A Pilot Study to Evaluate the Severity of Motor Dysfunction in Parkinson's Disease Based on AI Video Analysis
Condition: Parkinson Disease
MeSH terms: Parkinson Disease
Study type: Observational

Summary: 
      This study is a pilot study, mainly to explore the potential application value of specific
      pattern movement after video-based quantitative methods in the early recognition and
      assessment of Parkinson's disease. According to UPDRS III, a series of motion indicators
      related to the characteristics of the disease were determined and quantitatively analysed.
      Motor function scores were given by the senior physicians and the AI video analysis team
      separately to evaluate the accuracy of the scores of AI video analysis compared with that of
      senior specialists' team of movement disorders.
    

Detailed description: 
      In this study, participants are asked to perform video-recorded UPDRS-III test, and the
      unmarked motion feature identification and quantitative analysis based on the video were
      conducted.

      The investigators have collected standardized motor function videos of patients with
      Parkinson's disease from outpatient clinics and follow-up since August 2017. Based on the
      UPDRS III motor function test, the investigators screened five pattern actions that are both
      disease-characteristic and easy to visualize, including finger tapping, hand movements,
      pronation-supination movements of hands and gait test. With the assistance of the artificial
      intelligence team, the characteristic value quantification (frequency, distance, angle, etc.)
      of the above pattern actions has been initially implemented. Considering that the
      characteristic values of the pattern action under the identification of video are all
      continuous variables and cannot be directly compared with the discrete UPDRS rating scale,
      the investigators initially explored the construction of deep learning algorithm based on
      UPDRS rating in the previous work to verify the effectiveness of video analysis in motor
      function evaluation.

      In this study, the investigators plan to include patients with Parkinson's disease with
      different disease severity and analyse the consistency between UPDRS scores evaluated by
      specialists of movement disorders and video quantification score. The results of this study
      will hopefully lay a good foundation for launching a large-scale, multi-centre clinical trial
      in the future.
    

Intervention name(s): Video-based quantitative methods
Intervention type(s): Diagnostic Test
Intervention description(s): The AI video analysis is used to automatically identify motion details and quantify motion features such as amplitude and angular velocity, then an algorithm will be applied to generate the degree of motor dysfunction.
Observational model: Case-Control
Time perspective: Cross-Sectional
Sampling method: Probability Sample
Study population: 
        outpatients with idiopathic Parkinson's disease
      
Keywords: Parkinson Disease; Exploratory study; Quantitative method; AI Video analysis; Motor dysfunction