Avaliação de dor em camundongos com redes neurais e visão computacional

Abstract

Mice are among the most widely used animal models in biomedical research, making the assessment of animal welfare a fundamental requirement for both the ethical conduct of experiments and the generation of reliable and reproducible scientific results. Therefore, pain assessment remains a significant challenge, as animals cannot directly communicate their sensations, requiring researchers to rely on behavioral and physiological indicators to infer the presence of pain. To reduce the subjectivity inherent to human evaluation, several assessment methods have been proposed over the years, among which the Mouse Grimace Scale has emerged as one of the most widely adopted approaches. This scale evaluates pain through changes in facial expressions associated with painful states in mice. Despite its widespread use, the method still relies heavily on human interpretation, making assessments susceptible to observer bias and inter-rater variability. Motivated by these limitations, this work investigates the application of deep learning and computer vision techniques for automated pain assessment in mice based on the facial action units defined by the Mouse Grimace Scale. To this end, a complete experimental pipeline was developed, encompassing image acquisition, dataset organization, image preprocessing, dataset construction, and supervised training of classification models. Experiments were conducted using well-established convolutional neural network architectures, including variants of the ResNet, MobileNet, and DenseNet families. In addition, the Vision Transformer (ViT) architecture was evaluated to investigate the effectiveness of attention-based mechanisms for modeling global image relationships and to compare its performance with traditional convolutional approaches. Overall, the findings demonstrate the feasibility of applying deep learning techniques to automated pain assessment in laboratory animals, contributing to the reduction of subjectivity in human evaluations and supporting the development of computational tools capable of improving standardization, scalability, and reproducibility in biomedical research. Beyond its contributions to computer vision and artificial intelligence, this work also presents potential benefits for animal welfare and the refinement of experimental protocols.