Dr. Marcelo Feighelstein. (credit: TEL-HAI UNIVERSITY OF KIRYAT SHMONA IN THE GALILEE)<\/p>\n
His new AI framework, known as SHIC-XE, has been developed to detect signs of pain in horses via video analysis while providing stable, anatomically consistent explanations for its decisions.<\/p>\n
Feighelstein led the collaboration that included Prof. Anna Zamansky and the Tech4Animals Lab, Prof. Ilan Shimshoni of the University of Haifa<\/a>, students Omer Bibi and Ofer Rosenbaum from Haifa\u2019s Technion-Israel Institute of Technology, and researchers from the University of Bern, the University of Milan, the University of S\u00e3o Paulo, and Newcastle University.<\/p>\n It was published in the prestigious International Journal of Computer Vision under the title \u201cSHIC-XE: Viewpoint-Invariant Explainability via Dense 2D-3D Correspondences: an Application to Equine Pain Recognition.\u201d\u00a0<\/p>\n The research found a solution to an enduring challenge in both veterinary and human medicine: how to identify pain and distress in individuals who cannot communicate what they are experiencing.<\/p>\n People who could benefit include newborns, people in intensive care who are sedated and ventilated, and victims of dementia, stroke, progressive aphasia, head injuries, tumors, or serious infections who are unable to speak. It could also analyze neurological movement disorders and assist with clinical and surgical video analysis.<\/p>\n \u201cFor a long time, doctors thought that newborns didn\u2019t feel pain, so they didn\u2019t take measures to prevent their suffering,\u201d he noted.<\/p>\n For the first time, according to the team, these AI-generated explanations can be quantitatively compared with expert assessments, representing a significant breakthrough in the field of explainable artificial intelligence and an important step toward AI systems that can be trusted in real-world clinical and healthcare environments.<\/p>\n Traditional methods use 2D visualization techniques such as saliency maps \u2013 visual heat maps that highlight the most important parts of an image for a human viewer or a machine learning model. But when applied to video data, subject and camera motion produce unstable, flickering maps that cannot be temporally aggregated in meaningful ways.<\/p>\n \u201cThis limitation is critical in medical and veterinary settings, which demand biologically grounded explanations intuitive for experts. Horses are particularly challenging in the context of pain as they are known to hide pain signals in human presence,\u201d the chief researcher said.<\/p>\n AI supporting the doctors<\/p>\n