Guzman Malachy, Geuther Brian Q, Sabnis Gautam S, Kumar Vivek
The Jackson Laboratory, Bar Harbor, ME, USA.
Carleton College, Northfield, MN, USA.
Patterns (N Y). 2024 Aug 7;5(9):101039. doi: 10.1016/j.patter.2024.101039. eCollection 2024 Sep 13.
Changes in body mass are key indicators of health in humans and animals and are routinely monitored in animal husbandry and preclinical studies. In rodent studies, the current method of manually weighing the animal on a balance causes at least two issues. First, directly handling the animal induces stress, possibly confounding studies. Second, these data are static, limiting continuous assessment and obscuring rapid changes. A non-invasive, continuous method of monitoring animal mass would have utility in multiple biomedical research areas. We combine computer vision with statistical modeling to demonstrate the feasibility of determining mouse body mass by using video data. Our methods determine mass with a 4.8% error across genetically diverse mouse strains with varied coat colors and masses. This error is low enough to replace manual weighing in most mouse studies. We conclude that visually determining rodent mass enables non-invasive, continuous monitoring, improving preclinical studies and animal welfare.
体重变化是人类和动物健康的关键指标,在畜牧业和临床前研究中经常进行监测。在啮齿动物研究中,目前在天平上手动称量动物体重的方法至少会引发两个问题。首先,直接处理动物会产生应激反应,可能会混淆研究结果。其次,这些数据是静态的,限制了连续评估并掩盖了快速变化。一种非侵入性的连续监测动物体重的方法将在多个生物医学研究领域具有实用价值。我们将计算机视觉与统计建模相结合,以证明通过使用视频数据确定小鼠体重的可行性。我们的方法在具有不同毛色和体重的多种基因不同的小鼠品系中确定体重时,误差为4.8%。这个误差足够低,可以在大多数小鼠研究中取代手动称重。我们得出结论,通过视觉确定啮齿动物体重能够实现非侵入性的连续监测,改善临床前研究和动物福利。
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