2026-05-28T19:46:00Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/281202026-02-03T11:17:37Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Jiménez-Mesa, Carmen author Arco, Juan E. author Valentí-Soler, Meritxell author Frades-Payo, Belén author Zea-Sevilla, María A. author Ortiz-García, Andrés author Ávila-Villanueva, Marina author Castillo-Barnes, Diego author Ramírez, Javier author Del-Ser-Quijano, Teodoro author Carnero Pardo, Cristóbal author Górriz-Sáez, Juan Manuel author 2023-02-16 he prevalence of dementia is currently increasing worldwide. This syndrome produces a deteriorationin cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing itsprogress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessmentin which an individual has to manually draw a clock on a paper. There are a lot of scoring systems forthis test and most of them depend on the subjective assessment of the expert. This study proposes acomputer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDTand obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessingpipeline 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 informativepatterns 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 byclinical experts in a balanced sample size of 3282 drawings. The proposed method provides an accuracyof 75.65% in the binary case-control classification task, with an AUC of 0.83. These results are indeedrelevant considering the use of the classic version of the CDT. The large size of the sample suggests thatthe method proposed has a high reliability to be used in clinical contexts and demonstrates the suitabilityof CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods areapplied to identify the most relevant regions during classification. Finding these patterns is extremelyhelpful to understand the brain damage caused by CI. A validation method using resubstitution withupper bound correction in a machine learning approach is also discusse Jiménez Mesa, Carmen & Arco, Juan & Valentí-Soler, Meritxell & Frades-Payo, Belen & Zea-Sevilla, M. & Ortiz, Andrés & Avila, Marina & Castillo-Barnes, Diego & Ramírez, Javier & del Ser, Teodoro & Carnero-Pardo, Cristóbal & Gorriz, Juan. (2023). Using Explainable Artificial Intelligence in the Clock Drawing Test to Reveal the Cognitive Impairment Pattern. International Journal of Neural Systems. 33. 10.1142/S0129065723500156. https://hdl.handle.net/10630/28120 10.1142/S0129065723500156 Alzheimer, Enfermedad de - Diagnóstico - Proceso de datos Diagnóstico - Proceso de datos Medicina - Proceso de datos Aprendizaje automático (Inteligencia artificial) Using XAI in the Clock Drawing Test to reveal the cognitive impairment pattern.