A Comparative Study of Deep Learning Approaches for Cognitive Impairment Diagnosis Based on the Clock-Drawing Test

The global prevalence of dementia is on the rise, posing a challenge to healthcare systems worldwide. The disease leads to irreversible deterioration of cognitive function, which underlines the importance of early detection to mitigate its impact. The Clock Drawing Test (CDT) is a widely used tool in cognitive assessment, as it involves manually drawing a clock on a piece of paper. Despite its widespread use, CDT scoring methods often rely on subjective expert assessments. Thus, machine learning and deep learning-based models are recently being proposed for the automated evaluation of CDT drawings. In this study, we compare two state-of-the-art models, a simple CNN and API-Net, as cognitive state classification systems. Two databases were used, one from Spanish clinical centers (7009 samples) and the other from a hospital in Thailand (3108 samples). The obtained results align with expected accuracy rates in such scenarios (around 80%) and are similar in both models. Specifically, the accuracy rates obtained with the Spanish database are 75.65% and 72.42%, and with the Thai database, 86.42% and 86.90%. This reflects that the implementation of an excessively complex model is not necessary given the available sample size and the binary classification scenario. Therefore, although both models could be useful in the clinical domain, opting for models with lower computational costs is advisable to make them more cost-effective and easily accessible.