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Bachiller Mayoral, Margarita

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0000-0001-9122-0858
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Bachiller Mayoral
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Margarita
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20251
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Fecha de finalización: 2025 × Fecha de inicio: 2020 ×

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    Publicación
    Mild Cognitive Impairment Detection from Rey-Osterrieth Complex Figure Copy Drawings Using a Contrastive Loss Siamese Neural Network
    (Tech Science Press, 2025-10-15) Guerrero Martín, Juan; Estella Nonay, Eladio; Bachiller Mayoral, Margarita; Rincón Zamorano, Mariano; Universidad Nacional de Educación a Distancia (UNED)
    Neuropsychological tests, such as the Rey-Osterrieth complex figure (ROCF) test, help detect mild cognitive impairment (MCI) in adults by assessing cognitive abilities such as planning, organization, and memory. Furthermore, they are inexpensive and minimally invasive, making them excellent tools for early screening. In this paper, we propose the use of image analysis models to characterize the relationship between an individual’s ROCF drawing and their cognitive state.This task is usually framed as a classification problem and is solved using deep learning models, due to their success in the last decade. In order to achieve good performance, these models need to be trained with a large number of examples. Given that our data availability is limited, we alternatively treat our task as a similarity learning problem, performing pairwise ROCF drawing comparisons to define groups that represent different cognitive states.This way of working could lead to better data utilization and improved model performance. To solve the similarity learning problem, we propose a siamese neural network (SNN) that exploits the distances of arbitrary ROCF drawings to the ideal representation of the ROCF. Our proposal is compared against various deep learning models designed for classification using a public dataset of 528 ROCF copy drawings, which are associated with either healthy individuals or those with MCI. Quantitative results are derived from a scheme involving multiple rounds of evaluation, employing both a dedicated test set and 14-fold cross-validation. Our SNN proposal demonstrates superiority in validation performance, and test results comparable to those of the classification-based deep learning models.