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

D'Souza, Marcus; Van Munster, Caspar E P; Dorn, Jonas F; Dorier, Alexis; Kamm, Christian P; Steinheimer, Saskia; Dahlke, Frank; Uitdehaag, Bernard M J; Kappos, Ludwig; Johnson, Matthew (2020). Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study. Journal of medical internet research, 22(5), e16669. JMIR Publications 10.2196/16669

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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.


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.


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.


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.


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.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology

UniBE Contributor:

Kamm, Christian Philipp and Steinheimer, Saskia Marie


600 Technology > 610 Medicine & health




JMIR Publications




Chantal Kottler

Date Deposited:

26 Nov 2020 16:01

Last Modified:

26 Nov 2020 16:01

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

Neurostatus-EDSS autoencoder deep neuronal network machine learning algorithms video-rating




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