Ignite Creation Date:
2026-03-26 @ 3:18 PM
Ignite Modification Date:
2026-03-31 @ 7:49 AM
Study NCT ID:
NCT07381751
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-02-02
First Post:
2026-01-13
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Artificial Intelligence-assisted MDS-UPDRS Assessment for Parkinson's Disease
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010300', 'term': 'Parkinson Disease'}], 'ancestors': [{'id': 'D020734', 'term': 'Parkinsonian Disorders'}, {'id': 'D001480', 'term': 'Basal Ganglia Diseases'}, {'id': 'D001927', 'term': 'Brain Diseases'}, {'id': 'D002493', 'term': 'Central Nervous System Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D009069', 'term': 'Movement Disorders'}, {'id': 'D000080874', 'term': 'Synucleinopathies'}, {'id': 'D019636', 'term': 'Neurodegenerative Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D057832', 'term': 'Watchful Waiting'}], 'ancestors': [{'id': 'D017063', 'term': 'Outcome Assessment, Health Care'}, {'id': 'D010043', 'term': 'Outcome and Process Assessment, Health Care'}, {'id': 'D011787', 'term': 'Quality of Health Care'}, {'id': 'D006298', 'term': 'Health Services Administration'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-03-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2029-02-28', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-01-26', 'studyFirstSubmitDate': '2026-01-13', 'studyFirstSubmitQcDate': '2026-01-26', 'lastUpdatePostDateStruct': {'date': '2026-02-02', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-02', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2029-02-28', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'AI-based motor assessment tool', 'timeFrame': 'Baseline to 3 years', 'description': 'AI-based motor assessment tool utilizing RGB video for reliable and objective ratings of MDS-UPDRS III motor symptoms, including rigidity and postural stability.'}, {'measure': 'Feasibility of implementing RGB camera-based assessments', 'timeFrame': '3 years', 'description': '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.'}], 'secondaryOutcomes': [{'measure': "System's effectiveness", 'timeFrame': '3 years', 'description': '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.'}, {'measure': 'Patient and clinician satisfaction', 'timeFrame': 'baseline to 3 years', 'description': '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.'}, {'measure': "System's performance", 'timeFrame': 'baseline to 3 years', 'description': '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.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ["artificial intelligence, MDS-UPDRS, Parkinson's disease"], 'conditions': ['Parkinson Disease']}, 'referencesModule': {'references': [{'pmid': '26474316', 'type': 'BACKGROUND', 'citation': "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': '38501363', 'type': 'BACKGROUND', 'citation': "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 abstract available."}, {'pmid': '30635244', 'type': 'BACKGROUND', 'citation': "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': '33103164', 'type': 'BACKGROUND', 'citation': "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': '19025984', 'type': 'BACKGROUND', 'citation': "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 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."}, {'pmid': '34950630', 'type': 'BACKGROUND', 'citation': "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': '34210995', 'type': 'BACKGROUND', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': '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.\n\nThe 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.\n\nTo 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.', 'detailedDescription': "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.\n\nBlinded 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '95 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': "patients with Parkinson's disease", 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age ≥18 years\n2. Diagnosis of "Clinically Established PD" as defined by the Movement Disorder Society Clinical Diagnostic Criteria for Parkinson\'s disease (MDS-PD criteria) \\[12\\]\n3. Able to provide informed consent and willing to participate in video-recorded MDS-UPDRS Part III assessments\n4. No significant visual, auditory, or musculoskeletal impairments that would interfere with video-based motor assessments\n\nExclusion Criteria:\n\n1. Unwillingness to be video recorded for study purposes\n2. 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'}, 'identificationModule': {'nctId': 'NCT07381751', 'briefTitle': "Artificial Intelligence-assisted MDS-UPDRS Assessment for Parkinson's Disease", 'organization': {'class': 'OTHER', 'fullName': 'Hong Kong University of Science and Technology'}, 'officialTitle': "Artificial Intelligence-assisted MDS-UPDRS Assessment for Parkinson's Disease", 'orgStudyIdInfo': {'id': 'HREP-2025-0238'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'PD group', 'description': "patients with Parkinson's disease", 'interventionNames': ['Other: Observational']}], 'interventions': [{'name': 'Observational', 'type': 'OTHER', 'description': 'clinical profile, MDS-UPDRS III, Video Recording', 'armGroupLabels': ['PD group']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Hong Kong', 'country': 'China', 'contacts': [{'name': 'Qian Zhang, PhD', 'role': 'CONTACT', 'email': 'qianzh@ust.hk', 'phone': '+852-23588766'}, {'name': 'Hiu Yi Wong, PhD', 'role': 'CONTACT', 'email': 'annawong@ust.hk', 'phone': '+852-23587344'}, {'name': 'Qian Zhang, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Hong Kong University of Science and Technology', 'geoPoint': {'lat': 22.27832, 'lon': 114.17469}}], 'centralContacts': [{'name': 'Qian Zhang, PhD', 'role': 'CONTACT', 'email': 'qianzh@ust.hk', 'phone': '+852-23588766'}, {'name': 'Hiu Yi Wong, PhD', 'role': 'CONTACT', 'email': 'annawong@ust.hk', 'phone': '+852-23587344'}], 'overallOfficials': [{'name': 'Qian Zhang, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Hong Kong University of Science and Technology'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Hong Kong University of Science and Technology', 'class': 'OTHER'}, 'collaborators': [{'name': 'The Queen Elizabeth Hospital', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Research Assistant Professor', 'investigatorFullName': 'Hiu Yi Wong', 'investigatorAffiliation': 'Hong Kong University of Science and Technology'}}}}