Reference videos reduce variability of motor dysfunction assessments in multiple sclerosis

D’Souza, Marcus; Steinheimer, Saskia; Dorn, Jonas F.; Morrison, Cecily; Boisvert, Jonathan; Kravalis, Kristina; Burggraaff, Jessica; Munster, Caspar E.P. van; Diederich, Manuela; Sellen, Abigail; Kamm, Christian P.; Dahlke, Frank; Uitdehaag, Bernard M. J.; Kappos, Ludwig

doi:10.1177/2055217318792399

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Linkable database systems allow individual courses to be displayed in a standardized way over many years, and the data generated can be stored in a readable, transparent, and quickly retrievable form (12,13). Automated calculations lower the threshold for the systematic application of established scales such as Expanded Disability Status Scale (EDSS) or Multiple Sclerosis Functional Composite (MSFC), which are indispensable for the quantification of neurological deficits (14)(15)(16)(17)(18)(19). In addition to the standard instruments, patient-reported outcomes (PROs) increasingly complete the holistic assessment of the disease course (20,21).…”

Section: Patient Documentationmentioning

confidence: 99%

Data Collection in Multiple Sclerosis: The MSDS Approach

Ziemssen

Kern²,

Voigt

et al. 2020

Front. Neurol.

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Multiple sclerosis (MS) is a frequent chronic inflammatory disease of the central nervous system that affects patients over decades. As the monitoring and treatment of MS become more personalized and complex, the individual assessment and collection of different parameters ranging from clinical assessments via laboratory and imaging data to patient-reported data become increasingly important for innovative patient management in MS. These aspects predestine electronic data processing for use in MS documentation. Such technologies enable the rapid exchange of health information between patients, practitioners, and caregivers, regardless of time and location. In this perspective paper, we present our digital strategy from Dresden, where we are developing the Multiple Sclerosis Documentation System (MSDS) into an eHealth platform that can be used for multiple purposes. Various use cases are presented that implement this software platform and offer an important perspective for the innovative digital patient management in the future. A holistic patient management of the MS, electronically supported by clinical pathways, will have an important impact on other areas of patient care, such as neurorehabilitation.

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Section: Patient Documentationmentioning

confidence: 99%

Data Collection in Multiple Sclerosis: The MSDS Approach

Ziemssen

Kern²,

Voigt

et al. 2020

Front. Neurol.

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show abstract

“…Traditional scales used to assess MS, such as the Expanded Disability Status Scale (EDSS), are not sensitive enough to detect slight changes in motor performance [2]. Video recordings of patient performance are more accurate and increase the reliability of severity ratings [3,4]. Moreover, when these recordings are automated, quantitative disability assessments by machine learning algorithms (MLA) can be created [5].…”

Section: Introductionmentioning

confidence: 99%

Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study

D’Souza¹,

Munster²,

Dorn³

et al. 2020

J Med Internet Res

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Background In chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase the reliability of severity ratings. When these recordings are automated, quantitative disability assessments by machine learning algorithms can be created. Creation of these algorithms involves non–health care professionals, which is a challenge for maintaining data privacy. However, autoencoders can address this issue. Objective The aim of this proof-of-concept study was to test whether coded frame vectors of autoencoders contain relevant information for analyzing videos of the motor performance of patients with MS. Methods In this study, 20 pre-rated videos of patients performing the finger-to-nose test were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. The original and decoded videos were shown to 10 neurologists at an academic MS center in Basel, Switzerland. The neurologists tested whether the 200 videos were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-Expanded Disability Status Scale definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between the original and decoded videos. Results In total, 172 of 200 (86.0%) videos were of sufficient quality to be ratable. The intrarater agreement between the original and decoded videos was 0.317 (Cohen weighted kappa). The average difference in the ratings between the original and decoded videos was 0.26, in which the original videos were rated as more severe. The interrater agreement between the original videos was 0.459 and that between the decoded videos was 0.302. The agreement was higher when no deficits or very severe deficits were present. Conclusions The vast majority of videos (172/200, 86.0%) decoded by the autoencoder contained clinically relevant information and had fair intrarater agreement with the original videos. Autoencoders are a potential method for enabling the use of patient videos while preserving data privacy, especially when non–health-care professionals are involved.

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Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study (Preprint)

D’Souza¹,

Munster²,

Dorn³

et al. 2019

Preprint

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BACKGROUND In chronic neurological diseases, especially in multiple sclerosis (MS), clinical assessment of motor dysfunction is crucial to monitor the disease in patients. Traditional scales are not sensitive enough to detect slight changes. Video recordings of patient performance are more accurate and increase the reliability of severity ratings. When these recordings are automated, quantitative disability assessments by machine learning algorithms can be created. Creation of these algorithms involves non–health care professionals, which is a challenge for maintaining data privacy. However, autoencoders can address this issue. OBJECTIVE The aim of this proof-of-concept study was to test whether coded frame vectors of autoencoders contain relevant information for analyzing videos of the motor performance of patients with MS. METHODS In this study, 20 pre-rated videos of patients performing the finger-to-nose test were recorded. An autoencoder created encoded frame vectors from the original videos and decoded the videos again. The original and decoded videos were shown to 10 neurologists at an academic MS center in Basel, Switzerland. The neurologists tested whether the 200 videos were human-readable after decoding and rated the severity grade of each original and decoded video according to the Neurostatus-Expanded Disability Status Scale definitions of limb ataxia. Furthermore, the neurologists tested whether ratings were equivalent between the original and decoded videos. RESULTS In total, 172 of 200 (86.0%) videos were of sufficient quality to be ratable. The intrarater agreement between the original and decoded videos was 0.317 (Cohen weighted kappa). The average difference in the ratings between the original and decoded videos was 0.26, in which the original videos were rated as more severe. The interrater agreement between the original videos was 0.459 and that between the decoded videos was 0.302. The agreement was higher when no deficits or very severe deficits were present. CONCLUSIONS The vast majority of videos (172/200, 86.0%) decoded by the autoencoder contained clinically relevant information and had fair intrarater agreement with the original videos. Autoencoders are a potential method for enabling the use of patient videos while preserving data privacy, especially when non–health-care professionals are involved.

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Reference videos reduce variability of motor dysfunction assessments in multiple sclerosis

Cited by 5 publications

References 6 publications

Data Collection in Multiple Sclerosis: The MSDS Approach

Data Collection in Multiple Sclerosis: The MSDS Approach

Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study

Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study (Preprint)

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