Digitalisation of the Brief Visuospatial Memory Test-Revised and Evaluation with a Machine Learning Algorithm.

Birchmeier, Martin Eduard; Studer, Tobias; Lutterotti, Andreas; Penner, Iris-Katharina; Bignens, Serge (2020). Digitalisation of the Brief Visuospatial Memory Test-Revised and Evaluation with a Machine Learning Algorithm. Studies in health technology and informatics, 270, pp. 168-172. IOS Press 10.3233/SHTI200144

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The disease multiple sclerosis (MS) is characterized by various neurological symptoms. This paper deals with a novel tool to assess cognitive dysfunction. The Brief Visuospatial Memory Test-Revised (BVMT-R) is a recognized method to measure optical recognition deficits and their progression. Typically, the test is carried out on paper. We present a way to make this process more efficient, without losing quality by having the patients using a tablet App and having the drawings rated with the use of a machine learning (ML) algorithm. A dataset of 1'525 drawings were digitalized and then randomly split in a training dataset and in a test dataset. In addition to the training dataset the already trained drawings from a preliminary paper were added to the training dataset. The ratings done by two neuropsychologists matched for 81% of the test dataset. The ratings done automatically with the ML algorithm matched 72% with the ones of the first neuropsychologist and 79% of the ones of the second neuropsychologist. For a semi-automated rating we defined a threshold value for the reliability of the rating of 78.8%, under which the drawing is routed for manual rating. With this threshold value the ML algorithm matched 80.3% and 86.6% of the ratings of the first and second neuropsychologists. The neuropsychologists have in that case to manually check 17.4% of the drawings. With our results is it possible to execute the BVMT-R Test in a digital way. We found out, that our ML algorithms have with the semi-automated method the similar matching as the two professional raters.

Item Type:

Journal Article (Original Article)

Division/Institute:

Engineering and Information Technology > Institut für Medizininformatik I4MI

Name:

Birchmeier, Martin Eduard;
Studer, Tobias;
Lutterotti, Andreas;
Penner, Iris-Katharina and
Bignens, Serge0000-0003-3846-9464

Subjects:

R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry

ISSN:

1879-8365

Publisher:

IOS Press

Language:

English

Submitter:

Serge Bignens

Date Deposited:

30 Sep 2020 06:57

Last Modified:

30 Sep 2020 06:57

Publisher DOI:

10.3233/SHTI200144

PubMed ID:

32570368

Uncontrolled Keywords:

BICAMS BVMT-R Convolutional neural network Machine Learning Multiple Sclerosis digitalize

ARBOR DOI:

10.24451/arbor.12989

URI:

https://arbor.bfh.ch/id/eprint/12989

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