Phenomenology of Levodopa-Induced Dyskinesia

Zis, Panagiotis; Samuel, Michael

doi:10.1007/978-1-4471-6503-3_1

Cited by 4 publications

(4 citation statements)

References 74 publications

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“…However, after prolonged levodopa therapy, 40% of individuals develop levodopa-induced dyskinesia (LID) within 4–6 years [ 8 ]. LIDs are involuntary movements characterized by a non-rhythmic motion flowing from one body part to another (chorea) and/or involuntary contractions of opposing muscles causing twisting of the body into abnormal postures (dystonia) [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation

Mestre

Fox

et al. 2018

J NeuroEngineering Rehabil

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BackgroundDespite the effectiveness of levodopa for treatment of Parkinson’s disease (PD), prolonged usage leads to development of motor complications, most notably levodopa-induced dyskinesia (LID). Persons with PD and their physicians must regularly modify treatment regimens and timing for optimal relief of symptoms. While standardized clinical rating scales exist for assessing the severity of PD symptoms, they must be administered by a trained medical professional and are inherently subjective. Computer vision is an attractive, non-contact, potential solution for automated assessment of PD, made possible by recent advances in computational power and deep learning algorithms. The objective of this paper was to evaluate the feasibility of vision-based assessment of parkinsonism and LID using pose estimation.MethodsNine participants with 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). Movement trajectories of individual joints were extracted from videos of PD assessment using Convolutional Pose Machines, a pose estimation algorithm built with deep learning. Features of the movement trajectories (e.g. kinematic, frequency) were used to train random forests to detect and estimate the severity of parkinsonism and LID. 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.ResultsFor LID, the communication task yielded the best results (detection: AUC = 0.930, severity estimation: r = 0.661). 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.ConclusionThe proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD and demonstrates promising performance for the future translation of deep learning to PD clinical practices. Convenient and objective assessment of PD symptoms will facilitate more frequent touchpoints between patients and clinicians, leading to better tailoring of treatment and quality of care.

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Section: Introductionmentioning

confidence: 99%

Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation

Mestre

Fox

et al. 2018

J NeuroEngineering Rehabil

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“…BoNT injections may also be effective in severe retrocollis in PSP, particularly when painful. A small-blinded crossover trial comparing BoNT injections into the lumbar paraspinal muscles versus placebo in PD patients with Pisa syndrome showed significant improvement in posture after BoNT [85]. However, these results have not yet been replicated in other centers, and this approach cannot be recommended at the moment [95,97].…”

Section: Treatmentmentioning

confidence: 99%

“…Levodopa-induced lower limb dystonia points in the direction of CBS, unlike PD in which lower limb dystonia tends to be improved by levodopa [12,45,[49][50][51][52]. Indeed, PD dystonia represents a hypodopaminergic state in wearing-off states (e.g., nocturnal or early morning foot dystonia) or in the form of diphasic dyskinesia (e.g., beginning-of-dose and end-of-dose dyskinesia) [14,[78][79][80][81][82][83][84][85][86][87]. Exceptions notwithstanding, dystonia in APD is often a presenting manifestation [10,11,36]; in PD, except for monogenic parkinsonism in which dystonia (particularly of the lower limb) may be seen earlier in the disease course, is an expression of advanced disease [70,75].…”

Section: Diagnostic Cluesmentioning

confidence: 99%

Dystonia in atypical parkinsonian disorders

Marsili

Bologna

Kojović

et al. 2019

Parkinsonism & Related Disorders

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“…Our aim is the development of a machine learning system for the automatic assessment of LID, which provokes involuntary movements characterized by a non-rhythmic motion flowing from one body part to another (chorea) and/or involuntary contractions of opposing muscles causing the twisting of the body into abnormal postures (dystonia) [51]. Given that UDysRS consists of the assessment measure of LID, our work focuses on the estimation of UDysRS scores.…”

Section: Datasetmentioning

confidence: 99%

Learning Spatio-Temporal Radon Footprints for Assessment of Parkinson’s Dyskinesia

Theofilou,

Tsatiris,

Kollias

2024

Electronics

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Parkinson’s disease is a severe neurodegenerative disorder that leads to loss of control over various motor and mental functions. Its progression can be limited with medication, particularly through the use of levodopa. However, prolonged administration of levodopa often results in disorders independent of those caused by the disease. The detection of these disorders is based on the clinical examination of patients, through different type of activities and tasks, using the Unified Dyskinesia Rating Scale (UDysRS). In the present work, our aim is to develop a state-of-the-art assessment system for levodopa-induced dyskinesia (LID) using the joint coordinate data of a human skeleton body depicted on videotaped activities related to UDysRS. For this reason, we combine a robust mathematical method for encoding action sequences known as Spatio-temporal Radon Footprints (SRF) with a Convolutional Neural Network (CNN), in order to estimate dyskinesia’s ratings for six body parts. We introduce two different methodological approaches: Global SRF-CNN and Local SRF-CNN, based on the set of skeletal points used in the encoding scheme. A comparison between these approaches reveals that Local SRF-CNN demonstrates better performance than the Global one. Finally, Local SRF-CNN outperforms the state-of-the-art technique, on both tasks, for UDysRS dyskinesia assessment, using joint coordinate data of the human body, achieving an overall performance in mean RMSE value of 0.6198 for Drinking task and 0.4885 for Communication, compared to 0.6575 and 0.5175, respectively. This illustrates the ability of the proposed machine learning system to successfully assess LID.

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Phenomenology of Levodopa-Induced Dyskinesia

Cited by 4 publications

References 74 publications

Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation

Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation

Dystonia in atypical parkinsonian disorders

Learning Spatio-Temporal Radon Footprints for Assessment of Parkinson’s Dyskinesia

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