Artificial Intelligence-assisted MDS-UPDRS Assessment for Parkinson's Disease
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 500
- 试验地点
- 2
- 主要终点
- AI-based motor assessment tool
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 95 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
研究组 & 干预措施
PD group
patients with Parkinson's disease
干预措施: Observational (Other)
结局指标
主要结局
AI-based motor assessment tool
时间窗: 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
时间窗: 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.
次要结局
- System's effectiveness(3 years)
- Patient and clinician satisfaction(baseline to 3 years)
- System's performance(baseline to 3 years)
研究者
Hiu Yi Wong
Research Assistant Professor
Hong Kong University of Science and Technology