DOI:
https://doi.org/10.14483/22484728.17425Publicado:
2021-01-30Número:
Vol. 15 Núm. 1 (2021)Sección:
Visión InvestigadoraClassification of Facial Expression of Post-Surgical Pain in Children
Evaluación de redes neuronales convolucionales
Clasificación de la expresión facial de dolor postquirúrgico infantil
Palabras clave:
Inteligencia artificial, Herramientas de evaluación, Expresión facial, Dolor, Pediatría (es).Palabras clave:
Artificial intelligence, Assessment tools, Facial expression, Pain, Pediatrics (en).Descargas
Resumen (en)
There are certain difficulties in differentiating between children's facial expression related to pain and other stimuli. In addition, the limited communication ability of children in the preverbal stage leads to misdiagnosis when the child feels pain, for example, post-surgical conditions. In this article, a classification approach of facial expression of child pain is presented based on models of pre-trained convolutional neuronal networks from the study carried out in a Colombian hospital of level 4 (Hospital Universitario San Vicente Fundación), in the recovery areas of child surgery services. AlexNet and VGG (16, 19 and Face) networks are evaluated in the own dataset using the FLACC scale and their performances are compared in three experiments. The results show that the VGG-19 model achieves the best performance (92.9%) compared to the other networks. The effectiveness of the model and transfer learning for the classification of facial expression of child pain shows a promising solution for the assessment of post-surgical pain.
Resumen (es)
Existen ciertas dificultades para diferenciar entre la expresión facial infantil relacionada al dolor con la de otros estímulos. Además, la limitada capacidad de comunicación de los niños en la etapa preverbal conlleva a un error de diagnóstico cuando el niño siente dolor, por ejemplo, afecciones posteriores a las cirugías. En este artículo, se presenta un enfoque de clasificación de la expresión facial de dolor infantil basado en modelos de redes neuronales convolucionales pre-entrenadas a partir del estudio realizado en un hospital colombiano de nivel 4 (Hospital Universitario San Vicente Fundación), en las áreas de recuperación de los servicios de cirugía infantil. Se evalúan las redes AlexNet y VGG (16, 19 y Face) en el conjunto de datos propio utilizando la escala FLACC y se comparan sus rendimientos en tres experimentos. Los resultados muestran que el modelo VGG-19 logra el mejor rendimiento (92.9%) en comparación con las demás redes. La eficacia del modelo y el aprendizaje por transferencia para la clasificación de la expresión facial de dolor infantil muestran una solución prometedora para la evaluación del dolor postquirúrgico.
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