Using Explainable Artificial Intelligence in the Clock Drawing Test to Reveal the Cognitive Impairment Pattern
Metadatos
Mostrar el registro completo del ítemAutor
Jiménez Mesa, Carmen; Arco Martín, Juan Eloy; Valentí-Soler, Meritxell; Frades-Payo, Belén; Zea Sevilla, Maria A.; Ortiz, Andrés; Ávila-Villanueva, Marina; Castillo Barnes, Diego; Ramírez Pérez De Inestrosa, Javier; Del Ser-Quijano, Teodoro; Carnero-Pardo, Cristóbal; Gorriz Sáez, Juan ManuelEditorial
World Scientific
Materia
Clock Drawing Test Cognitive impairment Clinical Diagnosis computer-aided diagnosis Deep Learning Explainable AI image processing Machine Learning Alzheimer's Disease
Fecha
2022-12-30Referencia bibliográfica
Jimenez-Mesa, C., Arco, J. E., Valentí-Soler, M., Frades-Payo, B., Zea-Sevilla, M. A., Ortiz, A., ... & Górriz, J. M. (2023). Using Explainable Artificial Intelligence in the Clock Drawing Test to Reveal the Cognitive Impairment Pattern. International Journal of Neural Systems, 33(04), 2350015. (https://doi.org/10.1142/S0129065723500156)
Patrocinador
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 Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa and the Margarita-Salas grant to J.E. Arco.Resumen
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 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 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 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.