Graff Emily C, Lea Christopher R, Delmain Diane, Chamorro Erin D, Ma Xiaolei, Zheng Jingyi, Zhang Yue, Brinker Emily, Kittell Kenzii, Hicks Mackenzie, Pfister Casey, Hamilton Heather, Li Qinghong, Martin Douglas R, Wang Xu
Department of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Scott-Ritchey Research Center, College of Veterinary Medicine, Auburn University, Auburn, AL, United States.
Front Vet Sci. 2025 Aug 18;12:1604557. doi: 10.3389/fvets.2025.1604557. eCollection 2025.
Body Condition Score (BCS) is an effective tool for assessing body weight and fat mass, as well as diagnosing obesity and abnormal weight loss. A method for visual assessment of BCS in cats would be useful to expand access for feline health and research. The goal of this study is to determine whether BCS can be accurately assessed solely from photographs of cats, and to measure inter-evaluator bias in visually assessed BCS. To do this, a set of online-sourced cat images was administered as a quiz to nine evaluators. Inter-evaluator bias was relatively low (mean ± SE = 0.35 ± 0.03) with ~50% complete agreement. To validate the results, a BCS was clinically assessed during routine wellness exams for 38 cats, enrolled, through palpation by one evaluator and visual assessment by all nine evaluators using photographs collected at the exam. The visual assessment of BCS deviated from the clinically assessed BCS by 0.61 ± 0.04, which was slightly higher than the deviation observed in the online-sourced image set. In both scenarios, the majority voting among all evaluators achieved the highest accuracy, demonstrating its effectiveness in reducing evaluator bias. Inter-evaluator bias caused a 15.5% misclassification between ideal and overweight BCS but 1.8% between ideal and obese, indicating minimal bias in diagnosing feline obesity. The ability to accurately assess BCS through photographic evaluation will enhance remote consultations in telemedicine and support large-scale epidemiological studies. This study has developed a method for evaluating and minimizing inter-evaluator bias in BCS assessments across diverse practitioners and settings, thereby improving consistency and comparability and improving our understanding and application of BCS as a tool for feline health.
身体状况评分（BCS）是评估体重和体脂、诊断肥胖及体重异常减轻的有效工具。一种用于猫的BCS视觉评估方法，将有助于扩大猫科动物健康和研究的途径。本研究的目的是确定是否仅通过猫的照片就能准确评估BCS，并测量视觉评估BCS时评估者之间的偏差。为此，将一组网上获取的猫的图像作为测试题提供给九名评估者。评估者之间的偏差相对较低（均值±标准误 = 0.35 ± 0.03），完全一致的比例约为50%。为验证结果，在38只猫的常规健康检查期间进行了临床BCS评估，这38只猫通过一名评估者的触诊以及所有九名评估者使用检查时收集的照片进行视觉评估而被纳入研究。BCS的视觉评估与临床评估的BCS相差0.61 ± 0.04，略高于网上获取的图像集所观察到的数据偏差。在这两种情况下，所有评估者的多数投票都达到了最高准确率，证明了其在减少评估者偏差方面的有效性。评估者之间的偏差导致理想BCS与超重BCS之间有15.5%的错误分类，但理想BCS与肥胖BCS之间为1.8%，表明在诊断猫肥胖方面偏差极小。通过照片评估准确评估BCS的能力将增强远程医疗中的远程会诊，并支持大规模流行病学研究。本研究开发了一种方法，用于评估和最小化不同从业者和环境下BCS评估中评估者之间的偏差，从而提高一致性和可比性，并增进我们对BCS作为猫科动物健康工具的理解和应用。
