Automatic Scoring of Rey-Osterrieth Complex Figure Test Using Recursive Cortical Networks

Abstract

Mild Cognitive Impairment (MCI) is a condition which may lead to a more serious neurodegenerative disease called dementia, affecting between 12% and 18% of total Global population aged 60 or older. Neuropsychological tests conducted by professionals allow for early detection of MCI and early treatment of this condition to prevent further development. Several authors have attempted to automate the assessment process of these types of tests, which enables a faster screening of the population and therefore a better prevention of the symptoms of neurodegenerative diseases. However, most of the works published by previous authors rely on classical Machine Learning techniques, which require handcrafted features and their effectiveness depends on the quality of these features. Also, more advanced Deep Learning models used in the automation of these tests require high amounts of training data in order to be accurate, and they are also weak to noise and variability in the data. In this work, we propose a novel approach to automating one of these test called Rey-Osterrieth Complex Figure (ROCF) test, using Recursive Cortical Networks (RCN). The RCN framework provides an improvement over the disadvantages of previously mentioned techniques, presenting resilience to noise and variability, using an automatic hierarchical feature construction instead of hand-crafted features, while using a very small amount of training data. This work describes the properties of RCN and how they can be of use in the development of an automatic scoring algorithm for the ROCF test.