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Sabo A, Iaboni A, Taati B, Fasano A, Gorodetsky C. 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. Biomed Eng Online 2023; 22:120. [PMID: 38082277 PMCID: PMC10714555 DOI: 10.1186/s12938-023-01175-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 11/19/2023] [Indexed: 12/18/2023] Open
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.
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Affiliation(s)
- Andrea Sabo
- KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Andrea Iaboni
- KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
- Department of Psychiatry, University of Toronto, 250 College Street, 8th Floor, Toronto, ON, M5T 1R8, Canada
- Centre for Mental Health, University Health Network, 33 Russell Street, Toronto, ON, M5S 2S1, Canada
| | - Babak Taati
- KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
- Department of Computer Science, University of Toronto, 10 King's College Road, Room 3302, Toronto, ON, M5S 3G4, Canada
- Institute of Biomedical Engineering, University of Toronto, 164 College Street. Room 407, Toronto, ON, M2S 3G9, Canada
- Vector Institute, 661 University Ave Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Alfonso Fasano
- KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
- Division of Neurology, The Hospital for Sick Children, University of Toronto, 555 University Avenue, Toronto, ON, M5G 1X8, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, ON, Canada
- Krembil Brain Institute, Toronto, ON, Canada
- CenteR for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
| | - Carolina Gorodetsky
- Division of Neurology, The Hospital for Sick Children, University of Toronto, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, ON, Canada.
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