Overview

• The peer-reviewed proof-of-concept published in BMC Veterinary Research demonstrates reliable detection and spatial mapping of thermal signatures in calf eyes and muzzles using AI segmentation
• Researchers recorded synchronized infrared video and temperature data from 11 calves to extract roughly 200 distinct temperature-change patterns
• Analysis found consistent similarities among the highest 10% and 30% of temperature values in both eye and muzzle regions
• Automated ROI detection overcomes variability from manual thermography settings and reduces animal stress compared with single-time-point rectal measurements
• While the approach shows promise for continuous health and stress monitoring, its validation remains limited by the small sample size and lack of broader field testing