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dc.contributor.authorWilliams, S.
dc.contributor.authorRelton, S.D.
dc.contributor.authorFang, H.
dc.contributor.authorAlty, J.
dc.contributor.authorQahwaji, Rami S.R.
dc.contributor.authorGraham, C.D.
dc.contributor.authorWong, D.C.
dc.date.accessioned2021-03-21T00:38:33Z
dc.date.accessioned2021-03-31T14:10:31Z
dc.date.available2021-03-21T00:38:33Z
dc.date.available2021-03-31T14:10:31Z
dc.date.issued2020-11
dc.identifier.citationWilliams S, Relton SD, Fang H et al (2020) Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos. Artificial Intelligence in Medicine. 110: 101966.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18414
dc.descriptionNoen_US
dc.description.abstractBackground: Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim: We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia. Methods: We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis. Results: A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. Conclusion: The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.artmed.2020.101966en_US
dc.subjectClassificationen_US
dc.subjectParkinson'sen_US
dc.subjectBradykinesiaen_US
dc.subjectVideoen_US
dc.subjectComputer visionen_US
dc.subjectDiagnosisen_US
dc.subjectSupport vector machineen_US
dc.titleSupervised classification of bradykinesia in Parkinson’s disease from smartphone videosen_US
dc.status.refereedYesen_US
dc.date.Accepted2020-10-02
dc.date.application2020-10-06
dc.typeArticleen_US
dc.type.versionNo full-text in the repositoryen_US
dc.date.updated2021-03-21T00:38:36Z
refterms.dateFOA2021-03-31T14:33:15Z
dc.openaccess.statusNot Open Accessen_US


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