Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions
GARCIA SANTA CRUZ, Beatriz; HUSCH, Andreas; HERTEL, Frank
Parkinson's disease,neurodegeneration,Neuroimaging,machine learning,deep learning,computer-aided-diagnosis,Digital Health
Abstract :
[en] Parkinson’s disease (PD) is a progressive and complex neurodegenerative disorder
associated with age that affects motor and cognitive functions. As there is currently
no cure, early diagnosis and accurate prognosis are essential to increase the
effectiveness of treatment and control its symptoms. Medical imaging, specifically
magnetic resonance imaging (MRI), has emerged as a valuable tool for developing
support systems to assist in diagnosis and prognosis. The current literature aims
to improve understanding of the disease’s structural and functional manifestations
in the brain. By applying artificial intelligence to neuroimaging, such as deep
learning (DL) and other machine learning (ML) techniques, previously unknown
relationships and patterns can be revealed in this high-dimensional data. However,
several issues must be addressed before these solutions can be safely integrated
into clinical practice. This review provides a comprehensive overview of recent
ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain
MRI. The main challenges in applying ML to medical diagnosis and its implications
for PD are also addressed, including current limitations for safe translation into
hospitals. These challenges are analyzed at three levels: disease-specific, task-
specific, and technology-specific. Finally, potential future directions for each
challenge and future perspectives are discussed
Disciplines :
Human health sciences: Multidisciplinary, general & others
HUSCH, Andreas ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience
HERTEL, Frank ; Centre Hospitalier de Luxembourg > Service National du Neurochirurgie
External co-authors :
no
Title :
Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions
FNR12244779 - Molecular, Organellar And Cellular Quality Control In Parkinson'S Disease And Other Neurodegenerative Diseases, 2017 (01/05/2018-31/10/2024) - Jens Schwamborn
Abadi M. Chu A. Goodfellow I. McMahan H. B. Mironov I. Talwar K. et al. (2016b). “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (Savannah, GA), 308–318. 10.1145/2976749.297831834745246
Abadi M. Barham P. Chen J. Chen Z. Davis A. Dean J. et al. (2016a). “Tensorflow: a system for large-scale machine learning,” in OSDI, Volume 16 (Savannah, GA), 265–283.
Abdar M. Pourpanah F. Hussain S. Rezazadegan D. Liu L. Ghavamzadeh M. et al. (2021). A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf. Fusion 76, 243–297. 10.1016/j.inffus.2021.05.00835433168
Adeli E. Thung K.-H. An L. Wu G. Shi F. Wang T. et al. (2018). Semi-supervised discriminative classification robust to sample-outliers and feature-noises. IEEE Trans. Pattern Anal. Mach. Intell. 41, 515–522. 10.1109/TPAMI.2018.279447029994560
Akdemir Ü. Ö. Bora H. A. T. Atay L. Ö. (2021). Dopamine transporter spect imaging in Parkinson's disease and parkinsoniandisorders. Turk. J. Med. Sci. 51, 400–410. 10.3906/sag-2008-25333237660
Albrecht F. Poulakis K. Freidle M. Johansson H. Ekman U. Volpe G. et al. (2022). Unraveling Parkinson's disease heterogeneity using subtypes based on multimodal data. Parkinsonism Relat. Disord. 102, 19–29. 10.1016/j.parkreldis.2022.07.01435932584
Ali L. He Z. Cao W. Rauf H. T. Imrana Y. Bin Heyat M. B. et al. (2021). MMDD-ensemble: a multimodal data-driven ensemble approach for Parkinson's disease detection. Front. Neurosci. 15, 754058. 10.3389/fnins.2021.75405834790091
Ancona M. Ceolini E. Öztireli C. Gross M. (2019). “Gradient-based attribution methods,” in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, eds W. Samek, G. Montavon, A. Vedaldi, L. Hansen, and K. R. Müller (Cham: Springer), 169–191. 10.1007/978-3-030-28954-6_9
Aono Y. Hayashi T. Wang L. Moriai S. et al. (2017). Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans. Inf. Forensics Secur. 13, 1333–1345. 10.1109/TIFS.2017.2787987
Arroyo-Gallego T. Ledesma-Carbayo M. J. Butterworth I. Matarazzo M. Montero-Escribano P. Puertas-Martín V. et al. (2018). Detecting motor impairment in early Parkinson's disease via natural typing interaction with keyboards: validation of the neuroqwerty approach in an uncontrolled at-home setting. J. Med. Internet Res. 20, e89. 10.2196/jmir.946229581092
Augimeri A. Cherubini A. Cascini G. L. Galea D. Caligiuri M. E. Barbagallo G. et al. (2016). Coflupane in diagnosi–computer-aided datscan analysis. EJNMMI Phys. 3, 1–13. 10.1186/s40658-016-0140-926879864
Bajaj N. Hauser R. A. Grachev I. D. (2013). Clinical utility of dopamine transporter single photon emission CT (DAT-SPECT) with (123I) ioflupane in diagnosis of parkinsonian syndromes. J. Neurol. Neurosurg. Psychiatry 84, 1288–1295. 10.1136/jnnp-2012-30443623486993
Barbagallo G. Sierra-Peña M. Nemmi F. Traon A. P.-L. Meissner W. G. Rascol O. et al. (2016). Multimodal MRI assessment of nigro-striatal pathway in multiple system atrophy and Parkinson disease. Mov. Disord. 31, 325–334. 10.1002/mds.2647126676922
Behrad F. Abadeh M. S. (2022). An overview of deep learning methods for multimodal medical data mining. Expert Syst. Appl. 200, 117006. 10.1016/j.eswa.2022.117006
Bhagwat N. Barry A. Dickie E. W. Brown S. T. Devenyi G. A. Hatano K. et al. (2021). Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses. GigaScience 10, giaa155. 10.1093/gigascience/giaa15533481004
Bhuyan S. S. Kabir U. Y. Escareno J. M. Ector K. Palakodeti S. Wyant D. et al. (2020). Transforming healthcare cybersecurity from reactive to proactive: current status and future recommendations. J. Med. Syst. 44, 1–9. 10.1007/s10916-019-1507-y32239357
Biondetti E. Gaurav R. Yahia-Cherif L. Mangone G. Pyatigorskaya N. Valabrègue R. et al. (2020). Spatiotemporal changes in substantia nigra neuromelanin content in Parkinson's disease. Brain 143, 2757–2770. 10.1093/brain/awaa21633179028
Blauwendraat C. Nalls M. A. Singleton A. B. (2020). The genetic architecture of Parkinson's disease. Lancet Neurol. 19, 170–178. 10.1016/S1474-4422(19)30287-X31521533
Borghammer P. Van Den Berge N. (2019). Brain-first versus gut-first Parkinson's disease: a hypothesis. J. Parkinsons Dis. 9, S281–S295. 10.3233/JPD-19172131498132
Borghi J. A. Van Gulick A. E. (2018). Data management and sharing in neuroimaging: practices and perceptions of MRI researchers. PLoS ONE 13, e0200562. 10.1371/journal.pone.020056230011302
Boutet A. Madhavan R. Elias G. J. Joel S. E. Gramer R. Ranjan M. et al. (2021). Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning. Nat. Commun. 12, 3043. 10.1038/s41467-021-23311-934031407
Branch L. Eller W. Bias T. McCawley M. Myers D. Gerber B. et al. (2019). Trends in malware attacks against united states healthcare organizations, 2016-2017. Glob. Biosecur. 1, 15–24. 10.31646/gbio.7
Brauneck A. Schmalhorst L. Kazemi Majdabadi M. M. Bakhtiari M. Völker U. Baumbach J. et al. (2023). Federated machine learning, privacy-enhancing technologies, and data protection laws in medical research: scoping review. J. Med. Internet Res. 25, e41588. 10.2196/4158836995759
Broeder S. Nackaerts E. Heremans E. Vervoort G. Meesen R. Verheyden G. et al. (2015). Transcranial direct current stimulation in Parkinson's disease: neurophysiological mechanisms and behavioral effects. Neurosci. Biobehav. Rev. 57, 105–117. 10.1016/j.neubiorev.2015.08.01026297812
Cantello R. Tarletti R. Civardi C. (2002). Transcranial magnetic stimulation and Parkinson's disease. Brain Res. Rev. 38, 309–327. 10.1016/S0165-0173(01)00158-811890979
Cardoso M. J. Li W. Brown R. Ma N. Kerfoot E. Wang Y. et al. (2022). MONAI: an open-source framework for deep learning in healthcare. arXiv. [preprint]. 10.48550/arXiv.2211.027034711849
Castiglioni I. Rundo L. Codari M. Di Leo G. Salvatore C. Interlenghi F. et al. (2021). AI applications to medical images: from machine learning to deep learning. Phys. Med. 83, 9–24. 10.1016/j.ejmp.2021.02.00633662856
Castillo-Barnes D. Martinez-Murcia F. J. Ortiz A. Salas-Gonzalez D. RamÍrez J. Górriz J. M. (2020). Morphological characterization of functional brain imaging by isosurface analysis in Parkinson's disease. Int. J. Neural Syst. 30, 2050044. 10.1142/S012906572050044632787634
Castillo-Barnes D. Ramírez J. Segovia F. Martínez-Murcia F. J. Salas-Gonzalez D. Górriz J. M. (2018). Robust ensemble classification methodology for I123-ioflupane spect images and multiple heterogeneous biomarkers in the diagnosis of Parkinson's disease. Front. Neuroinform. 12, 53. 10.3389/fninf.2018.0005330154711
Castro D. C. Walker I. Glocker B. (2020). Causality matters in medical imaging. Nat. Commun. 11, 3673. 10.1038/s41467-020-17478-w32699250
Chakraborty S. Aich S. Kim H.-C. (2020). Detection of Parkinson's disease from 3t t1 weighted MRI scans using 3D convolutional neural network. Diagnostics 10, 402. 10.3390/diagnostics1006040232545609
Chan H.-P. Hadjiiski L. M. Samala R. K. (2020). Computer-aided diagnosis in the era of deep learning. Med. Phys. 47, e218–e227. 10.1002/mp.1376432418340
Chaudhuri K. R. Healy D. G. Schapira A. H. (2006). Non-motor symptoms of Parkinson's disease: diagnosis and management. Lancet Neurol. 5, 235–245. 10.1016/S1474-4422(06)70373-820642073
Chen C.-M. Chou Y.-H. Tagawa N. Do Y. (2013). Computer-aided detection and diagnosis in medical imaging. Comput. Math. Methods Med. 2013, 790608. 10.1155/2013/79060836416869
Chen H. Ritz B. (2018). The search for environmental causes of Parkinson's disease: moving forward. J. Parkinsons Dis. 8, S9–S17. 10.3233/JPD-18149330584168
Chlap P. Min H. Vandenberg N. Dowling J. Holloway L. Haworth A. et al. (2021). A review of medical image data augmentation techniques for deep learning applications. J. Med. Imaging Radiat. Oncol. 65, 545–563. 10.1111/1754-9485.1326134145766
Chougar L. Faouzi J. Pyatigorskaya N. Yahia-Cherif L. Gaurav R. Biondetti E. et al. (2021). Automated categorization of parkinsonian syndromes using magnetic resonance imaging in a clinical setting. Mov. Disord. 36, 460–470. 10.1002/mds.2834833137232
Chougar L. Pyatigorskaya N. Degos B. Grabli D. Lehéricy S. (2020). The role of magnetic resonance imaging for the diagnosis of atypical parkinsonism. Front. Neurol. 11, 665. 10.3389/fneur.2020.0066532765399
Chua A. S. Egorova S. Anderson M. C. Polgar-Turcsanyi M. Chitnis T. Weiner H. L. et al. (2015). Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: comparison of linear mixed-effect models. Neuroimage Clin. 8, 606–610. 10.1016/j.nicl.2015.06.00926199872
Cohen A. A. Ferrucci L. Fülöp T. Gravel D. Hao N. Kriete A. et al. (2022). A complex systems approach to aging biology. Nat. Aging 2, 580–591. 10.1038/s43587-022-00252-637117782
Coleman W. B. Tsongalis G. J. (2009). Molecular Pathology: The Molecular Basis of Human Disease. Cambridge, MA: Academic Press.
Constantinescu R. Mondello S. (2013). Cerebrospinal fluid biomarker candidates for parkinsonian disorders. Front. Neurol. 3, 187. 10.3389/fneur.2012.0018723346074
Cools R. (2006). Dopaminergic modulation of cognitive function-implications for l-dopa treatment in Parkinson's disease. Neurosci. Biobehav. Rev. 30, 1–23. 10.1016/j.neubiorev.2005.03.02415935475
Council of European Union (2016). General Data Protection Regulation. Avaialble online at: http://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1416170084502anduri=CELEX:32014R0269 (accessed July 1, 2023).
da Silva F. C. Iop R. R. de Oliveira L. C. Boll A. M. de Alvarenga J. G. S. Gutierres Filho P. J. B. et al. (2018). Effects of physical exercise programs on cognitive function in Parkinson's disease patients: a systematic review of randomized controlled trials of the last 10 years. PLoS ONE 13, e0193113. 10.1371/journal.pone.019311329486000
Dadu A. Satone V. Kaur R. Hashemi S. H. Leonard H. Iwaki H. et al. (2022). Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts. NPJ Parkinsons Dis. 8, 172. 10.1038/s41531-022-00439-z36526647
De Pablo-Fernández E. Lees A. J. Holton J. L. Warner T. T. (2019). Prognosis and neuropathologic correlation of clinical subtypes of Parkinson disease. JAMA Neurol. 76, 470–479. 10.1001/jamaneurol.2018.437730640364
Deeb W. Nozile-Firth K. Okun M. S. (2019). Parkinson's disease: diagnosis and appreciation of comorbidities. Handb. Clin. Neurol. 167, 257–277. 10.1016/B978-0-12-804766-8.00014-531753136
Devi S. (2023). Cyber-attacks on health-care systems. Lancet Oncol. 24, e148. 10.1016/S1470-2045(23)00119-536934730
Ding P. Miratrix L. W. (2015). To adjust or not to adjust? Sensitivity analysis of m-bias and butterfly-bias. J. Causal Inference 3, 41–57. 10.1515/jci-2013-0021
Dinsdale N. K. Jenkinson M. Namburete A. I. (2021). Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal. Neuroimage 228, 117689. 10.1016/j.neuroimage.2020.11768933385551
Doi K. (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31, 198–211. 10.1016/j.compmedimag.2007.02.00217349778
dos Santos Ferreira A. Freitas D. M. da Silva G. G. Pistori H. Folhes M. T. (2019). Unsupervised deep learning and semi-automatic data labeling in weed discrimination. Comput. Electron. Agric. 165, 104963. 10.1016/j.compag.2019.104963
Durcan R. Wiblin L. Lawson R. A. Khoo T. K. Yarnall A. Duncan G. W. et al. (2019). Prevalence and duration of non-motor symptoms in prodromal Parkinson's disease. Eur. J. Neurol. 26, 979–985. 10.1111/ene.1391930706593
Elfil M. Kamel S. Kandil M. Koo B. B. Schaefer S. M. (2020). Implications of the gut microbiome in Parkinson's disease. Mov. Disord. 35, 921–933. 10.1002/mds.2800436278360
Eriksen N. Stark A. K. Pakkenberg B. (2009). “Age and Parkinson's disease-related neuronal death in the substantia nigra pars compacta,” in Birth, Life and Death of Dopaminergic Neurons in the Substantia Nigra, eds G. Giovanni, V. Di Matteo, and E. Esposito (Vienna: Springer), 203–213. 10.1007/978-3-211-92660-4_1620411779
Espay A. J. Brundin P. Lang A. E. (2017). Precision medicine for disease modification in parkinson disease. Nat. Rev. Neurol. 13, 119–126. 10.1038/nrneurol.2016.19628106064
Fasano A. Daniele A. Albanese A. (2012). Treatment of motor and non-motor features of Parkinson's disease with deep brain stimulation. Lancet Neurol. 11, 429–442. 10.1016/S1474-4422(12)70049-222516078
Foulds P. G. Mitchell J. D. Parker A. Turner R. Green G. Diggle P. et al. (2011). Phosphorylated α-synuclein can be detected in blood plasma and is potentially a useful biomarker for Parkinson's disease. FASEB J. 25, 4127–4137. 10.1096/fj.10-17919221865317
Fredrikson M. Jha S. Ristenpart T. (2015). “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (New York, NY), 1322–1333. 10.1145/2810103.2813677
Gal Y. Koumoutsakos P. Lanusse F. Louppe G. Papadimitriou C. (2022). Bayesian uncertainty quantification for machine-learned models in physics. Nat. Rev. Phys. 4, 573–577. 10.1038/s42254-022-00498-4
Garcia Santa Cruz B. Bossa M. N. Sölter J. Husch A. D. (2021). Public covid-19 x-ray datasets and their impact on model bias-a systematic review of a significant problem. Med. Image Anal. 74, 102225. 10.1016/j.media.2021.10222534597937
Garcia Santa Cruz B. Slter J. Gomez-Giro G. Saraiva C. Sabate-Soler S. Modamio J. et al. (2022a). Generalising from conventional pipelines using deep learning in high-throughput screening workflows. Sci. Rep. 12, 11465. 10.1038/s41598-022-15623-735794231
Garcia Santa Cruz B. Vega C. Hertel F. (2022b). “The need of standardised metadata to encode causal relationships: towards safer data-driven machine learning biological solutions,” in Computational Intelligence Methods for Bioinformatics and Biostatistics: 17th International Meeting, CIBB 2021, Virtual Event, November 15-17, 2021. Revised Selected Papers (Cham: Springer), 200–216. 10.1007/978-3-031-20837-9_16
Ge W. Lueck C. Suominen H. Apthorp D. (2023). Has machine learning over-promised in healthcare? A critical analysis and a proposal for improved evaluation, with evidence from Parkinson's disease. Artif. Intell. Med. 139, 102524. 10.1016/j.artmed.2023.10252437100503
Ghassemi M. Oakden-Rayner L. Beam A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3, e745–e750. 10.1016/S2589-7500(21)00208-934711379
Gorgolewski K. J. Varoquaux G. Rivera G. Schwarz Y. Ghosh S. S. Maumet C. et al. (2015). NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front. Neuroinform. 9, 8. 10.3389/fninf.2015.0000825914639
Górriz J. M. Ramirez J. Suckling J. Consortium M. A. et al. (2019). On the computation of distribution-free performance bounds: application to small sample sizes in neuroimaging. Pattern Recognit. 93, 1–13. 10.1016/j.patcog.2019.03.032
Gulshan V. Peng L. Coram M. Stumpe M. C. Wu D. Narayanaswamy A. et al. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410. 10.1001/jama.2016.1721631170223
Gupta S. Singh P. Chang K. Qu L. Aggarwal M. Arun N. et al. (2021). Addressing catastrophic forgetting for medical domain expansion. arXiv. [preprint]. 10.48550/arXiv.2103.13511
Hassan A. Wu S. S. Schmidt P. Simuni T. Giladi N. Miyasaki J. M. et al. (2015). The profile of long-term Parkinson's disease survivors with 20 years of disease duration and beyond. J. Parkinsons Dis. 5, 313–319. 10.3233/JPD-14051525720446
Hastie T. Tibshirani R. Friedman J. H. Friedman J. H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Volume 2. Cham: Springer. 10.1007/978-0-387-84858-7
He R. Yan X. Guo J. Xu Q. Tang B. Sun Q. et al. (2018). Recent advances in biomarkers for Parkinson's disease. Front. Aging Neurosci. 10, 305. 10.3389/fnagi.2018.0030530364199
Hess C. W. Okun M. S. (2016). Diagnosing parkinson disease. Contin. Lifelong Learn. Neurol. 22, 1047–1063. 10.1212/CON.000000000000034527495197
Hill-Burns E. M. Debelius J. W. Morton J. T. Wissemann W. T. Lewis M. R. Wallen Z. D. et al. (2017). Parkinson's disease and Parkinson's disease medications have distinct signatures of the gut microbiome. Mov. Disord. 32, 739–749. 10.1002/mds.2694228195358
Hu H. Salcic Z. Sun L. Dobbie G. Yu P. S. Zhang X. et al. (2022). Membership inference attacks on machine learning: a survey. ACM Comput. Surv. 54(11s), 1–37. 10.1145/3523273
Huang Y.-P. Chen L.-S. Yen M.-F. Fann C.-Y. Chiu Y.-H. Chen H.-H. et al. (2013). Parkinson's disease is related to an increased risk of ischemic stroke–a population-based propensity score-matched follow-up study. PLoS ONE 8, e68314. 10.1371/journal.pone.006831424023710
Hüllermeier E. Waegeman W. (2021). Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110, 457–506. 10.1007/s10994-021-05946-3
Hustad E. Aasly J. O. (2020). Clinical and imaging markers of prodromal Parkinson's disease. Front. Neurol. 11, 395. 10.3389/fneur.2020.0039532457695
Jakubovitz D. Giryes R. Rodrigues M. R. (2019). “Generalization error in deep learning,” in Compressed Sensing and Its Applications: Third International MATHEON Conference 2017, eds H. Boche, G. Caire, R. Calderbank, G. Kutyniok, R. Mathar, and P. Petersen (Cham: Springer), 153–193. 10.1007/978-3-319-73074-5_532650153
Jankovic J. McDermott M. Carter J. Gauthier S. Goetz C. Golbe L. et al. (1990). Variable expression of Parkinson's disease: a base-line analysis of the dat atop cohort. Neurology 40, 1529–1529. 10.1212/WNL.40.10.15292215943
Jiang Y. Neyshabur B. Mobahi H. Krishnan D. Bengio S. (2019). Fantastic generalization measures and where to find them. arXiv. [preprint]. 10.48550/arXiv.1912.02178
Jimenez-Mesa C. Ramirez J. Suckling J. Vöglein J. Levin J. Gorriz J. M. Initiative A. D. N. et al. (2023). A non-parametric statistical inference framework for deep learning in current neuroimaging. Inf. Fusion 91, 598–611. 10.1016/j.inffus.2022.11.007
Kalia L. V. Lang A. E. (2015). Parkinson's disease. Lancet 386, 896–912. 10.1016/S0140-6736(14)61393-325904081
Kandasamy K. Neiswanger W. Schneider J. Poczos B. Xing E. P. (2018). Neural architecture search with bayesian optimisation and optimal transport. Adv. Neural Inf. Process. Syst. 31, 2016–2026. 10.5555/3326943.3327130
Karthik S. Revaud J. Chidlovskii B. (2021). Learning from long-tailed data with noisy labels. arXiv. [preprint]. 10.48550/arXiv.2108.11096
Kaur H. Nori H. Jenkins S. Caruana R. Wallach H. Wortman Vaughan J. et al. (2020). “Interpreting interpretability: understanding data scientists' use of interpretability tools for machine learning,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (New York, NY), 1–14. 10.1145/3313831.3376219
Kondrateva E. Pominova M. Popova E. Sharaev M. Bernstein A. Burnaev E. et al. (2021). “Domain shift in computer vision models for MRI data analysis: an overview,” in Thirteenth International Conference on Machine Vision, Volume 11605 (Bellingham, WA: SPIE), 126–133. 10.1117/12.2587872
Kubota K. J. Chen J. A. Little M. A. (2016). Machine learning for large-scale wearable sensor data in Parkinson's disease: concepts, promises, pitfalls, and futures. Mov. Disord. 31, 1314–1326. 10.1002/mds.2669327501026
Kukačka J. Golkov V. Cremers D. (2017). Regularization for deep learning: a taxonomy. arXiv. [preprint]. 10.48550/arXiv.1710.10686
Laird A. R. Eickhoff S. B. Fox P. M. Uecker A. M. Ray K. L. Saenz J. J. et al. (2011). The brainmap strategy for standardization, sharing, and meta-analysis of neuroimaging data. BMC Res. Notes 4, 1–9. 10.1186/1756-0500-4-34921906305
Langley J. He N. Huddleston D. E. Chen S. Yan F. Crosson B. et al. (2019). Reproducible detection of nigral iron deposition in 2 Parkinson's disease cohorts. Mov. Disord. 34, 416–419. 10.1002/mds.2760830597635
Lawton M. Baig F. Rolinski M. Ruffman C. Nithi K. May M. T. et al. (2015). Parkinson's disease subtypes in the oxford parkinson disease centre (OPDC) discovery cohort. J. Parkinsons Dis. 5, 269–279. 10.3233/JPD-14052326405788
Lee D. J. Lozano C. S. Dallapiazza R. F. Lozano A. M. (2019). Current and future directions of deep brain stimulation for neurological and psychiatric disorders: JNSPG 75th anniversary invited review article. J. Neurosurg. 131, 333–342. 10.3171/2019.4.JNS18176131370011
Lemay A. Hoebel K. Bridge C. P. Befano B. De Sanjosé S. Egemen D. et al. (2022). Improving the repeatability of deep learning models with Monte Carlo dropout. Npj Digit. Med. 5, 174. 10.1038/s41746-022-00709-336400939
Liu X. CONSORT-AI T. Group S.-A. S. (2019). Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat. Med. 25, 1467–1468. 10.1038/s41591-019-0603-331719666
Lozano A. M. (2017). Waving hello to noninvasive deep-brain stimulation. N. Engl. J. Med. 377, 1096–1098. 10.1056/NEJMcibr170716528902594
Lu M. Zhao Q. Poston K. L. Sullivan E. V. Pfefferbaum A. Shahid M. et al. (2021). Quantifying Parkinson's disease motor severity under uncertainty using mds-updrs videos. Med. Image Anal. 73, 102179. 10.1016/j.media.2021.10217934340101
Madry A. Makelov A. Schmidt L. Tsipras D. Vladu A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv. [preprint]. 10.48550/arXiv.1706.0608334648444
Magesh P. R. Myloth R. D. Tom R. J. (2020). An explainable machine learning model for early detection of Parkinson's disease using lime on datscan imagery. Comput. Biol. Med. 126, 104041. 10.1016/j.compbiomed.2020.10404133074113
Mahlknecht P. Seppi K. Stockner H. Nocker M. Scherfler C. Kiechl S. et al. (2013). Substantia nigra hyperechogenicity as a marker for Parkinson's disease: a population-based study. Neurodegener. Dis. 12, 212–218. 10.1159/00034859523689066
Mangasarian O. L. Street W. N. Wolberg W. H. (1995). Breast cancer diagnosis and prognosis via linear programming. Oper. Res. 43, 570–577. 10.1287/opre.43.4.570
Marek K. Jennings D. Lasch S. Siderowf A. Tanner C. Simuni T. et al. (2011). The parkinson progression marker initiative (PPMI). Prog. Neurobiol. 95, 629–635. 10.1016/j.pneurobio.2011.09.00521930184
Marras C. Beck J. Bower J. Roberts E. Ritz B. Ross G. et al. (2018). Prevalence of Parkinson's disease across north america. NPJ Parkinsons Dis. 4, 1–7. 10.1038/s41531-018-0058-030003140
Martínez-Murcia F. J. Górriz J. M. Ramírez J. Illán I. Ortiz A. Initiative P. P. M. et al. (2014). Automatic detection of parkinsonism using significance measures and component analysis in datscan imaging. Neurocomputing 126, 58–70. 10.1016/j.neucom.2013.01.054
Martinez-Murcia F. J. Ortiz A. Gorriz J.-M. Ramirez J. Castillo-Barnes D. (2019). Studying the manifold structure of alzheimer's disease: a deep learning approach using convolutional autoencoders. IEEE J. Biomed. Health Inform. 24, 17–26. 10.1109/JBHI.2019.291497031217131
Martins R. Oliveira F. Moreira F. Moreira A. P. Abrunhosa A. Januário C. et al. (2021). Automatic classification of idiopathic Parkinson's disease and atypical parkinsonian syndromes combining [11C] raclopride pet uptake and MRI grey matter morphometry. J. Neural. Eng. 18, 046037. 10.1088/1741-2552/abf77233848996
Mata I. F. Shi M. Agarwal P. Chung K. A. Edwards K. L. Factor S. A. et al. (2010). SNCA variant associated with parkinson disease and plasma α-synuclein level. Arch. Neurol. 67, 1350–1356. 10.1001/archneurol.2010.27921060011
Mehrabi N. Morstatter F. Saxena N. Lerman K. Galstyan A. (2021). A survey on bias and fairness in machine learning. ACM Comput. Surv. 54, 1–35. 10.1145/3457607
Mei J. Desrosiers C. Frasnelli J. (2021). Machine learning for the diagnosis of Parkinson's disease: a review of literature. Front. Aging Neurosci. 13, 633752. 10.3389/fnagi.2021.63375234025389
Miceli M. Schuessler M. Yang T. (2020). Between subjectivity and imposition: power dynamics in data annotation for computer vision. Proc. ACM Hum.-Comput. Interact. 4(CSCW2), 1–25. 10.1145/3415186
Michell A. Lewis S. Foltynie T. Barker R. (2004). Biomarkers and Parkinson's disease. Brain 127, 1693–1705. 10.1093/brain/awh19815215212
Mohammadi D. (2013). The harvard biomarker study's big plan. Lancet Neurol. 12, 739–740. 10.1016/S1474-4422(13)70155-823809962
Molnar C. (2020). Interpretable machine learning. Available online at: https://christophm.github.io/interpretableml-book/ (accessed July 1, 2023).
Morrish P. Sawle G. Brooks D. (1996). An [18F] dopa-pet and clinical study of the rate of progression in Parkinson's disease. Brain 119, 585–591. 10.1093/brain/119.2.5858800950
Movement Disorder Society Task Force on Rating Scales for Parkinson's Disease (2003). The unified Parkinson's disease rating scale (UPDRS): status and recommendations. Mov. Disord. 18, 738–750. 10.1002/mds.1047312815652
Muangpaisan W. Mathews A. Hori H. Seidel D. (2011). A systematic review of the worldwide prevalence and incidence of Parkinson's disease. J. Med. Assoc. Thailand 94, 749.21696087
Müller R. Kornblith S. Hinton G. E. (2019). When does label smoothing help? Adv. Neural Inf. Process. Syst. 32, 4671–4681. Available online at: https://dl.acm.org/doi/10.5555/3454287.3454709
Nair S. R. Tan L. K. Mohd Ramli N. Lim S. Y. Rahmat K. Mohd Nor H. et al. (2013). A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging. Eur. Radiol. 23, 1459–1466. 10.1007/s00330-012-2759-923300042
Neelakandan S. Beulah J. R. Prathiba L. Murthy G. Irudaya Raj E. F. Arulkumar N. et al. (2022). Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model. Int. J. Model. Simul. Sci. Comput. 13, 2241006. 10.1142/S1793962322410069
Neri E. de Souza N. Brady A. Bayarri A. A. Becker C. D. Coppola F. et al. (2019). What the radiologist should know about artificial intelligence-an ESR white paper. Insights Imaging 10, 44. 10.1186/s13244-019-0738-230949865
Nerius M. Fink A. Doblhammer G. (2017). Parkinson's disease in germany: prevalence and incidence based on health claims data. Acta Neurol. Scand. 136, 386–392. 10.1111/ane.1269427726128
Ngiam K. Y. Khor W. (2019). Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 20, e262–e273. 10.1016/S1470-2045(19)30149-431044724
Nicastro N. Garibotto V. Burkhard P. R. (2020). Extrastriatal 123 I-FP-CIT spect impairment in Parkinson's disease-the PPMI cohort. BMC Neurol. 20, 1–9. 10.1186/s12883-020-01777-232416724
Niotis K. West A. B. Saunders-Pullman R. (2022). Who to enroll in parkinson disease prevention trials?: the case for genetically at-risk cohorts. Neurology 99(7 Supplement 1), 10–18. 10.1212/WNL.000000000020081235970585
Obermeyer Z. Powers B. Vogeli C. Mullainathan S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453. 10.1126/science.aax234231649194
Oprescu M. Syrgkanis V. Wu Z. S. (2019). “Orthogonal random forest for causal inference,” in International Conference on Machine Learning (New York, NY: PMLR), 4932–4941.35495115
Pagano G. Niccolini F. Politis M. (2016). Imaging in Parkinson's disease. Clin. Med. 16, 371. 10.7861/clinmedicine.16-4-37127481384
Pal G. Mangone G. Hill E. J. Ouyang B. Liu Y. Lythe V. et al. (2022). Parkinson disease and subthalamic nucleus deep brain stimulation: cognitive effects in GBA mutation carriers. Ann. Neurol. 91, 424–435. 10.1002/ana.2630234984729
Parisi F. Ferrari G. Giuberti M. Contin L. Cimolin V. Azzaro C. et al. (2015). Body-sensor-network-based kinematic characterization and comparative outlook of UPDRS scoring in leg agility, sit-to-stand, and gait tasks in Parkinson's disease. IEEE J. Biomed. Health Inf. 19, 1777–1793. 10.1109/JBHI.2015.247264026316236
Parkinson J. (2002). An essay on the shaking palsy. J. Neuropsychiatry Clin. Neurosci. 14, 223–236. 10.1176/jnp.14.2.22311983801
Paszke A. Gross S. Massa F. Lerer A. Bradbury J. Chanan G. et al. (2019). Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 7994–8006. Available online at: https://dl.acm.org/doi/10.5555/3454287.3455008
Patterson D. Gonzalez J. Le Q. Liang C. Munguia L.-M. Rothchild D. et al. (2021). Carbon emissions and large neural network training. arXiv. [preprint]. 10.48550/arXiv.2104.10350
Pechevis M. Clarke C. Vieregge P. Khoshnood B. Deschaseaux-Voinet C. Berdeaux G. et al. (2005). Effects of dyskinesias in Parkinson's disease on quality of life and health-related costs: a prospective european study. Eur. J. Neurol. 12, 956–963. 10.1111/j.1468-1331.2005.01096.x16324089
Pellicano C. Benincasa D. Pisani V. Buttarelli F. R. Giovannelli M. Pontieri F. E. et al. (2007). Prodromal non-motor symptoms of Parkinson's disease. Neuropsychiatr. Dis. Treat. 3, 145. 10.2147/nedt.2007.3.1.14534145553
Pickrell A. M. Youle R. J. (2015). The roles of pink1, parkin, and mitochondrial fidelity in Parkinson's disease. Neuron 85, 257–273. 10.1016/j.neuron.2014.12.00725611507
Poewe W. Wenning G. (2002). The differential diagnosis of Parkinson's disease. Eur. J. Neurol. 9, 23–30. 10.1046/j.1468-1331.9.s3.3.x12464118
Poldrack R. A. Barch D. M. Mitchell J. P. Wager T. D. Wagner A. D. Devlin J. T. et al. (2013). Toward open sharing of task-based fmri data: the openfmri project. Front. Neuroinform. 7, 12. 10.3389/fninf.2013.0001223847528
Politis M. (2014). Neuroimaging in parkinson disease: from research setting to clinical practice. Nat. Rev. Neurol. 10, 708–722. 10.1038/nrneurol.2014.20525385334
Politis M. Piccini P. Pavese N. Koh S.-B. Brooks D. J. (2008). Evidence of dopamine dysfunction in the hypothalamus of patients with Parkinson's disease: an in vivo 11c-raclopride pet study. Exp. Neurol. 214, 112–116. 10.1016/j.expneurol.2008.07.02118723016
Postuma R. B. Poewe W. Litvan I. Lewis S. Lang A. E. Halliday G. et al. (2018). Validation of the mds clinical diagnostic criteria for Parkinson's disease. Mov. Disord. 33, 1601–1608. 10.1002/mds.2736230145797
Power J. D. Plitt M. Laumann T. O. Martin A. (2017). Sources and implications of whole-brain fmri signals in humans. Neuroimage 146, 609–625. 10.1016/j.neuroimage.2016.09.03827751941
Prechelt L. (1998). “Early stopping-but when?” in Neural Networks: Tricks of the Trade, eds G. B. Orr, and K. R. Müller (Berlin: Springer), 55–69. 10.1007/3-540-49430-8_3
Prell T. (2018). Structural and functional brain patterns of non-motor syndromes in Parkinson's disease. Front. Neurol. 9, 138. 10.3389/fneur.2018.0013829593637
Pyatigorskaya N. Sanz-Morère C. B. Gaurav R. Biondetti E. Valabregue R. Santin M. et al. (2020). Iron imaging as a diagnostic tool for Parkinson's disease: a systematic review and meta-analysis. Front. Neurol. 11, 366. 10.3389/fneur.2020.0036632547468
Qin R. Zhang H. Jiang L. Qiao K. Hai J. Chen J. et al. (2020). Multicenter computer-aided diagnosis for lymph nodes using unsupervised domain-adaptation networks based on cross-domain confounding representations. Comput. Math. Methods Med. 2020, 3709873. 10.1155/2020/370987332454880
Rajput A. (1992). Frequency and cause of Parkinson's disease. Can. J. Neurol. Sci. 19, 103–107. 10.1017/S0317167100041457
Reddy S. Allan S. Coghlan S. Cooper P. (2020). A governance model for the application of ai in health care. J. Am. Med. Inform. Assoc. 27, 491–497. 10.1093/jamia/ocz19231682262
Riboldi G. M. Frattini E. Monfrini E. Frucht S. J. Di Fonzo A. (2022). A practical approach to early-onset parkinsonism. J. Parkinsons Dis. 12, 1–26. 10.3233/JPD-21281534569973
Ricci Lara M. A. Echeveste R. Ferrante E. (2022). Addressing fairness in artificial intelligence for medical imaging. Nat. Commun. 13, 4581. 10.1038/s41467-022-32186-335933408
Rietdijk C. D. Perez-Pardo P. Garssen J. Van Wezel R. J. Kraneveld A. D. (2017). Exploring Braak's hypothesis of Parkinson's disease. Front. Neurol. 8, 37. 10.3389/fneur.2017.0003728243222
Saeed U. Compagnone J. Aviv R. I. Strafella A. P. Black S. E. Lang A. E. et al. (2017). Imaging biomarkers in Parkinson's disease and parkinsonian syndromes: current and emerging concepts. Transl. Neurodegener. 6, 1–25. 10.1186/s40035-017-0076-628360997
Sakai K. Yamada K. (2019). Machine learning studies on major brain diseases: 5-year trends of 2014-2018. Jpn. J. Radiol. 37, 34–72. 10.1007/s11604-018-0794-430498877
Sambasivan N. Kapania S. Highfill H. Akrong D. Paritosh P. K. Aroyo L. M. et al. (2021). “Everyone wants to do the model work, not the data work,” in Data Cascades in High-stakes AI (New York, NY), 1–15. 10.1145/3411764.3445518
Santiago J. A. Bottero V. Potashkin J. A. (2017). Biological and clinical implications of comorbidities in Parkinson's disease. Front. Aging Neurosci. 9, 394. 10.3389/fnagi.2017.0039429255414
Schootemeijer S. van der Kolk N. M. Bloem B. R. de Vries N. M. (2020). Current perspectives on aerobic exercise in people with Parkinson's disease. Neurotherapeutics 17, 1418–1433. 10.1007/s13311-020-00904-835290610
Schwarz S. T. Afzal M. Morgan P. S. Bajaj N. Gowland P. A. Auer D. P. et al. (2014). The ‘swallow tail' appearance of the healthy nigrosome-a new accurate test of Parkinson's disease: a case-control and retrospective cross-sectional MRI study at 3T. PLoS ONE 9, e93814. 10.1371/journal.pone.009381424710392
Settles B. (2009). Active Learning Literature Survey. Madison, WI: University of Wisconsin–Madison.
Shinde S. Prasad S. Saboo Y. Kaushick R. Saini J. Pal P. K. et al. (2019). Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI. Neuroimage Clin. 22, 101748. 10.1016/j.nicl.2019.10174830870733
Siderowf A. Concha-Marambio L. Lafontant D.-E. Farris C. M. Ma Y. Urenia P. A. et al. (2023). Assessment of heterogeneity among participants in the Parkinson's progression markers initiative cohort using α-synuclein seed amplification: a cross-sectional study. Lancet Neurol. 22, 407–417. 10.1016/S1474-4422(23)00109-637059509
Siderowf A. Jennings D. Eberly S. Oakes D. Hawkins K. A. Ascherio A. et al. (2012). Impaired olfaction and other prodromal features in the parkinson at-risk syndrome study. Mov. Disord. 27, 406–412. 10.1002/mds.2489222237833
Smith J. J. Sorensen A. G. Thrall J. H. (2003). Biomarkers in imaging: realizing radiology's future. Radiology 227, 633–638. 10.1148/radiol.227302051812663828
Smith M. G. Witte M. Rocha S. Basner M. (2019). Effectiveness of incentives and follow-up on increasing survey response rates and participation in field studies. BMC Med. Res. Methodol. 19, 1–13. 10.1186/s12874-019-0868-831805869
Song L. Shokri R. Mittal P. (2019). “Privacy risks of securing machine learning models against adversarial examples,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (New York, NY), 241–257. 10.1145/3319535.3354211
Stoker T. B. Barker R. A. (2020). Recent developments in the treatment of Parkinson's disease. F1000Res. 9, 11. 10.12688/f1000research.25634.132789002
Stolze H. Kuhtz-Buschbeck J. P. Drücke H. Jöhnk K. Illert M. Deuschl G. (2001). Comparative analysis of the gait disorder of normal pressure hydrocephalus and Parkinson's disease. J. Neurol. Neurosurg. Psychiatry 70, 289–297. 10.1136/jnnp.70.3.28911181848
Strother S. C. (2006). Evaluating fmri preprocessing pipelines. IEEE Eng. Med. Biol Mag. 25, 27–41. 10.1109/MEMB.2006.160766716568935
Sulzer D. Cassidy C. Horga G. Kang U. J. Fahn S. Casella L. et al. (2018). Neuromelanin detection by magnetic resonance imaging (MRI) and its promise as a biomarker for Parkinson's disease. NPJ Parkinsons Dis. 4, 11. 10.1038/s41531-018-0047-329644335
Sveinbjornsdottir S. (2016). The clinical symptoms of Parkinson's disease. J. Neurochem. 139, 318–324. 10.1111/jnc.1369127401947
Tahmasian M. Bettray L. M. van Eimeren T. Drzezga A. Timmermann L. Eickhoff C. R. et al. (2015). A systematic review on the applications of resting-state fmri in Parkinson's disease: does dopamine replacement therapy play a role? Cortex 73, 80–105. 10.1016/j.cortex.2015.08.00526386442
Talai A. S. Sedlacik J. Boelmans K. Forkert N. D. (2021). Utility of multi-modal MRI for differentiating of Parkinson's disease and progressive supranuclear palsy using machine learning. Front. Neurol. 12, 648548. 10.3389/fneur.2021.64854833935946
Tamburri D. A. (2020). “Sustainable mlops: trends and challenges,” in 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) (Timisoara: IEEE), 17–23. 10.1109/SYNASC51798.2020.00015
Tan A. H. Lim S.-Y. Chong K. K. Manap M. A. A. A. Hor J. W. Lim J. L. et al. (2021). Probiotics for constipation in parkinson disease: a randomized placebo-controlled study. Neurology 96, e772–e782. 10.1212/WNL.000000000001099833046607
Tedeschini B. C. Savazzi S. Stoklasa R. Barbieri L. Stathopoulos I. Nicoli M. et al. (2022). Decentralized federated learning for healthcare networks: a case study on tumor segmentation. IEEE Access 10, 8693–8708. 10.1109/ACCESS.2022.3141913
Thenganatt M. A. Jankovic J. (2014). Parkinson disease subtypes. JAMA Neurol. 71, 499–504. 10.1001/jamaneurol.2013.623324514863
Thevathasan W. Debu B. Aziz T. Bloem B. R. Blahak C. Butson C. et al. (2018). Pedunculopontine nucleus deep brain stimulation in Parkinson's disease: a clinical review. Mov. Disord. 33, 10–20. 10.1002/mds.2709828960543
Tolosa E. Garrido A. Scholz S. W. Poewe W. (2021). Challenges in the diagnosis of Parkinson's disease. Lancet Neurol. 20, 385–397. 10.1016/S1474-4422(21)00030-233894193
Tolosa E. Vila M. Klein C. Rascol O. (2020). Lrrk2 in parkinson disease: challenges of clinical trials. Nat. Rev. Neurol. 16, 97–107. 10.1038/s41582-019-0301-231980808
Toulas B. (2023). Hospital Clínic de Barcelona Severely Impacted by Ransomware Attack. Available online at: https://www.bleepingcomputer.com/news/security/hospital-cl-nic-de-barcelona-severely-impacted-by-ransomware-attack/ (accessed July 1, 2023).
van Veluw S. J. Zwanenburg J. J. Hendrikse J. van der Kolk A. G. Luijten P. R. Biessels G. J. et al. (2014). “High resolution imaging of cerebral small vessel disease with 7 T MRI,” in Trends Neurovascular Interventions, eds T. Tsukahara, G. Esposito, H. J. Steiger., G. Rinkel, and L. Regli (Cham: Springer), 125–130. 10.1007/978-3-319-02411-0_2124728645
Vaswani A. Shazeer N. Parmar N. Uszkoreit J. Jones L. Gomez A. N. et al. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5999–6010. Available online at: https://dl.acm.org/doi/10.5555/3295222.3295349
Vega C. (2021). From hume to Wuhan: an epistemological journey on the problem of induction in covid-19 machine learning models and its impact upon medical research. IEEE Access 9, 97243–97250. 10.1109/ACCESS.2021.309522234812399
Virreira Winter S. Karayel O. Strauss M. T. Padmanabhan S. Surface M. Merchant K. et al. (2021). Urinary proteome profiling for stratifying patients with familial Parkinson's disease. EMBO Mol. Med. 13, e13257. 10.15252/emmm.20201325733481347
Visani G. Bagli E. Chesani F. Poluzzi A. Capuzzo D. (2022). Statistical stability indices for lime: obtaining reliable explanations for machine learning models. J. Oper. Res. Soc. 73, 91–101. 10.1080/01605682.2020.1865846
Wald Y. Feder A. Greenfeld D. Shalit U. (2021). On calibration and out-of-domain generalization. Adv. Neural Inf. Process. Syst. 34, 2215–2227. 10.48550/arXiv.2102.10395
Wang H. Wu Z. Liu Z. Cai H. Zhu L. Gan C. et al. (2020). HAT: hardware-aware transformers for efficient natural language processing. arXiv. [preprint]. 10.48550/arXiv.2005.14187
Wang H. Wu Z. Xing E. P. (2018). “Removing confounding factors associated weights in deep neural networks improves the prediction accuracy for healthcare applications,” in BIOCOMPUTING 2019: Proceedings of the Pacific Symposium (Singapore: World Scientific), 54–65. 10.1142/9789813279827_000630864310
Weingärtner S. Desmond K. L. Obuchowski N. A. Baessler B. Zhang Y. Biondetti E. et al. (2022). Development, validation, qualification, and dissemination of quantitative MR methods: overview and recommendations by the ISMRM quantitative MR study group. Magn. Reson. Med. 87, 1184–1206. 10.1002/mrm.2908434825741
Westreich D. Lessler J. Funk M. J. (2010). Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J. Clin. Epidemiol. 63, 826–833. 10.1016/j.jclinepi.2009.11.02020630332
Widner K. Virmani S. Krause J. Nayar J. Tiwari R. Pedersen E. R. et al. (2023). Lessons learned from translating ai from development to deployment in healthcare. Nat. Med. 29, 1304–1306. 10.1038/s41591-023-02293-937248297
Wiens J. Price W. N. Sjoding M. W. (2020). Diagnosing bias in data-driven algorithms for healthcare. Nat. Med. 26, 25–26. 10.1038/s41591-019-0726-631932798
Wyman B. T. Harvey D. J. Crawford K. Bernstein M. A. Carmichael O. Cole P. E. et al. (2013). Standardization of analysis sets for reporting results from adni MRI data. Alzheimers Dement. 9, 332–337. 10.1016/j.jalz.2012.06.00423110865
Xu X.-W. Doi K. Kobayashi T. MacMahon H. Giger M. L. (1997). Development of an improved CAD scheme for automated detection of lung nodules in digital chest images. Med. Phys. 24, 1395–1403. 10.1118/1.5980289304567
Yagis E. DE Herrera A. G. S. Citi L. (2019). “Generalization performance of deep learning models in neurodegenerative disease classification,” in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (San Diego, CA: IEEE), 1692–1698. 10.1109/BIBM47256.2019.898308835103240
Yoshikawa K. Nakata Y. Yamada K. Nakagawa M. (2004). Early pathological changes in the parkinsonian brain demonstrated by diffusion tensor MRI. J. Neurol. Neurosurg. Psychiatry 75, 481–484. 10.1136/jnnp.2003.02187314966170
Yosinski J Clune J. Bengio Y. Lipson H. (2014). How transferable are features in deep neural networks? Adv. Neural Inf. Process. Syst. 27, 3320–3329. Available online at: https://dl.acm.org/doi/10.5555/2969033.296919730935654
Zeng X. Martinez T. R. (2000). Distribution-balanced stratified cross-validation for accuracy estimation. J. Exp. Theor. Artif. Intell. 12, 1–12. 10.1080/09528130014627234413823
Zetusky W. J. Jankovic J. Pirozzolo F. J. (1985). The heterogeneity of Parkinson's disease: clinical and prognostic implications. Neurology 35, 522–522. 10.1212/WNL.35.4.5223982637
Zhang C. Bengio S. Hardt M. Recht B. Vinyals O. (2021). Understanding deep learning (still) requires rethinking generalization. Commun. ACM 64, 107–115. 10.1145/344677610388030
Zhang S. Tao K. Wang J. Duan Y. Wang B. Liu X. et al. (2020). Substantia nigra hyperechogenicity reflects the progression of dopaminergic neurodegeneration in 6-ohda rat model of Parkinson's disease. Front. Cell. Neurosci. 14, 216. 10.3389/fncel.2020.0021632848616
Zhang X. Chou J. Liang J. Xiao C. Zhao Y. Sarva H. et al. (2019). Data-driven subtyping of Parkinson's disease using longitudinal clinical records: a cohort study. Sci. Rep. 9, 797. 10.1038/s41598-018-37545-z30692568
Zhao J. Wang T. Yatskar M. Ordonez V. Chang K.-W. (2017). Men also like shopping: reducing gender bias amplification using corpus-level constraints. arXiv. [preprint]. 10.48550/arXiv.1707.09457
