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: 02 july 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.
References
1. Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med. 2021;117:102081. doi:10.1016/j. artmed.2021.102081
2. Kim J, Young GS, Willett AS, et al. Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects. Cerebellum Lond Engl. 2023;22(6): 1098-1108. doi:10.1007/s12311-022-01472-7
3. Höglinger GU, Huppertz HJ, Wagenpfeil S, et al. Tideglusib reduces progression of brain atrophy in progressive supranuclear palsy in a randomized trial. Mov Disord Off J Mov Disord Soc. 2014;29(4):479-487. doi:10.1002/ mds.25815
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
20. Bae YJ, Kim JM, Kim E, et al. Loss of Nigral Hyperintensity on 3 Tesla MRI of Parkinsonism: Comparison With (123) I-FP-CIT SPECT. Mov Disord Off J Mov Disord Soc. 2016;31(5):684-692. doi:10.1002/mds.26584
21. Whitwell JL, Tosakulwong N, Schwarz CG, et al. MRI Outperforms [18F]AV-1451 PET as a Longitudinal Biomarker in Progressive Supranuclear Palsy. Mov Disord Off J Mov Disord Soc. 2019;34(1):105-113. doi:10.1002/ mds.27546
22. Sun F, Lyu J, Jian S, Qin Y, Tang X. Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline. Eur Radiol. 2023;33(12):8844-8853. doi:10.1007/s00330-023-09979-1
23. Bateman TM. Advantages and disadvantages of PET and SPECT in a busy clinical practice. J Nucl Cardiol. 2012;19(1):3-11. doi:10.1007/s12350-011-9490-9
24. Martín-Noguerol T, Casado-Verdugo OL, Beltrán LS, Aguilar G, Luna A. Role of advanced MRI techniques for sacroiliitis assessment and quantification. Eur J Radiol. 2023;163:110793. doi:10.1016/j.ejrad.2023.110793
25. Buckner RL, Krienen FM, Yeo BTT. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci. 2013;16(7):832-837. doi:10.1038/nn.3423
26. Zhang Y, Dong Z, Wang S, Phillips P, Ji G. Prediction of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images by 3D discrete wavelet transform and artificial neural network. Alzheimers Dement. 2015;11(7, Supplement): P78-P79. doi:10.1016/j.jalz.2015.06.136
27. Eickhoff SB, Yeo BTT, Genon S. Imaging-based parcellations of the human brain. Nat Rev Neurosci. 2018;19(11):672-686. doi:10.10 38/s41583-018-0071-7
28. Madetko N, Alster P, Kutyłowski M, et al. Is MRPI 2.0 More Useful than MRPI and M/P Ratio in Differential Diagnosis of PSP-P with Other Atypical Parkinsonisms? J Clin Med. 2022;11(10):2701. doi:10.3390/jcm11102701
29. Eraslan C, Acarer A, Guneyli S, et al. MRI evaluation of progressive supranuclear palsy: differentiation from Parkinson’s disease and multiple system atrophy. Neurol Res. 2019;41 (2):110-117. doi:10.1080/01616412.2018.1541115
30. Meijer FJ, Aerts MB, Abdo WF, et al. Contribution of routine brain MRI to the differential diagnosis of parkinsonism: a 3-year prospective follow-up study. J Neurol. 2012;259(5):929-935. doi:10.1007/s00415-01 1-6280-x
31. Constantinides VC, Paraskevas GP, Velonakis G, Toulas P, Stefanis L, Kapaki E. Midbrain morphology in idiopathic normal pressure hydrocephalus: A progressive supranuclear palsy mimic. Acta Neurol Scand. 2020;141(4):328-334. doi:10.1111/ane.13205
32. Heim B, Krismer F, Seppi K. Differentiating PSP from MSA using MR planimetric measurements: a systematic review and meta-analysis. J Neural Transm Vienna Austria 1996. 2021;128(10):1497-1505. doi:10.1007/s00702-021-02362-8
33. Shinde S, Prasad S, Saboo Y, et al. Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. NeuroImage Clin. 2019;22: 101748. doi:10.1016/j.nicl.2019.101748
34. Picillo M, Tepedino MF, Abate F, et al. Uncovering clinical and radiological asymmetry in progressive supranuclear palsy-Richardson’s syndrome. Neurol Sci Off J Ital Neurol Soc Ital Soc Clin Neurophysiol. 2022;43(6):3677-3682. doi:10.1007/s10072-022-05919-x
35. Muñoz-Lopetegi A, Berenguer J, Iranzo A, et al. Magnetic resonance imaging abnormalities as a marker of multiple system atrophy in isolated rapid eye movement sleep behavior disorder. Sleep. 2021;44(1):zsaa089. doi:10.1093/sleep/zsaa089
36. Wattjes MP, Huppertz HJ, Mahmoudi N, et al. Brain MRI in Progressive Supranuclear Palsy with Richardson’s Syndrome and Variant Phenotypes. Mov Disord Off J Mov Disord Soc. 2023;38(10):1891-1900. doi:10.1002/md s.29527
37. Page I, Gaillard F. Descriptive neuroradiology: beyond the hummingbird. Pract Neurol. 2020;20(6):463-471. doi:10. 1136/practneurol-2020-002526
38. Krismer F, Péran P, Beliveau V, et al. Progressive Brain Atrophy in Multiple System Atrophy: A Longitudinal, Multicenter, Magnetic Resonance Imaging Study. Mov Disord Off J Mov Disord Soc. 2024;39(1):119-129. doi:10.1002/mds.29633
39. Bang J, Lobach IV, Lang AE, et al. Predicting disease progression in progressive supranuclear palsy in multicenter clinical trials. Parkinsonism Relat Disord. 2016;28:41-48. doi:10.1016/j.parkreldis.2016.04.014
40. Tsai RM, Lobach I, Bang J, et al. Clinical correlates of longitudinal brain atrophy in progressive supranuclear palsy. Parkinsonism Relat Disord. 2016;28:29-35. doi:10.1016/ j.parkreldis.2016.04.006
41. Wang Y, He N, Zhang C, et al. An automatic interpretable deep learning pipeline for accurate Parkinson’s disease diagnosis using quantitative susceptibility mapping and T1-weighted images. Hum Brain Mapp. 2023;44(12):4426-4438. doi:10. 1002/ hbm.26399
42. Zhang J, Zhou C, Xiao X, et al. Magnetic resonance imaging image analysis of the therapeutic effect and neuroprotective effect of deep brain stimulation in Parkinson’s disease based on a deep learning algorithm. Int J Numer Methods Biomed Eng. 2022; 38(11):e3642. doi:10.1002/cnm.3642
43. Dünnwald M, Ernst P, Düzel E, Tönnies K, Betts MJ, Oeltze-Jafra S. Fully automated deep learning-based localization and segmentation of the locus coeruleus in aging and Parkinson’s disease using neuromelanin-sensitive MRI. Int J Comput Assist Radiol Surg. 2021;16(12):2129-2135. doi:10.1007/s11548-021-02528-5
44. Wu P, Zhao Y, Wu J, et al. Differential Diagnosis of Parkinsonism Based on Deep Metabolic Imaging Indices. J Nucl Med Off Publ Soc Nucl Med. 2022;63(11):1741-1747. doi:10.2967/jnumed.121.263029
45. Leung KH, Rowe SP, Pomper MG, Du Y. A three-stage, deep learning, ensemble approach for prognosis in patients with Parkinson’s disease. EJNMMI Res. 2021;11 (1):52. doi:10.1186/s13550-021-00795-6
46. Nilashi M, Abumalloh RA, Minaei-Bidgoli B, et al. Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning. J Healthc Eng. 2022; 2022:2793361. doi:10.1155/2022/2793361
47. Arslan J, Racoceanu D, Benke KK. Deep Learning Using Images of the Retina for Assessment of Severity of Neurological Dysfunction in Parkinson Disease. JAMA Ophthalmol. 2023;141(3):240-241. doi:10. 1001/jamaophthalmol.2022.6036
48. Kaur R, Motl RW, Sowers R, Hernandez ME. A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson’s Disease Gait Dysfunctions-A Deep Learning Approach. IEEE J Biomed Health Inform. 2023;27(1):190-201. doi:10.1109/JBHI.2022.3208077
49. Ahn S, Shin J, Song SJ, et al. Neurologic Dysfunction Assessment in Parkinson Disease Based on Fundus Photographs Using Deep Learning. JAMA Ophthalmol. 2023;141(3):234-240. doi:10.1001/jamaophthalmol.2022.5928
50. Koga S, Aoki N, Uitti RJ, et al. When DLB, PD, and PSP masquerade as MSA. Neurology. 2015;85(5):404-412. doi:10.1212/WNL.0000000000001807
51. Duchesne S, Rolland Y, Vérin M. Automated computer differential classification in Parkinsonian Syndromes via pattern analysis on MRI. Acad Radiol. 2009;16(1):61-70. doi:10.1016/j.acra.2008.05.024
52. Tsuda M, Asano S, Kato Y, Murai K, Miyazaki M. Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson’s disease using neural networks. J Neurol Sci. 2019;401:19-26. doi:10.1016/j.jns.2019.04.014
53. Rau A, Schröter N, Rijntjes M, et al. Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy. Eur Radiol. 2023;33(10):7160-7167. doi:10.1007/s00330-023-09665-2
54. Tan H, Luo B, Cong C. Using Deep Learning to differentiate between Parkinson’s Disease and Multiple System Atrophy based on PET and MRI images. In: IEEE Computer Society; 2023:3181-3187. doi:10.1109/BIBM5 8861.2023.10385520
55. Nemmi F, Pavy-Le Traon A, Phillips OR, et al. A totally data-driven whole-brain multimodal pipeline for the discrimination of Parkinson’s disease, multiple system atrophy and healthy control. NeuroImage Clin. 2019; 23:101858. doi:10.1016/j.nicl.2019.101858
56. Jucaite A, Cselényi Z, Kreisl WC, et al. Glia Imaging Differentiates Multiple System Atrophy from Parkinson’s Disease: A Positron Emission Tomography Study with [11C]PBR28 and Machine Learning Analysis. Mov Disord. 2022;37(1):119-129. doi:10.1002/mds.28814
57. Abos A, Baggio HC, Segura B, et al. Differentiation of multiple system atrophy from Parkinson’s disease by structural connectivity derived from probabilistic tractography. Sci Rep. 2019;9(1):16488. doi: 10.1038/s41598-019-52829-8
58. Coll L, Pareto D, Carbonell-Mirabent P, et al. Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI. NeuroImage Clin. 2023;38:103376. doi: 10.1016/j.nicl.2023.103376
59. Tupe-Waghmare P, Rajan A, Prasad S, Saini J, Pal PK, Ingalhalikar M. Radiomics on routine T1-weighted MRI can delineate Parkinson’s disease from multiple system atrophy and progressive supranuclear palsy. Eur Radiol. 2021;31(11):8218-8227. doi:10. 1007/s00330-021-07979-7
60. Bu S, Pang H, Li X, et al. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy. BMC Med Imaging. 2023;23(1):204. doi:10.1186/s1 2880-023-01169-1
61. Salvatore C, Cerasa A, Castiglioni I, et al. Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and Progressive Supranuclear Palsy. J Neurosci Methods. 2014;222:230-237. doi:1 0.1016/j.jneumeth.2013.11.016
62. Focke NK, Helms G, Scheewe S, et al. Individual voxel-based subtype prediction can differentiate progressive supranuclear palsy from idiopathic parkinson syndrome and healthy controls. Hum Brain Mapp. 2011;32(1 1):1905-1915. doi:10.1002/hbm.21161
63. Singh G, Samavedham L. Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: A case study on early-stage diagnosis of Parkinson disease. J Neurosci Methods. 2015;256:30-40. doi:10.1016/j.jneumeth.2015.08.011
64. Haller S, Badoud S, Nguyen D, Garibotto V, Lovblad KO, Burkhard PR. Individual Detection of Patients with Parkinson Disease using Support Vector Machine Analysis of Diffusion Tensor Imaging Data: Initial Results. Am J Neuroradiol. 2012;33(11):2123-2128. doi:10.3174/ajnr.A3126
65. Haller S, Badoud S, Nguyen D, et al. Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): initial results. Eur Radiol. 2013;23(1):12-19. doi:10.1 007/s00330-012-2579-y
66. Illán-Gala I, Nigro S, VandeVrede L, et al. Diagnostic Accuracy of Magnetic Resonance Imaging Measures of Brain Atrophy Across the Spectrum of Progressive Supranuclear Palsy and Corticobasal Degeneration. JAMA Netw Open. 2022;5(4):e229588. doi:10.1001/ jamanetworkopen.2022.9588
67. Wu P, Zhao Y, Wu J, et al. Differential diagnosis of parkinsonism based on deep metabolic imaging indices. J Nucl Med. Published online April 1, 2022. doi:10.2967/ jnumed.121.263029
68. Koga S, Zhou X, Dickson DW. Machine learning‐based decision tree classifier for the diagnosis of progressive supranuclear palsy and corticobasal degeneration. Neuropathol Appl Neurobiol. 2021;47(7):931-941. doi:10. 1111/nan.12710
69. Erickson BJ. Magician’s corner: 2. Optimizing a simple image classifier. Radiol Artif Intell. 2019;1(5):e190113. doi:10.1148/ ryai.2019190113
70. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics. 2017;37(2):505-515. doi:10.11 48/rg.2017160130
71. Combi R, Salsone M, Clara V, Ferini-Strambi L. Genetic Architecture and Molecular, Imaging and Prodromic Markers in Dementia With Lewy Bodies: State of the Art, Opportunities and Challenges. Int J Mol Sci. Published online 2021. doi:10.3390/ijms22083960
72. Suzuki Y, Suzuki M, Shigenobu K, et al. A prospective multicenter validation study of a machine learning algorithm classifier on quantitative electroencephalogram for differentiating between dementia with Lewy bodies and Alzheimer’s dementia. PloS One. 2022;17(3):e0265484. doi:10.1371/journal.pone.0265484
73. Katako A, Shelton P, Goertzen AL, et al. Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia. Sci Rep. 2018;8 (1):13236. doi:10.1038/s41598-018-31653-6
74. Perovnik M, Vo A, Nguyen N, et al. Automated differential diagnosis of dementia syndromes using FDG PET and machine learning. Front Aging Neurosci. 2022;14:1005 731. doi:10.3389/fnagi.2022.1005731
75. Lee J, Burkett BJ, Min HK, et al. Deep learning-based brain age prediction in normal aging and dementia. Nat Aging. 2022;2(5): 412-424. doi:10.1038/s43587-022-00219-7
76. Perovnik M, Tomše P, Jamšek J, Tang CC, Eidelberg D, Trošt M. Metabolic Brain Pattern in Dementia With Lewy Bodies: Relationship to Alzheimer’s Disease Topography. Neuroimage Clin. Published online 2022. doi:10.1016/j.nicl.2022.103080
77. Etsuko Imabayashi, Tsutomu Sawada, Daichi Sone, et al. Validation of the Cingulate Island Sign With Optimized Ratios for Discriminating Dementia With Lewy Bodies From Alzheimer’s Disease Using Brain Perfusion SPECT. Ann Nucl Med. Published online 2017. doi:10.1007/s12149-017-1181-4
78. Iizuka T, Fukasawa M, Kameyama M. Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies. Sci Rep. 2019;9(1):8944. doi:10.1038/s41598-019-45415-5
79. Ruffini G, Ibañez D, Castellano M, et al. Deep Learning With EEG Spectrograms in Rapid Eye Movement Behavior Disorder. Front Neurol. 2019;10:806. doi:10.3389/fneur .2019.00806
80. Jeong E. EEG-based Machine Learning Models for the Prediction of Phenoconversion Time and Subtype in Isolated Rapid Eye Movement Sleep Behavior Disorder. Sleep. Published online 2024. doi:10.1093/sleep/zsae031
81. Salman A, Lapidot I, Shufan E, Agbaria AH, Porat Katz BS, Mordechai S. Potential of infrared microscopy to differentiate between dementia with Lewy bodies and Alzheimer’s diseases using peripheral blood samples and machine learning algorithms. J Biomed Opt. 2020;25(4):1-15. doi:10.1117/1.JBO.25.4.046501
82. Dauwan M, van der Zande JJ, van Dellen E, et al. Random forest to differentiate dementia with Lewy bodies from Alzheimer’s disease. Alzheimers Dement Amst Neth. 2016;4:99-106. doi:10.1016/j.dadm.2016.07.003
83. Li Z, Guo W, Ding S, et al. Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods. Front Genet. 2022;13:880997. doi:10.3389/ fgene.2022.880997
84. Yamada Y, Shinkawa K, Nemoto M, Ota M, Nemoto K, Arai T. Speech and language characteristics differentiate Alzheimer’s disease and dementia with Lewy bodies. Alzheimers Dement Amst Neth. 2022;14(1):e1 2364. doi:10.1002/dad2.12364
85. Faghani S, Moassefi M, Rouzrokh P, et al. Quantifying Uncertainty in Deep Learning of Radiologic Images. Radiology. 2023;308(2):e2 22217. doi:10.1148/radiol.222217
2. Kim J, Young GS, Willett AS, et al. Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects. Cerebellum Lond Engl. 2023;22(6): 1098-1108. doi:10.1007/s12311-022-01472-7
3. Höglinger GU, Huppertz HJ, Wagenpfeil S, et al. Tideglusib reduces progression of brain atrophy in progressive supranuclear palsy in a randomized trial. Mov Disord Off J Mov Disord Soc. 2014;29(4):479-487. doi:10.1002/ mds.25815
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
20. Bae YJ, Kim JM, Kim E, et al. Loss of Nigral Hyperintensity on 3 Tesla MRI of Parkinsonism: Comparison With (123) I-FP-CIT SPECT. Mov Disord Off J Mov Disord Soc. 2016;31(5):684-692. doi:10.1002/mds.26584
21. Whitwell JL, Tosakulwong N, Schwarz CG, et al. MRI Outperforms [18F]AV-1451 PET as a Longitudinal Biomarker in Progressive Supranuclear Palsy. Mov Disord Off J Mov Disord Soc. 2019;34(1):105-113. doi:10.1002/ mds.27546
22. Sun F, Lyu J, Jian S, Qin Y, Tang X. Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline. Eur Radiol. 2023;33(12):8844-8853. doi:10.1007/s00330-023-09979-1
23. Bateman TM. Advantages and disadvantages of PET and SPECT in a busy clinical practice. J Nucl Cardiol. 2012;19(1):3-11. doi:10.1007/s12350-011-9490-9
24. Martín-Noguerol T, Casado-Verdugo OL, Beltrán LS, Aguilar G, Luna A. Role of advanced MRI techniques for sacroiliitis assessment and quantification. Eur J Radiol. 2023;163:110793. doi:10.1016/j.ejrad.2023.110793
25. Buckner RL, Krienen FM, Yeo BTT. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci. 2013;16(7):832-837. doi:10.1038/nn.3423
26. Zhang Y, Dong Z, Wang S, Phillips P, Ji G. Prediction of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images by 3D discrete wavelet transform and artificial neural network. Alzheimers Dement. 2015;11(7, Supplement): P78-P79. doi:10.1016/j.jalz.2015.06.136
27. Eickhoff SB, Yeo BTT, Genon S. Imaging-based parcellations of the human brain. Nat Rev Neurosci. 2018;19(11):672-686. doi:10.10 38/s41583-018-0071-7
28. Madetko N, Alster P, Kutyłowski M, et al. Is MRPI 2.0 More Useful than MRPI and M/P Ratio in Differential Diagnosis of PSP-P with Other Atypical Parkinsonisms? J Clin Med. 2022;11(10):2701. doi:10.3390/jcm11102701
29. Eraslan C, Acarer A, Guneyli S, et al. MRI evaluation of progressive supranuclear palsy: differentiation from Parkinson’s disease and multiple system atrophy. Neurol Res. 2019;41 (2):110-117. doi:10.1080/01616412.2018.1541115
30. Meijer FJ, Aerts MB, Abdo WF, et al. Contribution of routine brain MRI to the differential diagnosis of parkinsonism: a 3-year prospective follow-up study. J Neurol. 2012;259(5):929-935. doi:10.1007/s00415-01 1-6280-x
31. Constantinides VC, Paraskevas GP, Velonakis G, Toulas P, Stefanis L, Kapaki E. Midbrain morphology in idiopathic normal pressure hydrocephalus: A progressive supranuclear palsy mimic. Acta Neurol Scand. 2020;141(4):328-334. doi:10.1111/ane.13205
32. Heim B, Krismer F, Seppi K. Differentiating PSP from MSA using MR planimetric measurements: a systematic review and meta-analysis. J Neural Transm Vienna Austria 1996. 2021;128(10):1497-1505. doi:10.1007/s00702-021-02362-8
33. Shinde S, Prasad S, Saboo Y, et al. Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. NeuroImage Clin. 2019;22: 101748. doi:10.1016/j.nicl.2019.101748
34. Picillo M, Tepedino MF, Abate F, et al. Uncovering clinical and radiological asymmetry in progressive supranuclear palsy-Richardson’s syndrome. Neurol Sci Off J Ital Neurol Soc Ital Soc Clin Neurophysiol. 2022;43(6):3677-3682. doi:10.1007/s10072-022-05919-x
35. Muñoz-Lopetegi A, Berenguer J, Iranzo A, et al. Magnetic resonance imaging abnormalities as a marker of multiple system atrophy in isolated rapid eye movement sleep behavior disorder. Sleep. 2021;44(1):zsaa089. doi:10.1093/sleep/zsaa089
36. Wattjes MP, Huppertz HJ, Mahmoudi N, et al. Brain MRI in Progressive Supranuclear Palsy with Richardson’s Syndrome and Variant Phenotypes. Mov Disord Off J Mov Disord Soc. 2023;38(10):1891-1900. doi:10.1002/md s.29527
37. Page I, Gaillard F. Descriptive neuroradiology: beyond the hummingbird. Pract Neurol. 2020;20(6):463-471. doi:10. 1136/practneurol-2020-002526
38. Krismer F, Péran P, Beliveau V, et al. Progressive Brain Atrophy in Multiple System Atrophy: A Longitudinal, Multicenter, Magnetic Resonance Imaging Study. Mov Disord Off J Mov Disord Soc. 2024;39(1):119-129. doi:10.1002/mds.29633
39. Bang J, Lobach IV, Lang AE, et al. Predicting disease progression in progressive supranuclear palsy in multicenter clinical trials. Parkinsonism Relat Disord. 2016;28:41-48. doi:10.1016/j.parkreldis.2016.04.014
40. Tsai RM, Lobach I, Bang J, et al. Clinical correlates of longitudinal brain atrophy in progressive supranuclear palsy. Parkinsonism Relat Disord. 2016;28:29-35. doi:10.1016/ j.parkreldis.2016.04.006
41. Wang Y, He N, Zhang C, et al. An automatic interpretable deep learning pipeline for accurate Parkinson’s disease diagnosis using quantitative susceptibility mapping and T1-weighted images. Hum Brain Mapp. 2023;44(12):4426-4438. doi:10. 1002/ hbm.26399
42. Zhang J, Zhou C, Xiao X, et al. Magnetic resonance imaging image analysis of the therapeutic effect and neuroprotective effect of deep brain stimulation in Parkinson’s disease based on a deep learning algorithm. Int J Numer Methods Biomed Eng. 2022; 38(11):e3642. doi:10.1002/cnm.3642
43. Dünnwald M, Ernst P, Düzel E, Tönnies K, Betts MJ, Oeltze-Jafra S. Fully automated deep learning-based localization and segmentation of the locus coeruleus in aging and Parkinson’s disease using neuromelanin-sensitive MRI. Int J Comput Assist Radiol Surg. 2021;16(12):2129-2135. doi:10.1007/s11548-021-02528-5
44. Wu P, Zhao Y, Wu J, et al. Differential Diagnosis of Parkinsonism Based on Deep Metabolic Imaging Indices. J Nucl Med Off Publ Soc Nucl Med. 2022;63(11):1741-1747. doi:10.2967/jnumed.121.263029
45. Leung KH, Rowe SP, Pomper MG, Du Y. A three-stage, deep learning, ensemble approach for prognosis in patients with Parkinson’s disease. EJNMMI Res. 2021;11 (1):52. doi:10.1186/s13550-021-00795-6
46. Nilashi M, Abumalloh RA, Minaei-Bidgoli B, et al. Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning. J Healthc Eng. 2022; 2022:2793361. doi:10.1155/2022/2793361
47. Arslan J, Racoceanu D, Benke KK. Deep Learning Using Images of the Retina for Assessment of Severity of Neurological Dysfunction in Parkinson Disease. JAMA Ophthalmol. 2023;141(3):240-241. doi:10. 1001/jamaophthalmol.2022.6036
48. Kaur R, Motl RW, Sowers R, Hernandez ME. A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson’s Disease Gait Dysfunctions-A Deep Learning Approach. IEEE J Biomed Health Inform. 2023;27(1):190-201. doi:10.1109/JBHI.2022.3208077
49. Ahn S, Shin J, Song SJ, et al. Neurologic Dysfunction Assessment in Parkinson Disease Based on Fundus Photographs Using Deep Learning. JAMA Ophthalmol. 2023;141(3):234-240. doi:10.1001/jamaophthalmol.2022.5928
50. Koga S, Aoki N, Uitti RJ, et al. When DLB, PD, and PSP masquerade as MSA. Neurology. 2015;85(5):404-412. doi:10.1212/WNL.0000000000001807
51. Duchesne S, Rolland Y, Vérin M. Automated computer differential classification in Parkinsonian Syndromes via pattern analysis on MRI. Acad Radiol. 2009;16(1):61-70. doi:10.1016/j.acra.2008.05.024
52. Tsuda M, Asano S, Kato Y, Murai K, Miyazaki M. Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson’s disease using neural networks. J Neurol Sci. 2019;401:19-26. doi:10.1016/j.jns.2019.04.014
53. Rau A, Schröter N, Rijntjes M, et al. Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy. Eur Radiol. 2023;33(10):7160-7167. doi:10.1007/s00330-023-09665-2
54. Tan H, Luo B, Cong C. Using Deep Learning to differentiate between Parkinson’s Disease and Multiple System Atrophy based on PET and MRI images. In: IEEE Computer Society; 2023:3181-3187. doi:10.1109/BIBM5 8861.2023.10385520
55. Nemmi F, Pavy-Le Traon A, Phillips OR, et al. A totally data-driven whole-brain multimodal pipeline for the discrimination of Parkinson’s disease, multiple system atrophy and healthy control. NeuroImage Clin. 2019; 23:101858. doi:10.1016/j.nicl.2019.101858
56. Jucaite A, Cselényi Z, Kreisl WC, et al. Glia Imaging Differentiates Multiple System Atrophy from Parkinson’s Disease: A Positron Emission Tomography Study with [11C]PBR28 and Machine Learning Analysis. Mov Disord. 2022;37(1):119-129. doi:10.1002/mds.28814
57. Abos A, Baggio HC, Segura B, et al. Differentiation of multiple system atrophy from Parkinson’s disease by structural connectivity derived from probabilistic tractography. Sci Rep. 2019;9(1):16488. doi: 10.1038/s41598-019-52829-8
58. Coll L, Pareto D, Carbonell-Mirabent P, et al. Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI. NeuroImage Clin. 2023;38:103376. doi: 10.1016/j.nicl.2023.103376
59. Tupe-Waghmare P, Rajan A, Prasad S, Saini J, Pal PK, Ingalhalikar M. Radiomics on routine T1-weighted MRI can delineate Parkinson’s disease from multiple system atrophy and progressive supranuclear palsy. Eur Radiol. 2021;31(11):8218-8227. doi:10. 1007/s00330-021-07979-7
60. Bu S, Pang H, Li X, et al. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy. BMC Med Imaging. 2023;23(1):204. doi:10.1186/s1 2880-023-01169-1
61. Salvatore C, Cerasa A, Castiglioni I, et al. Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and Progressive Supranuclear Palsy. J Neurosci Methods. 2014;222:230-237. doi:1 0.1016/j.jneumeth.2013.11.016
62. Focke NK, Helms G, Scheewe S, et al. Individual voxel-based subtype prediction can differentiate progressive supranuclear palsy from idiopathic parkinson syndrome and healthy controls. Hum Brain Mapp. 2011;32(1 1):1905-1915. doi:10.1002/hbm.21161
63. Singh G, Samavedham L. Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: A case study on early-stage diagnosis of Parkinson disease. J Neurosci Methods. 2015;256:30-40. doi:10.1016/j.jneumeth.2015.08.011
64. Haller S, Badoud S, Nguyen D, Garibotto V, Lovblad KO, Burkhard PR. Individual Detection of Patients with Parkinson Disease using Support Vector Machine Analysis of Diffusion Tensor Imaging Data: Initial Results. Am J Neuroradiol. 2012;33(11):2123-2128. doi:10.3174/ajnr.A3126
65. Haller S, Badoud S, Nguyen D, et al. Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): initial results. Eur Radiol. 2013;23(1):12-19. doi:10.1 007/s00330-012-2579-y
66. Illán-Gala I, Nigro S, VandeVrede L, et al. Diagnostic Accuracy of Magnetic Resonance Imaging Measures of Brain Atrophy Across the Spectrum of Progressive Supranuclear Palsy and Corticobasal Degeneration. JAMA Netw Open. 2022;5(4):e229588. doi:10.1001/ jamanetworkopen.2022.9588
67. Wu P, Zhao Y, Wu J, et al. Differential diagnosis of parkinsonism based on deep metabolic imaging indices. J Nucl Med. Published online April 1, 2022. doi:10.2967/ jnumed.121.263029
68. Koga S, Zhou X, Dickson DW. Machine learning‐based decision tree classifier for the diagnosis of progressive supranuclear palsy and corticobasal degeneration. Neuropathol Appl Neurobiol. 2021;47(7):931-941. doi:10. 1111/nan.12710
69. Erickson BJ. Magician’s corner: 2. Optimizing a simple image classifier. Radiol Artif Intell. 2019;1(5):e190113. doi:10.1148/ ryai.2019190113
70. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics. 2017;37(2):505-515. doi:10.11 48/rg.2017160130
71. Combi R, Salsone M, Clara V, Ferini-Strambi L. Genetic Architecture and Molecular, Imaging and Prodromic Markers in Dementia With Lewy Bodies: State of the Art, Opportunities and Challenges. Int J Mol Sci. Published online 2021. doi:10.3390/ijms22083960
72. Suzuki Y, Suzuki M, Shigenobu K, et al. A prospective multicenter validation study of a machine learning algorithm classifier on quantitative electroencephalogram for differentiating between dementia with Lewy bodies and Alzheimer’s dementia. PloS One. 2022;17(3):e0265484. doi:10.1371/journal.pone.0265484
73. Katako A, Shelton P, Goertzen AL, et al. Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia. Sci Rep. 2018;8 (1):13236. doi:10.1038/s41598-018-31653-6
74. Perovnik M, Vo A, Nguyen N, et al. Automated differential diagnosis of dementia syndromes using FDG PET and machine learning. Front Aging Neurosci. 2022;14:1005 731. doi:10.3389/fnagi.2022.1005731
75. Lee J, Burkett BJ, Min HK, et al. Deep learning-based brain age prediction in normal aging and dementia. Nat Aging. 2022;2(5): 412-424. doi:10.1038/s43587-022-00219-7
76. Perovnik M, Tomše P, Jamšek J, Tang CC, Eidelberg D, Trošt M. Metabolic Brain Pattern in Dementia With Lewy Bodies: Relationship to Alzheimer’s Disease Topography. Neuroimage Clin. Published online 2022. doi:10.1016/j.nicl.2022.103080
77. Etsuko Imabayashi, Tsutomu Sawada, Daichi Sone, et al. Validation of the Cingulate Island Sign With Optimized Ratios for Discriminating Dementia With Lewy Bodies From Alzheimer’s Disease Using Brain Perfusion SPECT. Ann Nucl Med. Published online 2017. doi:10.1007/s12149-017-1181-4
78. Iizuka T, Fukasawa M, Kameyama M. Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies. Sci Rep. 2019;9(1):8944. doi:10.1038/s41598-019-45415-5
79. Ruffini G, Ibañez D, Castellano M, et al. Deep Learning With EEG Spectrograms in Rapid Eye Movement Behavior Disorder. Front Neurol. 2019;10:806. doi:10.3389/fneur .2019.00806
80. Jeong E. EEG-based Machine Learning Models for the Prediction of Phenoconversion Time and Subtype in Isolated Rapid Eye Movement Sleep Behavior Disorder. Sleep. Published online 2024. doi:10.1093/sleep/zsae031
81. Salman A, Lapidot I, Shufan E, Agbaria AH, Porat Katz BS, Mordechai S. Potential of infrared microscopy to differentiate between dementia with Lewy bodies and Alzheimer’s diseases using peripheral blood samples and machine learning algorithms. J Biomed Opt. 2020;25(4):1-15. doi:10.1117/1.JBO.25.4.046501
82. Dauwan M, van der Zande JJ, van Dellen E, et al. Random forest to differentiate dementia with Lewy bodies from Alzheimer’s disease. Alzheimers Dement Amst Neth. 2016;4:99-106. doi:10.1016/j.dadm.2016.07.003
83. Li Z, Guo W, Ding S, et al. Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods. Front Genet. 2022;13:880997. doi:10.3389/ fgene.2022.880997
84. Yamada Y, Shinkawa K, Nemoto M, Ota M, Nemoto K, Arai T. Speech and language characteristics differentiate Alzheimer’s disease and dementia with Lewy bodies. Alzheimers Dement Amst Neth. 2022;14(1):e1 2364. doi:10.1002/dad2.12364
85. Faghani S, Moassefi M, Rouzrokh P, et al. Quantifying Uncertainty in Deep Learning of Radiologic Images. Radiology. 2023;308(2):e2 22217. doi:10.1148/radiol.222217