Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease

Author:

Sabo Andrea,Iaboni Andrea,Taati Babak,Fasano Alfonso,Gorodetsky Carolina

Abstract

Abstract Introduction Gait impairments in Parkinson’s disease (PD) are treated with dopaminergic medication or deep-brain stimulation (DBS), although the magnitude of the response is variable between individuals. Computer vision-based approaches have previously been evaluated for measuring the severity of parkinsonian gait in videos, but have not been evaluated for their ability to identify changes within individuals in response to treatment. This pilot study examines whether a vision-based model, trained on videos of parkinsonism, is able to detect improvement in parkinsonian gait in people with PD in response to medication and DBS use. Methods A spatial–temporal graph convolutional model was trained to predict MDS-UPDRS-gait scores in 362 videos from 14 older adults with drug-induced parkinsonism. This model was then used to predict MDS-UPDRS-gait scores on a different dataset of 42 paired videos from 13 individuals with PD, recorded while ON and OFF medication and DBS treatment during the same clinical visit. Statistical methods were used to assess whether the model was responsive to changes in gait in the ON and OFF states. Results The MDS-UPDRS-gait scores predicted by the model were lower on average (representing improved gait; p = 0.017, Cohen’s d = 0.495) during the ON medication and DBS treatment conditions. The magnitude of the differences between ON and OFF state was significantly correlated between model predictions and clinician annotations (p = 0.004). The predicted scores were significantly correlated with the clinician scores (Kendall’s tau-b = 0.301, p = 0.010), but were distributed in a smaller range as compared to the clinician scores. Conclusion A vision-based model trained on parkinsonian gait did not accurately predict MDS-UPDRS-gait scores in a different PD cohort, but detected weak, but statistically significant proportional changes in response to medication and DBS use. Large, clinically validated datasets of videos captured in many different settings and treatment conditions are required to develop accurate vision-based models of parkinsonian gait.

Funder

Walter and Maria Schroeder Institute for Brain Innovation and Recovery

National Sciences and Engineering Research Council

Alzheimer’s Association (USA) and Brain Canada

Canadian Institutes of Health Research

Vector Scholarship in Artificial Intelligence

Ontario Graduate Scholarship

AMS Healthcare Fellowship in Compassion and Artificial Intelligence

AbbVie

Boston Scientific Corporation

The Michael J. Fox Foundation

Medtronic

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Biomedical Engineering,General Medicine,Biomaterials,Radiological and Ultrasound Technology

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