Using XAI in the Clock Drawing Test to reveal the cognitive impairment pattern

Abstract

The prevalence of dementia is currently increasing worldwide. This syndrome produces a deterioration in cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing its progress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessment in which an individual has to manually draw a clock on a paper. There are a lot of scoring systems for 20 this test and most of them depend on the subjective assessment of the expert. This study proposes a computer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDT and obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessing pipeline in which the clock is detected, centered and binarized to decrease the computational burden. Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informative 25 patterns within the CDT drawings that are relevant for the assessment of the patient’s cognitive status. Performance is evaluated in a real context where patients with CI and controls have been classified by clinical experts in a balanced sample size of 3282 drawings. The proposed method provides an accuracy of 75.65% in this classification task, with an AUC of 0.83. These results overcome previous studies, showing that the method proposed has a high reliability to be used in clinical contexts. The large size 30 of the sample and the performance obtained despite being applied to the classic version of the CDT demonstrate the suitability of CAD systems in the CDT assessment process. Explainable AI (XAI) methods are applied to identify the most relevant regions during classification. Finding these patterns is extremely helpful to understand the brain damage caused by cognitive impairment. A validation method using resubstitution with upper bound correction in a machine learning approach is also discussed.