Feline Grimace Scale: Revolutionizing Pain Assessment Through AI Technology

AI Feature: The Feline Grimace Scale

AI has become a vital part of various industries, from healthcare to finance. In veterinary medicine, recognizing pain in animals is essential for providing the right care. The development of the Feline Grimace Scale (FGS) is a significant advancement in the assessment of acute pain in cats, and it leverages artificial intelligence to improve and simplify the process.

Using AI to Assess Animals’ Pain

With the incorporation of deep neural networks and machine learning models, the FGS has been designed to predict facial landmark placements and pain scores. By utilizing Convolutional Neural Networks (CNN) and XGBoost models, researchers have been successful in training AI models to distinguish between painful and non-painful states in cats. The FGS demonstrated high accuracy in assessing feline pain, indicating the potential for more effective pain management.

Results and Application of the FGS

Through extensive testing, the FGS has shown promising results. The top-performing CNN model achieved a normalized root mean squared error (NRMSE) of 16.76%. The leading XGBoost model successfully predicted FGS scores with an impressive accuracy of 95.5% and minimal mean square error (MSE) of 0.0096. These measurements have made it possible to accurately differentiate between painful and non-painful states in cats.


The Feline Grimace Scale (FGS) is a revolutionary development in the field of veterinary medicine, offering an effective and practical method for assessing acute pain in cats. By leveraging the power of AI, the FGS aims to streamline the process of identifying and addressing feline pain, leading to more timely and effective treatments.

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