Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation

Objective: To apply deep learning pose estimation algorithms for vision-based assessment of parkinsonism and levodopa-induced dyskinesia (LID). Methods: Nine participants with Parkinson's disease (PD) and LID completed a levodopa infusion protocol, where symptoms were assessed at regular intervals using the Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating Scale (UPDRS). A state-of-the-art deep learning pose estimation method was used to extract movement trajectories from videos of PD assessments. Features of the movement trajectories were used to detect and estimate the severity of parkinsonism and LID using random forest. Communication and drinking tasks were used to assess LID, while leg agility and toe tapping tasks were used to assess parkinsonism. Feature sets from tasks were also combined to predict total UDysRS and UPDRS Part III scores. Results: For LID, the communication task yielded the best results for dyskinesia (severity estimation: r = 0.661, detection: AUC = 0.930). For parkinsonism, leg agility had better results for severity estimation (r = 0.618), while toe tapping was better for detection (AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741 and 0.530, respectively. Conclusion: This paper presents the first application of deep learning for vision-based assessment of parkinsonism and LID and demonstrates promising performance for the future translation of deep learning to PD clinical practices. Significance: The proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD.Comment: 8 pages, 1 figure. Under revie

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

Remote and objective assessment of the motor symptoms of Parkinson's disease is an area of great interest particularly since the COVID-19 crisis emerged. In this paper, we focus on a) the challenges of assessing motor severity via videos and b) the use of emerging video-based Artificial Intelligence (AI)/Machine Learning techniques to quantitate human movement and its potential utility in assessing motor severity in patients with Parkinson's disease. While we conclude that video-based assessment may be an accessible and useful way of monitoring motor severity of Parkinson's disease, the potential of video-based AI to diagnose and quantify disease severity in the clinical context is dependent on research with large, diverse samples, and further validation using carefully considered performance standards

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way

How Does Technology Development Influence the Assessment of Parkinson’s Disease? A Systematic Review

abstract: Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The pathology for PD is difficult and expensive. Furthermore, it depends on patient diaries and the neurologist’s subjective assessment of clinical scales. Objective, accurate, and continuous patient monitoring have become possible with the advancement in mobile and portable equipment. Consequently, a significant amount of work has been done to explore new cost-effective and subjective assessment methods or PD symptoms. For example, smart technologies, such as wearable sensors and optical motion capturing systems, have been used to analyze the symptoms of a PD patient to assess their disease progression and even to detect signs in their nascent stage for early diagnosis of PD. This review focuses on the use of modern equipment for PD applications that were developed in the last decade. Four significant fields of research were identified: Assistance diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study reviews the papers published between January 2008 and December 2018 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. After removing unrelated articles, ones published in languages other than English, duplicate entries and other articles that did not fulfill the selection criteria, 778 papers were manually investigated and included in this review. A general overview of PD applications, devices used and aspects monitored for PD management is provided in this systematic review.Dissertation/ThesisMasters Thesis Computer Engineering 201

Improving Reinforcement Learning Techniques for Medical Decision Making

Reinforcement learning (RL) is a powerful tool for developing personalized treatment regimens from healthcare data. In RL, an agent samples experiences from an environment (such as a model of patient health) to learn a policy that maximizes long-term reward. This dissertation proposes methodological and practical developments in the application of RL to treatment planning problems. First, we develop a novel time series model for simulating patient health states from observed clinical data. We use a generative neural network architecture that learns a direct mapping between distributions over clinical measurements at adjacent time points. We show that this model produces realistic patient trajectories and can be paired with on-policy RL to learn effective treatment policies. Second, we develop a novel extension of hidden Markov models, which are commonly used to model and predict patient health states. Specifically, we develop a special case of recurrent neural networks with the same likelihood function as a corresponding discrete-observation hidden Markov model. We demonstrate how combining our model with other predictive neural networks improves disease forecasting and offers novel clinical interpretations compared with a standard hidden Markov model. Third, we develop a method for selecting high-performing reinforcement learning-based treatment policies for underrepresented patient subpopulations using limited observations. Our method learns a probability distribution over treatment policies from a reference patient group, then adapts its recommendations using limited data from an underrepresented patient group. We show that our method outperforms state-of-the-art benchmarks in selecting effective treatment policies for patients with non-typical clinical characteristics, and predicting these patients\u27 outcomes under its policies. Finally, we use RL to optimize medication regimens for Parkinson\u27s disease patients using high-frequency wearable sensor data. We build an environment model of how patients\u27 symptoms respond to medication, then use RL to recommend optimal medication types, timing, and dosages for each patient. We show that these patient-specific RL-prescribed medication regimens outperform physician-prescribed regimens and provide clinically defensible treatment strategies. Our framework also enables physicians to identify patients who could could switch to lower-frequency regimens for improved adherence, and to identify patients who may be candidates for advanced therapies

Circuit-level analyses of cortico-basal ganglia-thalamic networks. Effects of dopamine dysregulation and experience dependent plasticity.

The cortico-basal ganglia-thalamic (CBT) circuit is thought to be involved in control of voluntary and goal-directed movements and action selection. Dopamine is known to play a crucial role in this circuit and regulating its activity. The important role of dopamine is particularly evident in Parkinson’s patients, where dopaminergic cells are dying and motor impairments follow. While dopamine replacement is an effective therapy, satisfactory alleviation only lasts for a limited number of years, after which patients frequently develop side-effects in the form of levodopa-induced dyskinesia. In order to clarify the neurophysiological consequences of dopamine dysregulation we have here investigated the electrophysiological activity of each part of the CBT-loop in rats during different experimental conditions, using custom made multi-channel electrodes. Neuronal activity changes in 16 CBT structures were characterized upon acute pharmacological dopaminergic manipulations and firing rate changes of subgroup of cells within different structures in the CBT circuit were shown to potentially be responsible for the severe akinesia induced by the drugs. We have also developed a novel method to monitor the global state of the CBT circuit in a rat model of levodopa-induced dyskinesia and showed how this approach can be used to help developing new pharmacological therapies. Lastly, to investigate how somatosensory input is affecting motor circuits, we have recorded activity of the whole CBT-loop in rats before and after extensive skilled forelimb reaching and grasping training. Preliminary results show that only the motor cortex display experience-dependent changes due to the reaching training