RT Journal Article
T1 Using XAI in the Clock Drawing Test to reveal the cognitive impairment pattern.
A1 Jiménez-Mesa, Carmen
A1 Arco, Juan E.
A1 Valentí-Soler, Meritxell
A1 Frades-Payo, Belén
A1 Zea-Sevilla, María A.
A1 Ortiz-García, Andrés
A1 Ávila-Villanueva, Marina
A1 Castillo-Barnes, Diego
A1 Ramírez, Javier
A1 Del-Ser-Quijano, Teodoro
A1 Carnero Pardo, Cristóbal
A1 Górriz-Sáez, Juan Manuel
K1 Alzheimer, Enfermedad de - Diagnóstico - Proceso de datos
K1 Diagnóstico - Proceso de datos
K1 Medicina - Proceso de datos
K1 Aprendizaje automático (Inteligencia artificial)
AB 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
PB World Scientific
YR 2023
FD 2023-02-16
LK https://hdl.handle.net/10630/28120
UL https://hdl.handle.net/10630/28120
LA eng
NO 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.
NO This work was supported by the MCIN/ AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018- 098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de An765 dalucia) and FEDER under CV20-45250, A-TIC080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. JimenezMesa and the Margarita-Salas grant to J.E. Arco.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026