A Pilot Study to Evaluate the Severity of Motor Dysfunction in Parkinson's Disease Based on AI Video Analysis

NCT ID: NCT03655171

Last Updated: 2019-01-22

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 30 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-08-20
• Study Completion Date: 2018-11-20

Brief 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.

Conditions

Parkinson Disease

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: CROSS_SECTIONAL

Study Groups

H&Y 0

Age-matched non-disease population, or prodromal Parkinson's patients with no noticeable motor symptoms.

This group of patients will perform the video-recorded UPDRS-III test at the time of enrollment and evaluate the motor function using video-based quantitative methods.

Video-based quantitative methods

Intervention Type: DIAGNOSTIC_TEST

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.

H&Y 1

Hoehn and Yahr disability stage was 1: Unilateral involvement only usually with minimal or no functional disability.

This group of patients will perform the video-recorded UPDRS-III test at the time of enrollment and evaluate the motor function using video-based quantitative methods.

Video-based quantitative methods

Intervention Type: DIAGNOSTIC_TEST

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.

H&Y 2

Hoehn and Yahr disability stage was 2: Bilateral or midline involvement without impairment of balance.

This group of patients will perform the video-recorded UPDRS-III test at the time of enrollment and evaluate the motor function using video-based quantitative methods.

Video-based quantitative methods

Intervention Type: DIAGNOSTIC_TEST

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.

H&Y 3

Hoehn and Yahr disability stage was 3: Bilateral disease: mild to moderate disability with impaired postural reflexes; physically independent.

This group of patients will perform the video-recorded UPDRS-III test at the time of enrollment and evaluate the motor function using video-based quantitative methods.

Video-based quantitative methods

Intervention Type: DIAGNOSTIC_TEST

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.

H&Y 4

Hoehn and Yahr disability stage was 4: Severely disabling disease; still able to walk or stand unassisted.

This group of patients will perform the video-recorded UPDRS-III test at the time of enrollment and evaluate the motor function using video-based quantitative methods.

Video-based quantitative methods

Intervention Type: DIAGNOSTIC_TEST

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.

Interventions

Video-based quantitative methods

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• The primary criteria for diagnosis are idiopathic Parkinson's disease, defined as bradykinesia with resting tremor or muscular rigidity according to MDS-PD diagnosis criteria (2015). The examination of major symptoms needs to be performed by the methods described in the Unified Parkinson's Disease Rating Scale (UPDRS).

Exclusion Criteria

• 1. Definite cerebellar abnormalities, such as cerebellar gait, limb ataxia, or cerebellar eye movement abnormalities (such as sustained staring induced nystagmus, giant square wave beats, and distant hypertonia).
• 2. Downward vertical suprachiasmatic gaze palsy, or selective downward vertical scanning is slowing down.
• 3. Within five years of onset, the disease was diagnosed as a likely frontotemporal dementia or primary progressive aphasia (according to revised diagnostic criteria for frontotemporal dementia published in 2011).
• 4. With more than three years after the onset of the disease, Parkinson's disease symptoms are still limited to the lower limbs.
• 5. They have been treated with dopamine receptor blockers or dopamine depletion agents and the dosage and duration of administration are consistent with the diagnosis of drug-induced Parkinson's disease.
• 6. Although the condition is at least moderately severe, there is no discernible effect on high-dose levodopa therapy.
• 7. Definite cortical hypoesthesia (such as loss of skin writing and body recognition when the primary sensory function pattern is complete), definite motor aphasia of body concept, or progressive aphasia.
• 8. Functional neuroimaging study of the presynaptic dopaminergic system shows normal results.
• 9. There is evidence of Parkinson's disease or other diseases suspected to be related to the patient's symptoms, or professional physicians identify they may be other syndromes rather than Parkinson's disease based on a complete diagnostic assessment.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Tencent AI Lab

UNKNOWN

Sponsor Role: collaborator

Huashan Hospital

OTHER

Sponsor Role: lead

Responsible Party

Jian Wang

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Huashan Hospital

Shanghai, Shanghai Municipality, China

Countries

China

Other Identifiers

KY2018-340

Identifier Type: -

Identifier Source: org_study_id