A non-invasive method to determine core temperature for cats and dogs using surface temperatures based on machine learning

Abstract

Abstract Background Rectal temperature (RT) is an important index of core temperature, which has guiding significance for the diagnosis and treatment of pet diseases. Objectives Development and evaluation of an alternative method based on machine learning to determine the core temperatures of cats and dogs using surface temperatures. Animals 200 cats and 200 dogs treated between March 2022 and May 2022. Methods A group of cats and dogs were included in this study. The core temperatures and surface body temperatures were measured. Multiple machine learning methods were trained using a cross-validation approach and evaluated in one retrospective testing set and one prospective testing set. Results The machine learning models could achieve promising performance in predicting the core temperatures of cats and dogs using surface temperatures. The root mean square errors (RMSE) were 0.25 and 0.15 for cats and dogs in the retrospective testing set, and 0.15 and 0.14 in the prospective testing set. Conclusion The machine learning model could accurately predict core temperatures for companion animals of cats and dogs using easily obtained body surface temperatures.