Parkinsonian DisordersNCT05906719
Machine Vision Based MDS-UPDRS III Machine Rating
An observational study of Parkinsonian Disorders and Machine Learning, run by Ruijin Hospital at 8 sites in China.
Trial summary
The Movement Disorders Society (MDS) Unified Parkinson's Disease Rating Scale (UPDRS) Part III (MDS-UPDRS III) is the primary assessment method for motor symptoms in Parkinson's disease patients. Read more about this study
- Who is it for?
- Ages 20 to 80 years
- Conditions the study is for
- Parkinsonian Disorders, Machine Learning
- What is being tested?
- video recording
- How many people
- 1,679 participants
- Where
- 8 sitesChina
- Study dates
- 1 Mar 2023to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
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 20 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: In this study, participants are mainly recruited from the Department of Neurology at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, as well as from movement disorder clinics at 7 other centers. Participants are enrolled through outpatient assessment and screening. Two specialists in movement disorders independently diagnosed participants as having "Parkinsonism". In cases where there is a diagnostic disagreement, the Movement Disorder Specialist Group at the Department of Neurology, Ruijin Hospital collectively discusses and makes the decision.
Who may be able to join
- Meeting the diagnostic criteria for Parkinsonism established by the International Movement Disorder Society: having bradykinesia, and meeting at least one of the two criteria for resting tremor or muscle rigidity
- 20 to 80 years old
- Good compliance, voluntarily joining the study, and able to sign an informed consent form or have it signed by a legal representative
Who cannot join
- Significant cognitive impairment (MMSE ≤ 23)
- Unable to sign written informed consent or unable to complete the trial due to other reasons
- Other situations in which the researcher deems the participant unsuitable for this study
- Participation in other clinical trials
Trial locations and contacts
8 sites in 1 country.
8 sites in 1 country
China8 sites
Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University
Beijing, Beijing Municipality, 100050
Beijing Hospital, Neurology Department
Beijing, Beijing Municipality, 100730
Department of Neurology, West China Hospital, Sichuan University
Chengdu, Sichuan, 610044
Department of Neurology, Fujian Medical University Union Hospital
Fuzhou, Fujian, 350001
Department of Neurology, Guangdong Neuroscience Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences
Guangzhou, Guangdong, 510080
Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, 200025
Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University
Suzhou, Jiangsu, 215004
Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430022
About this study
Official title: Machine Vision Based Machine Rating of MDS-UPDRS III
The Movement Disorders Society (MDS) Unified Parkinson's Disease Rating Scale (UPDRS) Part III (MDS-UPDRS III) is the primary assessment method for motor symptoms in Parkinson's disease patients. Currently, movement disorder specialists conduct semi-quantitative scoring, which entails limitations such as subjectivity, weak sensitivity, and a limited number of professional physicians. This study, based on machine vision, establishes gold standard labels according to expert scoring. By using machine learning, we develop a machine rating model and compare the model's performance with gold standard rating and general clinical rating to investigate the accuracy of machine vision-based MDS-UPDRS III machine rating.
- Conditions
- Parkinsonian Disorders, Machine Learning
- Being tested
- video recording
- Medical subject headings
- Parkinsonian Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
video recordingOther
Patients' performance of MDS-UPDRS III will be recorded.
What is being measured
The primary outcome is the main question the study is designed to answer.
Item-level: MAE
At the item level, mean absolute error (MAE) between paired AI scores and consensus reference scores, calculated as the average absolute difference across items. Lower values indicate closer agreement.
Measured: 1 day
Item-level: P(|Δ|≥2)
At the item level, the proportion of paired AI and consensus reference scores with an absolute difference of 2 or more points. Lower values indicate fewer large item-level scoring disagreements.
Measured: 1 day
Score-level: ICC
Intraclass correlation coefficient (ICC) between AI-derived scores and consensus reference scores, calculated separately for each of the four subdomain scores and the total score. Higher ICC values indicate greater agreement.
Measured: 1 day
Score-level: SEM
Standard error of measurement (SEM) for AI-derived scores relative to consensus reference scores, calculated separately for each of the four subdomain scores and the total score. SEM quantifies measurement error in the units of the corresponding score, with lower values indicating greater measurement precision.
Measured: 1 day
Score-level: SDC
Smallest detectable change (SDC), derived from the measurement error and calculated separately for each of the four subdomain scores and the total score. SDC represents the minimum score change required to exceed expected measurement error. Lower values indicate greater measurement precision.
Measured: 1 day
Secondary outcomes (9)
Item-level: Within-one agreement (ACC1; P(|Δ| ≤ 1))
At the item level, ACC1 is defined as the proportion of paired AI and consensus reference scores with an absolute difference of no more than 1 point, i.e., P(|Δ| ≤ 1). Higher values indicate closer item-level agreement.
Measured: 1 day
Item-level: Exact-error proportions [P(|Δ| = k)]
At the item level, exact-error proportions, P(|Δ| = k), represent the proportions of paired AI and consensus reference scores with each exact absolute error magnitude k. In particular, P(|Δ| = 0) corresponds to exact-match accuracy (ACC), defined as the proportion of items for which the AI score exactly matches the consensus reference score. The remaining values of k characterize the distribution of item-level scoring errors.
Measured: 1 day
Item-level: Per-class exact recall
At the item level, for each consensus reference score class, the proportion of items for which the AI score exactly matches the consensus reference score among all items belonging to that reference class. Higher values indicate better class-specific exact agreement.
Measured: 1 day
Item-level: Confusion matrix
At the item level, a cross-tabulation of AI scores against consensus reference scores, showing the number of paired ratings for each combination of reference and AI score categories. Rows represent consensus reference scores and columns represent AI scores.
Measured: 1 day
Item-level: Row-normalised confusion matrix
At the item level, the confusion matrix normalised within each consensus reference score row so that each row sums to 1, showing the distribution of AI scores conditional on each reference score class.
Measured: 1 day
Score-level: Limits of agreement
Bland-Altman limits of agreement between AI-derived scores and consensus reference scores, calculated separately for each of the four subdomain scores and the total score. The limits characterize the range within which most paired differences between AI and reference scores are expected to fall.
Measured: 1 day
Score-level: MAE
Mean absolute error (MAE) between AI-derived scores and consensus reference scores, calculated separately for each of the four subdomain scores and the total score as the average absolute difference between paired scores. Lower values indicate smaller scoring errors.
Measured: 1 day
Score-level: RMSE
Root mean square error (RMSE) between AI-derived scores and consensus reference scores, calculated separately for each of the four subdomain scores and the total score. RMSE gives greater weight to larger scoring errors, with lower values indicating closer agreement.
Measured: 1 day
Score-level: Spearman correlation
Spearman rank correlation between AI-derived scores and consensus reference scores, calculated separately for each of the four subdomain scores and the total score. Higher values indicate a stronger monotonic association between AI-derived and reference scores.
Measured: 1 day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jun 2023
- Study start1 Mar 2023
- Primary completion31 Dec 2025
- Study completion31 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Parkinsonian DisordersStudies of the same condition, open and under way.
- More trials run by Ruijin HospitalThe sponsor's other registered studies.
- 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
- Cross-sectional
- Enrolment
- 1,679 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ruijin Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Second Affiliated Hospital of Soochow University, Beijing Hospital, Beijing Tiantan Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Fujian Medical University Union Hospital, Guangdong Provincial People's Hospital, West China Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05906719
- Sponsor's protocol ID
- u3
- Status last verified
- Aug 2026
- First submitted
- 7 Jun 2023
- Last updated
- 21 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Vision Based Machine Rating of MDS-UPDRS III
- ClinicalTrials.gov ID
- NCT05906719
- Acronym / sponsor ID
- u3
- Conditions
- Parkinsonian Disorders, Machine Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,679 participants (actual)
- Lead sponsor
- Ruijin HospitalUniversity, hospital or other
- Collaborators
- Second Affiliated Hospital of Soochow University, Beijing Hospital, Beijing Tiantan Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Fujian Medical University Union Hospital, Guangdong Provincial People's Hospital, West China Hospital
- Study start
- 1 Mar 2023 (actual)
- Primary completion
- 31 Dec 2025 (actual)
- Study completion
- 31 Dec 2025 (actual)
- First posted
- 18 Jun 2023 (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.
The Movement Disorders Society (MDS) Unified Parkinson's Disease Rating Scale (UPDRS) Part III (MDS-UPDRS III) is the primary assessment method for motor symptoms in Parkinson's disease patients. Currently, movement disorder specialists conduct semi-quantitative scoring, which entails limitations such as subjectivity, weak sensitivity, and a limited number of professional physicians. This study, based on machine vision, establishes gold standard labels according to expert scoring. By using machine learning, we develop a machine rating model and compare the model's performance with gold standard rating and general clinical rating to investigate the accuracy of machine vision-based MDS-UPDRS III machine rating.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 20 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- In this study, participants are mainly recruited from the Department of Neurology at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, as well as from movement disorder clinics at 7 other centers. Participants are enrolled through outpatient assessment and screening. Two specialists in movement disorders independently diagnosed participants as having "Parkinsonism". In cases where there is a diagnostic disagreement, the Movement Disorder Specialist Group at the Department of Neurology, Ruijin Hospital collectively discusses and makes the decision.
Key inclusion criteria (3 listed)
- Meeting the diagnostic criteria for Parkinsonism established by the International Movement Disorder Society: having bradykinesia, and meeting at least one of the two criteria for resting tremor or muscle rigidity
- 20 to 80 years old
- Good compliance, voluntarily joining the study, and able to sign an informed consent form or have it signed by a legal representative
Key exclusion criteria (4 listed)
- Significant cognitive impairment (MMSE ≤ 23)
- Unable to sign written informed consent or unable to complete the trial due to other reasons
- Other situations in which the researcher deems the participant unsuitable for this study
- Participation in other clinical trials
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 |
|---|---|---|
| video recordingPatients' performance of MDS-UPDRS III will be recorded. | Other | – |
Primary outcome
Item-level: MAE
Time frame: 1 day
At the item level, mean absolute error (MAE) between paired AI scores and consensus reference scores, calculated as the average absolute difference across items. Lower values indicate closer agreement.
Item-level: P(|Δ|≥2)
Time frame: 1 day
At the item level, the proportion of paired AI and consensus reference scores with an absolute difference of 2 or more points. Lower values indicate fewer large item-level scoring disagreements.
Score-level: ICC
Time frame: 1 day
Intraclass correlation coefficient (ICC) between AI-derived scores and consensus reference scores, calculated separately for each of the four subdomain scores and the total score. Higher ICC values indicate greater agreement.
Score-level: SEM
Time frame: 1 day
Standard error of measurement (SEM) for AI-derived scores relative to consensus reference scores, calculated separately for each of the four subdomain scores and the total score. SEM quantifies measurement error in the units of the corresponding score, with lower values indicating greater measurement precision.
Score-level: SDC
Time frame: 1 day
Smallest detectable change (SDC), derived from the measurement error and calculated separately for each of the four subdomain scores and the total score. SDC represents the minimum score change required to exceed expected measurement error. Lower values indicate greater measurement precision.
9 secondary outcomes
Item-level: Within-one agreement (ACC1; P(|Δ| ≤ 1))
Time frame: 1 day
Item-level: Exact-error proportions [P(|Δ| = k)]
Time frame: 1 day
Item-level: Per-class exact recall
Time frame: 1 day
Item-level: Confusion matrix
Time frame: 1 day
Item-level: Row-normalised confusion matrix
Time frame: 1 day
Score-level: Limits of agreement
Time frame: 1 day
Score-level: MAE
Time frame: 1 day
Score-level: RMSE
Time frame: 1 day
Score-level: Spearman correlation
Time frame: 1 day
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Aug 2026.
- 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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
8 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Parkinsonian Disorders and Machine Learning, run by Ruijin Hospital at 8 sites in China.
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 3 inclusion and 4 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: finished, as of the sponsor's last update on 21 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 8 sites in China. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Item-level: MAE (time frame: 1 day). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 21 Aug 2026.