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NCT Number: NCT07381751

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

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.

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Key information

Conditions

Parkinson Disease Basal Ganglia Diseases Brain Diseases Central Nervous System Diseases Movement Disorders Nervous System Diseases Neurodegenerative Diseases Parkinsonian Disorders Synucleinopathies

Age range

18 year–95 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Hong Kong University of Science and Technology, Hong Kong, China

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Observational

Other

clinical profile, MDS-UPDRS III, Video Recording

Primary outcomes

  1. AI-based motor assessment tool

    Time frame: Baseline to 3 years

    AI-based motor assessment tool utilizing RGB video for reliable and objective ratings of MDS-UPDRS III motor symptoms, including rigidity and postural stability.

  2. Feasibility of implementing RGB camera-based assessments

    Time frame: 3 years

    Feasibility of implementing RGB camera-based assessments in routine clinical settings will be assessed by the proportion of assessments in which the AI system is able to generate an estimated MDS-UPDRS Part III total score based on RGB video that can be directly compared with clinician-rated MDS-UPDRS Part III scores. Patients perform standardized motor tasks under physician guidance while RGB video is recorded using a smartphone. Clinician-rated MDS-UPDRS Part III scores are used as the ground truth. Feasibility outcomes will be reported as the percentage (%) of assessments with valid AI-generated scores over a 3-year study period.

Secondary outcomes

  1. System's effectiveness

    Time frame: 3 years

    System effectiveness in estimating motor symptom severity will be measured by the agreement between AI-predicted and clinician-rated MDS-UPDRS Part III scores. RGB video is recorded using a smartphone while patients perform standardized motor tasks under physician supervision. Clinician-rated MDS-UPDRS Part III scores are used as the ground truth. The AI system generates predicted MDS-UPDRS Part III total scores (range 0-108, with higher scores indicating more severe motor impairment). System effectiveness will be evaluated using F1 score, correlation between predicted and clinician-rated scores, and sensitivity and specificity for the detection of clinically significant motor impairment based on predefined score thresholds. Effectiveness outcomes will be reported over a 3-year study period.

  2. Patient and clinician satisfaction

    Time frame: baseline to 3 years

    Patient satisfaction with the AI-assisted assessment system will be assessed using a study-specific questionnaire administered after completion of RGB camera-based motor tasks. Questionnaire items are rated on a Likert scale, with higher scores indicating greater satisfaction. Clinician evaluation of the AI-assisted assessment system will be assessed using a study-specific questionnaire evaluating perceived credibility, perceived effectiveness, and overall satisfaction with the system. Questionnaire items are rated on a Likert scale, with higher scores indicating more positive evaluations. Patient and clinician evaluation outcomes will be reported as mean ± standard deviation over the study period.

  3. System's performance

    Time frame: baseline to 3 years

    System performance will be measured by the accuracy and mean absolute error (MAE) of AI-predicted MDS-UPDRS Part III total scores compared with clinician-rated scores. Patients perform standardized motor tasks under physician supervision while RGB video is recorded using a smartphone. Clinician-rated MDS-UPDRS Part III scores are used as the ground truth. System performance is defined as the accuracy and MAE of AI predictions. Performance outcomes will be summarized over the study period.

Study contacts

Contact information is provided by the study sponsor or research team.

Hiu Yi Wong, PhD

CONTACT

[email protected]

+852-23587344

Qian Zhang, PhD

CONTACT

[email protected]

+852-23588766

Sponsors and collaborators

Lead sponsor

Hong Kong University of Science and Technology

Other

Collaborators

  • The Queen Elizabeth Hospital

Registry information

Important dates

Study start
2026
Primary completion
2029
Study completion
2029
First posted
Feb 2, 2026
Registry last updated
Jul 8, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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