-e2471. doi:10.1212/WNL.0000000000207905
17. Furuta M, Sato M, Tsukagoshi S, Tsushima Y, Ikeda Y. Criteria-unfulfilled multiple system atrophy at an initial stage exhibits laterality of middle cerebellar peduncles. J Neurol Sci. 2022;438:120281. doi:10.1016/j.jns.2022.120281
18. Miyoshi F, Kanasaki Y, Shinohara Y, et al. Significance of combined use of MRI and perfusion SPECT for evaluation of multiple system atrophy, cerebellar type. Acta Radiol Stockh Swed 1987. 2016;57(6):742-749. doi: 10.1177/0284185115598810
19. Sakamoto F, Shiraishi S, Kitajima M, et al. Diagnostic Performance of (123)I-FPCIT SPECT Specific Binding Ratio in Progressive Supranuclear Palsy: Use of Core Clinical Features and MRI for Comparison. AJR Am J Roentgenol. 2020;215(6):1443-1448. doi:10. 2214/AJR.19.22436
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