Parkinson DiseaseNCT07381751
Artificial Intelligence-assisted MDS-UPDRS Assessment for Parkinson's Disease
An observational study of Parkinson Disease, run by Hong Kong University of Science and Technology at 2 sites in 2 countries.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 95 years
- Conditions the study is for
- Parkinson Disease
- What is being tested?
- Observational
- How many people
- 500 participants
- Where
- 2 sitesin 2 countries
- Study dates
- 1 Jun 2026to 28 Feb 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 95 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients with Parkinson's disease
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
2 sites in 2 countries. Contact the study team or a site near you to ask about taking part.
2 sites in 2 countries
China1 site, 1 recruiting
- Not yet recruiting
Hong Kong University of Science and Technology
Hong Kong
Qian Zhang, PhD, +852-23588766, qianzh@ust.hk
Hiu Yi Wong, PhD, +852-23587344, annawong@ust.hk
Hong Kong1 site, 1 recruiting
- Recruiting
Queen Elizabeth Hospital
Hong Kong
Qian Zhang, PhD, +852 2358 8766, qianzh@cse.ust.hk
About this study
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.
Read the 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.
- Conditions
- Parkinson Disease
- Being tested
- Observational
- Keywords
- artificial intelligence, MDS-UPDRS, Parkinson's disease
- Medical subject headings
- Parkinson Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ObservationalOther
clinical profile, MDS-UPDRS III, Video Recording
| Study group | Role | What they receive |
|---|---|---|
| PD group | patients with Parkinson's disease Receives: Other: Observational |
What is being measured
The primary outcome is the main question the study is designed to answer.
AI-based motor assessment tool
AI-based motor assessment tool utilizing RGB video for reliable and objective ratings of MDS-UPDRS III motor symptoms, including rigidity and postural stability.
Measured: Baseline to 3 years
Feasibility of implementing RGB camera-based assessments
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.
Measured: 3 years
Secondary outcomes (3)
System's effectiveness
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.
Measured: 3 years
Patient and clinician satisfaction
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.
Measured: baseline to 3 years
System's performance
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.
Measured: baseline to 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Feb 2026
- Study start1 Jun 2026
- Primary completion28 Feb 2029 (estimated)
- Study completion28 Feb 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Parkinson DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hong Kong University of Science and Technology
- Sponsor type
- University, hospital or other
- Collaborators
- The Queen Elizabeth Hospital
- Responsible party
- Principal investigator, Hiu Yi Wong, Hong Kong University of Science and Technology
- Investigators
- Qian Zhang, PhD, Principal investigator, Hong Kong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07381751
- Sponsor's protocol ID
- HREP-2025-0238
- Status last verified
- Jan 2026
- First submitted
- 13 Jan 2026
- Last updated
- 8 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 26474316Zhu 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 abstract available.
PubMed 38501363Boroojerdi 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.
PubMed 30635244Lu 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.
PubMed 33103164Goetz 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 Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord. 2008 Nov 15;23(15):2129-70. doi: 10.1002/mds.22340.
PubMed 19025984Ou 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.
PubMed 34950630Aarsland 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.
PubMed 34210995
Study at a glance
From the registry record, last updated by the sponsor on 8 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-assisted MDS-UPDRS Assessment for Parkinson's Disease
- ClinicalTrials.gov ID
- NCT07381751
- Acronym / sponsor ID
- HREP-2025-0238
- Conditions
- Parkinson Disease
- Keywords
- artificial intelligence, MDS-UPDRS, Parkinson's disease
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Hong Kong University of Science and TechnologyUniversity, hospital or other
- Collaborators
- The Queen Elizabeth Hospital
- Study start
- 1 Jun 2026 (actual)
- Primary completion
- 28 Feb 2029 (estimated)
- Study completion
- 28 Feb 2029 (estimated)
- First posted
- 2 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| PD group | – | Other: Observationalpatients with Parkinson's disease |
Who may qualify
- Ages
- 18 to 95 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients with Parkinson's disease
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (2 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Observationalclinical profile, MDS-UPDRS III, Video Recording | Other | PD group |
Primary outcome
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.
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.
3 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jul 2026.
- Background: PubMed 26474316Postuma 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.
- Background: PubMed 38501363Zhu 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 abstract available.
- Background: PubMed 30635244Boroojerdi 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.
- Background: PubMed 33103164Lu 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.
- Background: PubMed 19025984Goetz 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 Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord. 2008 Nov 15;23(15):2129-70. doi: 10.1002/mds.22340.
- Background: PubMed 34950630Ou 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.
- Background: PubMed 34210995Aarsland 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Qian Zhang, PhD principal investigator
Hong Kong University of Science and Technology
2 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Parkinson Disease, run by Hong Kong University of Science and Technology at 2 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 8 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China, Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Qian Zhang, PhD (+852-23588766, qianzh@ust.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI-based motor assessment tool (time frame: Baseline to 3 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07381751, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Jul 2026; AzTrials last refreshed its copy on 6 Nov 2026.