Vision-Based Method for Automatic Quantification of Parkinsonian Bradykinesia

Video-Based Analyses of Parkinson’s Disease Severity: A Brief Review

“…In a recent study combining video-based analyses with machine learning techniques, severe motion blur and fluency issues with videos made it difficult for the AI system to score aspects of bradykinesia in 60 patients with PD [ 34 ]. Rating items of bradykinesia using the MDS-UPDRS requires scoring the fluency and quality of movement, and likewise characterising small amplitude tremor relies on discernment.…”

“…The development of such tools and applications to an illness such as PD requires extensive model optimisation using data from large numbers of individuals and then complex validation with careful consideration of the gold standard against which the tool should be validated, given that our human clinical skills are intrinsically flawed, and patient performance varies according to fatigue, medication and time of day. In addition, most machine learning techniques such as those described above, are supervised, for example, an AI model dedicated to scoring a video according to the MDS-UPDRS is trained using clinician’s scores of MDS-UPDRS [ 34 ]. Therefore, at best, the accuracy of machine learning techniques will be as good as the clinician assessment of MDS- UPDRS, which already presents issues with inter/intra rater variability, thus there is a need to demonstrate greater inter/intra assessment reliability using AI tools which would further the argument that AI can provide truly objective ratings.…”

“…In addition, a deep learning method (utilised by multiple companies in Table 2 ), works by training a model using example data that is inputted, to be able to subsequently identify PD from novel data and this heavily relies on large amounts of high quality labelled data, to ensure that the model achieves state of the art accuracy. One caveat with the current research into automated video-based assessments is that often the models are trained using small samples of cognitively intact, predominantly white participants, that are relatively younger, and present milder symptoms of disease (Hoehn and Yahr stage 2) [ 34, 37, 38, 41 ], which may bias the automated assessment framework and thus findings cannot necessarily be generalised to the wider population of people living with PD. One study has demonstrated that it is possible to apply automated video-based assessment to an older population with lower cognitive status [ 42 ], but this is yet to be demonstrated in the older population of people with PD, who have a more severe disease status.…”

“…r e c t e d A u t h o r P r o o f older population with lower cognitive status [42], butthis is yet to be demonstrated in the older population of people with PD, who have a more severe disease status.So far, recent research developing computer vision-based methods for AI analysis have shown success in scoring bradykinesia, gait and facial expressions, as well as AI scores showing good correlation with clinician ratings among these studies[34,[38][39][40][41][42][43][44]. At present, the application of AI to diagnosis of movement disorder patients will likely only ever be used in conjunction with human expert movement disorders clinicians, since rigorous validation of AI technology and subsequent regulatory approval would be required.…”

Video Based Shuffling Step Detection for Parkinsonian Patients Using 3D Convolution

“…A 3D convolutional neural network was designed to estimate the viewing angle and perform subject identification. Liu et al [19] proposed a video-based method to quantify hand movement bradykinesia severity on PD patients. Human pose estimation method was used to get finger joints' locations and then an SVM classifier used them to generate score ratings.…”