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Article type: Research Article
Authors: Sibley, Kristaa | Girges, Christinea | Candelario, Josepha | Milabo, Catherinea | Salazar, Maricela | Esperida, John Onila | Dushin, Yuriyb | Limousin, Patriciaa | Foltynie, Thomasa; *
Affiliations: [a] Department of Clinical & Movement Neurosciences, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK | [b] Machine Medicine Technologies, London, UK
Correspondence: [*] Correspondence to: Professor T. Foltynie, PhD, Box 146, National Hospital for Neurology & Neurosurgery, Queen Square, London. WC1N 3BG, UK. Tel.: +44 0 203 448 8726; E-mail: T.Foltynie@ucl.ac.uk.; ORCID: 0000-0003-0752-1813
Abstract: Background:Parkinson’s disease severity is typically measured using the Movement Disorder Society Unified Parkinson’s disease rating scale (MDS-UPDRS). While training for this scale exists, users may vary in how they score a patient with the consequence of intra-rater and inter-rater variability. Objective:In this study we explored the consistency of an artificial intelligence platform compared with traditional clinical scoring in the assessment of motor severity in PD. Methods:Twenty-two PD patients underwent simultaneous MDS-UPDRS scoring by two experienced MDS-UPDRS raters and the two sets of accompanying video footage were also scored by an artificial intelligence video analysis platform known as KELVIN. Results:KELVIN was able to produce a summary score for 7 MDS-UPDRS part 3 items with good inter-rater reliability (Intraclass Correlation Coefficient (ICC) 0.80 in the OFF-medication state, ICC 0.73 in the ON-medication state). Clinician scores had exceptionally high levels of inter-rater reliability in both the OFF (0.99) and ON (0.94) medication conditions (possibly reflecting the highly experienced team). There was an ICC of 0.84 in the OFF-medication state and 0.31 in the ON-medication state between the mean Clinician and mean Kelvin scores for the equivalent 7 motor items, possibly due to dyskinesia impacting on the KELVIN scores. Conclusion:We conclude that KELVIN may prove useful in the capture and scoring of multiple items of MDS-UPDRS part 3 with levels of consistency not far short of that achieved by experienced MDS-UPDRS clinical raters, and is worthy of further investigation.
Keywords: Artificial intelligence, clinical trials, digital measures, Parkinson’s disease, remote monitoring
DOI: 10.3233/JPD-223493
Journal: Journal of Parkinson's Disease, vol. 12, no. 7, pp. 2223-2233, 2022
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