Inter-rater variability in motor function assessment in Parkinson's disease between experts in movement disorders and nurses specialising in PD management

Fonticoba, Teresa de Deus; Garcia, David; Arribí, M. Macías

doi:10.1016/j.nrleng.2017.03.006

Cited by 11 publications

(10 citation statements)

References 29 publications

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“…One limitation of this study is that one of the clinicians was not blinded to the ON/OFF status of the participants which may have also biased the score assigned. A previous study has noted that there was moderate variability in MDS-UPDRS part III gait score annotations when rated by multiple clinicians (ICC = 0.746) [ 16 ]. This is similar to the ICC of 0.73 noted between the two clinicians’ ratings on the PD dataset in this study.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease

Sabo,

Iaboni,

Taati

et al. 2023

BioMed Eng OnLine

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Introduction Gait impairments in Parkinson’s disease (PD) are treated with dopaminergic medication or deep-brain stimulation (DBS), although the magnitude of the response is variable between individuals. Computer vision-based approaches have previously been evaluated for measuring the severity of parkinsonian gait in videos, but have not been evaluated for their ability to identify changes within individuals in response to treatment. This pilot study examines whether a vision-based model, trained on videos of parkinsonism, is able to detect improvement in parkinsonian gait in people with PD in response to medication and DBS use. Methods A spatial–temporal graph convolutional model was trained to predict MDS-UPDRS-gait scores in 362 videos from 14 older adults with drug-induced parkinsonism. This model was then used to predict MDS-UPDRS-gait scores on a different dataset of 42 paired videos from 13 individuals with PD, recorded while ON and OFF medication and DBS treatment during the same clinical visit. Statistical methods were used to assess whether the model was responsive to changes in gait in the ON and OFF states. Results The MDS-UPDRS-gait scores predicted by the model were lower on average (representing improved gait; p = 0.017, Cohen’s d = 0.495) during the ON medication and DBS treatment conditions. The magnitude of the differences between ON and OFF state was significantly correlated between model predictions and clinician annotations (p = 0.004). The predicted scores were significantly correlated with the clinician scores (Kendall’s tau-b = 0.301, p = 0.010), but were distributed in a smaller range as compared to the clinician scores. Conclusion A vision-based model trained on parkinsonian gait did not accurately predict MDS-UPDRS-gait scores in a different PD cohort, but detected weak, but statistically significant proportional changes in response to medication and DBS use. Large, clinically validated datasets of videos captured in many different settings and treatment conditions are required to develop accurate vision-based models of parkinsonian gait.

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Section: Discussionmentioning

confidence: 99%

Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease

Sabo,

Iaboni,

Taati

et al. 2023

BioMed Eng OnLine

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show abstract

“…The assessment can be standardized, for instance, according to the Unified Parkinson's Disease Rating Scale of the Movement Disorder Society (MDS-UPDRS) [8]. The inter-rater variability between experts and trained staff for UPDRS-III is generally high (ICC = 0.749, p < 0.0001) [9,10], but in everyday clinical practice under time pressure, objective parameters are sought. The UPDRS achieves a high test-retest reliability with an ICC of 0.9 [9], indicating a high intra-rater reliability [10][11][12].…”

Section: Figure 1 Visual Description Of Muscle Shear Wave Elastograph...mentioning

confidence: 99%

“…The inter-rater variability between experts and trained staff for UPDRS-III is generally high (ICC = 0.749, p < 0.0001) [9,10], but in everyday clinical practice under time pressure, objective parameters are sought. The UPDRS achieves a high test-retest reliability with an ICC of 0.9 [9], indicating a high intra-rater reliability [10][11][12]. However, UPDRS only allows a coarse-grained assessment within an integer from 0 to 4, whereas SWE offers a more fine-grained evaluation.…”

Section: Figure 1 Visual Description Of Muscle Shear Wave Elastograph...mentioning

confidence: 99%

Ultrasound of the Biceps Muscle in Idiopathic Parkinson’s Disease with Deep Brain Stimulation: Rigidity Can Be Quantified by Shear Wave Elastography

et al. 2023

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Rigidity in Parkinson’s disease (PD) is assessed by clinical scales, mostly the Unified Parkinson’s Disease Rating Scale of the Movement Disorders Society (MDS-UPDRS). While the MDS-UPDRS-III ranges on an integer from 0 to 4, we investigated whether muscle ultrasound shear wave elastography (SWE) offers a refined assessment. Ten PD patients (five treated with deep brain stimulation (DBS) and levodopa, five with levodopa only) and ten healthy controls were included. Over a period of 80 min, both the SWE value and the item 22b-c of the MDS-UPDRS-III were measured at 5 min intervals. The measurements were performed bilaterally at the biceps brachii muscle (BB) and flexor digitorum profundus muscle in flexion and passive extension. Rigidity was modified and tracked under various therapeutic conditions (with and without medication/DBS). The feasibility of SWE for objective quantification was evaluated by correlation with the UPDRS-III: considering all positions and muscles, there was already a weak correlation (r = 0.01, p < 0.001)—in a targeted analysis, the BB in passive extension showed a markedly higher correlation (r = 0.494, p < 0.001). The application of dopaminergic medication and DBS resulted in statistically significant short-term changes in both clinical rigidity and SWE measurements in the BB (p < 0.001). We conclude that rigidity is reflected in the SWE measurements, indicating that SWE is a potential non-invasive quantitative assessment tool for PD.

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“…The Unified Parkinson's Disease Rating Scale (UPDRS) is a commonly used set of clinical tests to assess the state of Parkinson's disease. These tests include, for example, a walking test for assessing the subject's gait [8]. However, Parkinson's disease symptoms' clinical assessment suffers from significant interrater variability due to its subjective nature [8].…”

Section: Introductionmentioning

confidence: 99%

“…These tests include, for example, a walking test for assessing the subject's gait [8]. However, Parkinson's disease symptoms' clinical assessment suffers from significant interrater variability due to its subjective nature [8]. For these reasons, an objective assessment method based on automatic analysis of the movement data recorded continuously in the patient's natural living environment would have the potential to significantly improve the effectiveness of care for patients with Parkinson's disease and other neurological diseases.…”

Section: Introductionmentioning

confidence: 99%

Mobilemicroservices Architecture for Remote Monitoring of Patients: A Feasibility Study

Ruokolainen

Haladijan

Juutinen

et al. 2022

MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation

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Recent developments in smart mobile devices (SMDs), wearable sensors, the Internet, mobile networks, and computing power provide new healthcare opportunities that are not restricted geographically. This paper aims to introduce Mobilemicroservices Architecture (MMA) based on a study on architectures. In MMA, an HTTP-based Mobilemicroservivce (MM) is allocated to each SMD’s sensor. The key benefits are extendibility, scalability, ease of use for the patient, security, and the possibility to collect raw data without the necessity to involve cloud services. Feasibility was investigated in a two-year project, where MMA-based solutions were used to collect motor function data from patients with Parkinson’s disease. First, we collected motor function data from 98 patients and healthy controls during their visit to a clinic. Second, we monitored the same subjects in real-time for three days in their everyday living environment. These MMA applications represent HTTP-based business-logic computing in which the SMDs’ resources are accessible globally.

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Inter-rater variability in motor function assessment in Parkinson's disease between experts in movement disorders and nurses specialising in PD management

Cited by 11 publications

References 29 publications

Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease

Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease

Ultrasound of the Biceps Muscle in Idiopathic Parkinson’s Disease with Deep Brain Stimulation: Rigidity Can Be Quantified by Shear Wave Elastography

Mobilemicroservices Architecture for Remote Monitoring of Patients: A Feasibility Study

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