{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T13:36:27Z","timestamp":1793194587968,"version":"4.5.0"},"reference-count":116,"publisher":"Korean Society of Animal Science and Technology","issue":"4","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"funder":[{"name":"Natural Sciences and Engineering Research Council of\n                                Canada","award":["RGPIN 2024-04450"],"award-info":[{"award-number":["RGPIN 2024-04450"]}]},{"name":"New Brunswick Agriculture, Aquaculture and\n                                Fisheries","award":["NB2425-0025"],"award-info":[{"award-number":["NB2425-0025"]}]}],"content-domain":{"domain":["www.ejast.org"],"crossmark-restriction":true},"short-container-title":["J Anim Sci Technol"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:p>Pain management in dairy cattle remains a persistent challenge, hindered by\n                    subjective assessments and inherent observer biases that compromise animal\n                    welfare and impose significant economic burdens due to conditions such as\n                    mastitis and lameness. Emerging AI technologies, integrated with computer vision\n                    and mobile platforms, now offer transformative solutions through objective,\n                    automated facial expression analysis. Advancements in neurobiological research\n                    have elucidated the mechanisms underlying bovine pain expression, enabling the\n                    development of robust grimace scales validated by high sensitivity and\n                    specificity (e.g., UCAPS, sensitivity\/specificity: 0.78\u20130.85). Recent AI\n                    models employing advanced architectures such as YOLOv8-Pose (achieving 96.9% mAP\n                    in landmark detection) and transformer-based frameworks (demonstrating 98.36%\n                    accuracy in facial recognition tasks) significantly surpass conventional\n                    methodologies in accuracy, reliability, and scalability. Moreover, multimodal\n                    approaches fusing RGB and thermal imaging have demonstrated remarkable efficacy\n                    (81%\u201395% accuracy) in capturing nuanced physiological indicators of pain.\n                    Edge-optimized deployment strategies now enable real-time, field-level\n                    applications, delivering rapid classifications at up to 24 frames per second\n                    with classification accuracies of 94.2%. Yet, substantial challenges persist,\n                    particularly in accounting for breed-specific variability and environmental\n                    interferences that limit universal applicability. Critical future research\n                    avenues include transfer learning for improved crossbreed adaptability,\n                    multimodal integration for chronic pain detection, and the advancement of\n                    longitudinal monitoring frameworks within precision livestock farming. The\n                    practical implications of these technologies are profound, promising significant\n                    welfare improvements through timely interventions, reduced economic losses, and\n                    the broader ethical advancement of AI-driven veterinary partnerships. The\n                    integration of automated facial expression-based pain detection in dairy\n                    operations thus holds immense potential to redefine standards in animal welfare\n                    and establish a new paradigm for sustainable and ethically aligned global dairy\n                    production.<\/jats:p>","DOI":"10.5187\/jast.2500286","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:49:07Z","timestamp":1763426947000},"page":"959-1001","update-policy":"https:\/\/doi.org\/10.5187\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["CowPainCheck: AI-based facial expression analysis for dairy cow\n                    welfare"],"prefix":"10.5187","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6921-8497","authenticated-orcid":false,"given":"Shivam","family":"Patel","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01e6qks80","id-type":"ROR","asserted-by":"publisher"}],"name":"Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0990-0235","authenticated-orcid":false,"given":"Suresh","family":"Neethirajan","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01e6qks80","id-type":"ROR","asserted-by":"publisher"}],"name":"Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada"},{"id":[{"id":"https:\/\/ror.org\/01e6qks80","id-type":"ROR","asserted-by":"publisher"}],"name":"Faculty of Agriculture, Dalhousie University, Truro, NS, 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