Artificial Intelligence-assisted MDS-UPDRS Assessment for Parkinson's Disease

Overview

Idiopathic Parkinson's disease (PD) is a neurodegenerative disease that progressively causes both motor and non-motor symptoms. As the second most common neurodegenerative disease and most common movement disorder, it affects over 8.5 million people worldwide and 13,000 people in Hong Kong. The most classical symptoms of PD are resting tremors, rigidity of the muscles, bradykinesia (slowing of movement), and gait difficulty. Other symptoms include sleep disorders, psychiatric symptoms, cognitive impairment, and autonomic dysfunction. Its pathophysiology is marked by the loss of dopaminergic neurons and the accumulation of aggregates called Lewy bodies. The severity of PD-related motor symptoms is usually semi-quantitatively ("normal", "slight", "mild", "moderate", and "severe") evaluated by expert physicians and physiotherapists according to the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III). However, the MDS-UPDRS III is semiquantitative and subjective, which might mask mild treatment effects or even provide false-positive results. Moreover, it takes significant time and effort for assessment with expected inter-observer variations. To address these issues, various artificial intelligence (AI) technologies and telemedicine approaches have been investigated for patient evaluation. However, previous studies did not incorporate items assessing rigidity and postural stability, which require physical contact as per the MDS-UPDRS III instructions. Zhu et al. explored a motor symptom machine-rating system for the complete MDS-UPDRS III. Nevertheless, they employed a depth camera and conducted the tests within a strictly controlled ideal laboratory environment. For the widespread implementation of AI-assisted rating, the RGB camera is a more accessible alternative.

Detailed description

This is a single-center, prospective, observational study designed to develop and validate an AI-based MDS-UPDRS III assessment system using RGB camera data. Participants will be recruited from Queen Elizabeth Hospital's neurology outpatient clinic. Each subject will undergo standard MDS-UPDRS III evaluation by a certified clinician or physiotherapist, alongside synchronized RGB-D video recording. The videos will be processed through a deep learning pipeline trained to estimate the MDS-UPDRS III scores.

Blinded evaluations will be performed to compare AI-generated scores with ground truth clinician ratings. Statistical analysis will include inter-rater agreement metrics (e.g., ICC, Cohen's kappa), sensitivity to change, and subgroup analyses.

Interventions

• Other Observational
  clinical profile, MDS-UPDRS III, Video Recording

Primary outcome measures

• AI-based motor assessment tool [Time frame: Baseline to 3 years]
• Feasibility of implementing RGB camera-based assessments [Time frame: 3 years]

Secondary outcome measures (3)

• System's effectiveness [Time frame: 3 years]
• Patient and clinician satisfaction [Time frame: baseline to 3 years]
• System's performance [Time frame: baseline to 3 years]

Eligibility criteria

Inclusion criteria

• Age ≥18 years
• Diagnosis of "Clinically Established PD" as defined by the Movement Disorder Society Clinical Diagnostic Criteria for Parkinson's disease (MDS-PD criteria) \[12\]
• Able to provide informed consent and willing to participate in video-recorded MDS-UPDRS Part III assessments
• No significant visual, auditory, or musculoskeletal impairments that would interfere with video-based motor assessments

Exclusion criteria

• Unwillingness to be video recorded for study purposes
• History of neurodevelopmental disorder, neurodegenerative disease other than PD, CNS infection, neuroinflammatory disease (e.g. multiple sclerosis, CNS lupus), malignancy within the last 10 years, cerebrovascular accident, HIV infection, systemic autoimmune disease, alcohol dependence or other substance use

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

China · 1 center

• Hong Kong University of Science and Technology — Hong Kong

Hong Kong · 1 center

• Queen Elizabeth Hospital — Hong Kong

Publications

• Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K, Litvan I, Lang AE, Halliday G, Goetz CG, Gasser T, Dubois B, Chan P, Bloem BR, Adler CH, Deuschl G. MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord. 2015 Oct;30(12):1591-601. doi: 10.1002/mds.26424. PMID 26474316
• Zhu X, Chen Z, Ling Y, Luo N, Yin Q, Zhang Y, Zhao A, Ye G, Zhou H, Pan J, Zhou L, Cao L, Huang P, Zhang P, Chen C, Shi W, Lin S, Zhuang H, Zhao J, Ren K, Tan Y, Liu J. Motor symptom machine rating system for complete MDS-UPDRS III in Parkinson's disease: A proof-of-concept pilot study. Chin Med J (Engl). 2024 Jul 5;137(13):1632-1634. doi: 10.1097/CM9.0000000000003044. Epub 2024 Mar 19. No abstrac PMID 38501363
• Boroojerdi B, Ghaffari R, Mahadevan N, Markowitz M, Melton K, Morey B, Otoul C, Patel S, Phillips J, Sen-Gupta E, Stumpp O, Tatla D, Terricabras D, Claes K, Wright JA Jr, Sheth N. Clinical feasibility of a wearable, conformable sensor patch to monitor motor symptoms in Parkinson's disease. Parkinsonism Relat Disord. 2019 Apr;61:70-76. doi: 10.1016/j.parkreldis.2018.11.024. Epub 2018 Nov 27. PMID 30635244
• Lu M, Poston K, Pfefferbaum A, Sullivan EV, Fei-Fei L, Pohl KM, Niebles JC, Adeli E. Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity. Med Image Comput Comput Assist Interv. 2020 Oct;12263:637-647. doi: 10.1007/978-3-030-59716-0_61. Epub 2020 Sep 29. PMID 33103164
• Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stern MB, Dodel R, Dubois B, Holloway R, Jankovic J, Kulisevsky J, Lang AE, Lees A, Leurgans S, LeWitt PA, Nyenhuis D, Olanow CW, Rascol O, Schrag A, Teresi JA, van Hilten JJ, LaPelle N; Movement Disorder Society UPDRS Revision Task Force. Movement Disorder Society-sponsored revision of the Unified Parkin PMID 19025984
• Ou Z, Pan J, Tang S, Duan D, Yu D, Nong H, Wang Z. Global Trends in the Incidence, Prevalence, and Years Lived With Disability of Parkinson's Disease in 204 Countries/Territories From 1990 to 2019. Front Public Health. 2021 Dec 7;9:776847. doi: 10.3389/fpubh.2021.776847. eCollection 2021. PMID 34950630
• Aarsland D, Batzu L, Halliday GM, Geurtsen GJ, Ballard C, Ray Chaudhuri K, Weintraub D. Parkinson disease-associated cognitive impairment. Nat Rev Dis Primers. 2021 Jul 1;7(1):47. doi: 10.1038/s41572-021-00280-3. PMID 34210995

Identifiers

NCT: NCT07381751 · HREP-2025-0238

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗