Abstract
The Movement Disorders Society clinical diagnostic criteria for Parkinson’s disease (MDS-PD) allow highly sensitive and specific diagnosis of Parkinson’s disease. However, their adoption has been limited due to lack of a clinical decision support (CDS) tool to support clinicians and researchers in systematically and accurately applying the MDS-PD criteria. We have developed and performed preliminary validation of a CDS platform for PD (CDS-PD) as a modular and extensible informatics platform with comprehensive functionalities for recording relevant patient information. We have performed real-time application of diagnostic algorithm of the MDS-PD criteria. The CDS-PD platform shows high concordance with application of the MDS-PD criteria by experienced movement disorders neurologists for established PD (disease duration ≥ 5 years). The CDS-PD platform is a step towards realizing the standardized electronic implementation of the MDS-PD criteria for PD patient care and clinical trials at point-of-care. The CDS-PD platform can be accessed after registration at weblink https://www.cdspd.org.
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Data availability
The dataset used in this publication will be made available by request from any qualified investigator. To make such request, please contact corresponding author of this paper from your institutional email address.Source code is not publicly available due to institutional restrictions, but access may be granted to qualified researchers for non-commercial academic use upon reasonable request (subject to data security review and data use agreement). The live platform is accessible to qualified researchers for non-commercial academic use at cdspd.org.
References
Willis, A. W. et al. Incidence of Parkinson disease in North America. Npj Parkinson’s Disease. 8 (1), 170 (2022).
Feigin, V. L. et al. Global, regional, and National burden of neurological disorders during 1990–2015: a systematic analysis for the global burden of disease study 2015. Lancet Neurol. 16 (11), 877–897 (2017).
Dorsey, E. R. et al. The emerging evidence of the Parkinson pandemic. J. Parkinsons Dis. 8 (s1), S3–S8 (2018).
Raty, V. et al. Stability and accuracy of a diagnosis of Parkinson disease over 10 years. Neurology 104 (9), e213499 (2025).
Lang, A. E. et al. Trial designs used to study neuroprotective therapy in parkinson’s disease. Mov. Disord. 28 (1), 86–95 (2013).
Crotty, G. F. et al. Planning for prevention of Parkinson disease: now is the time. Neurology 99 (7 Suppl 1), 1–9 (2022).
McFarland, N. R. Diagnostic approach to atypical parkinsonian syndromes. Continuum (Minneap Minn). 22 (4 Movement Disorders), 1117–1142 (2016).
Chou, K. Diagnosis and Differential Diagnosis of Parkinson Disease (UpToDate, 2024).
McKeith, I. G. et al. Diagnosis and management of dementia with lewy bodies: fourth consensus report of the DLB consortium. Neurology 89 (1), 88–100 (2017).
Wenning, G. K. et al. The movement disorder society criteria for the diagnosis of multiple system atrophy. Mov. Disord. 37 (6), 1131–1148 (2022).
Höglinger, G. U. et al. Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov. Disord. 32 (6), 853–864 (2017).
Armstrong, M. J. et al. Criteria for the diagnosis of corticobasal degeneration. Neurology 80 (5), 496–503 (2013).
Postuma, R. B. et al. MDS clinical diagnostic criteria for parkinson’s disease. Mov. Disord. 30 (12), 1591–1601 (2015).
Postuma, R. B. et al. Validation of the MDS clinical diagnostic criteria for parkinson’s disease. Mov. Disord. 33 (10), 1601–1608 (2018).
Virameteekul, S. et al. Clinical diagnostic accuracy of parkinson’s disease. Where Do We Stand? Mov. Disord. 38 (4), 558–566 (2023).
MDS Position Paper: Diagnosis of Parkinson’s Disease. ; (2023). Available from: https://www.movementdisorders.org/MDS/News/Newsroom/Position-Papers/MDS-Position-Diagnosis-of-PD.htm
IT, A. S. f.T.P.O.o.t.N.C.f.H. National Trends in Hospital and Physician Adoption of Electronic Health Records. Available from: https://www.healthit.gov/data/quickstats/national-trends-hospital-and-physician-adoption-electronic-health-records
Django. Available from: https://www.djangoproject.com/
Ontology-based & ORMIS-PD). Real-time, Machine learning Informatics System for PD (. ; (2021). Available from: https://dtic.dimensions.ai/details/grant/grant.9843089?search_mode=content&search_text=ormis-pd&search_type=kws&search_field=full_search
Sounderajah, V. et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI steering group. Nat. Med. 26 (6), 807–808 (2020).
Talmon, J. et al. STARE-HI—Statement on reporting of evaluation studies in health informatics. Int. J. Med. Informatics. 78 (1), 1–9 (2009).
Elements, N. C. D. CDE Detailed Report - Family History. Available from: https://cde-fe.ninds.nih.gov/ninds/cde-details/CRF/pd/F2948/Participant%20History%20and%20Family%20History/General%20Health%20History/Family%20History
Stebbins, G. T. et al. How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified parkinson’s disease rating scale: comparison with the unified parkinson’s disease rating scale. Mov. Disord. 28 (5), 668–670 (2013).
Lal, V. & Truong, D. Eye movement abnormalities in movement disorders. Clin. Park Relat. Disord. 1, 54–63 (2019).
Berg, D. et al. Movement disorder society criteria for clinically established early parkinson’s disease. Mov. Disord. 33 (10), 1643–1646 (2018).
Cognitive Functions Impairment as a Novel Paradigm for Delineating Cognitive Dysfunction in Parkinson’s Disease (PD-CFI). ; (2023). Available from: https://dtic.dimensions.ai/details/grant/grant.13858982?search_mode=content&search_text=Cognitive%20Functions%20Impairment%20as%20a%20Novel%20Paradigm%20for%20Delineating%20Cognitive%20Dysfunction%20in%20Parkinson%27s%20Disease%20(PD-CFI)%0A&search_type=kws&search_field=full_search
Bruno, M. K. et al. A general neurologist’s practical diagnostic algorithm for atypical parkinsonian disorders: a consensus statement. Neurology: Clin. Pract. 14 (6), e200345 (2024).
Mandel, J. C. et al. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J. Am. Med. Inform. Assoc. 23 (5), 899–908 (2016).
Fereshtehnejad, S. M. et al. Clinical criteria for subtyping parkinson’s disease: biomarkers and longitudinal progression. Brain 140 (7), 1959–1976 (2017).
Dadu, A. et al. Identification and prediction of parkinson’s disease subtypes and progression using machine learning in two cohorts. NPJ Parkinsons Dis. 8 (1), 172 (2022).
Marek, K. et al. The parkinson’s progression markers initiative (PPMI) - establishing a PD biomarker cohort. Ann. Clin. Transl Neurol. 5 (12), 1460–1477 (2018).
Accelerating Medicines Partnership Program for Parkinson’s Disease and Related Disorders (AMP PDRD). Available from: https://amp-pd.org/data
Funding
This study was funded by the project titled “Ontology-based, Real-time, Machine learning Informatics System for Parkinson’s Disease (ORMIS-PD)”, funded by the Early Investigator Research Award (EIRA) of the Parkinson Research Program (PRP) of the Congressionally Directed Medical Research Program (CDMRP) of the United States (US) Department of Defense (DOD). Award # W81XWH2110859.
This work also benefited from the projects funded with grants from the Dravet Syndrome Foundation (DSF), US National Institutes of Health (NIH): U24EB029005, R01DA053028.
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Deepak Gupta: Conceptualization, Validation, Visualization, Investigation, Data Curation, Software, Formal Analysis, Project Administration, Writing – Original Draft, Writing – Review and EditingPedram Golnari: Data Curation, Methodology, Formal Analysis, Software, Writing—Review and EditingKatrina Prantzalos: Methodology, SoftwareIan Zurlo: Conceptualization, MethodologyVivikta Iyer: Investigation, Data Curation, Formal Analysis, Writing – Review and EditingBrenna M Lobb: Investigation, Data Curation, Formal Analysis, Writing – Review and EditingCole Zweber: Data Curation, Formal Analysis, Visualization, Writing—Review and EditingManu Bulusu: SoftwareDakota Clarke: Investigation, Data CurationJames T. Boyd: Writing—Review and EditingCurtis Tatsuoka: Formal AnalysisAmie L. Hiller: Conceptualization, Methodology, Writing—Review and EditingSatya S. Sahoo: Conceptualization, Validation, Visualization, Investigation, Data Curation, Formal Analysis, Project Administration, Writing – Original Draft, Writing – Review and Editing.
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Gupta, D.K., Golnari, P., Prantzalos, K. et al. CDS-PD: a novel clinical decision support platform for Parkinson’s disease. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37316-1
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DOI: https://doi.org/10.1038/s41598-026-37316-1
