This work concerns methods for automated rating of the progression of Multiple Sclerosis (MS). Often, MS patients develop cognitive deficits. 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 using geometric figures which the patient should recognize and trace. The results are rated manually by a physician. The goal of this work was to digitize the BVMT-R and to support the interpretation of the test results using a machine learning (ML) algorithm. A convolutional neural network (CNN) was used to rate the drawings of a patient. As a result, the correct point value of the BVMT-R could be determined with an accuracy between 57% and 76% based on a training set of 624 patient drawings obtained from 135 patients. These drawings had been previously physician rated to serve as a gold standard. In our experiment, we obtained reasonable accuracy above 80% when more than 40 drawings were available, but our training sample was too small for more detailed analysis.

Conclusion: At the currently achieved classification accuracy, results analysis will remain a physician task, potentially supported with ML based preclassification, but there is hope that ML accuracy can be further improved to enable automated follow-ups.