Comparing the Performance of Deep Learning Video-based Models and Trained Veterinarians in Cattle Pain Assessment

Accurately assessing pain in cattle remains a significant challenge because these prey animals naturally conceal signs of discomfort, which can lead to subjective and highly variable human evaluations. To address this limitation, this study introduced a novel, video-based deep learning framework designed to automatically detect subtle pain-related expressions and behavioral changes. Using computer vision techniques to analyze temporal and spatial features in video recordings of bulls, the system’s performance was compared against standardized scoring by trained veterinarians. The machine learning model achieved an impressive 97% accuracy in pain classification. This performance successfully outperformed video-based human assessments and matched the accuracy of real-time expert evaluations, offering a highly objective and scalable tool for monitoring livestock welfare.