Highly accurate and precise determination of mouse mass using computer vision

被引:0
|
作者
Guzman, Malachy [1 ,2 ]
Geuther, Brian Q. [1 ]
Sabnis, Gautam S. [1 ]
Kumar, Vivek [1 ,3 ,4 ]
机构
[1] Jackson Lab, Bar Harbor, ME 04609 USA
[2] Carleton Coll, Northfield, MN USA
[3] Tufts Univ, Sch Grad Biomed Sci, Sch Med, Boston, MA 04469 USA
[4] Univ Maine, Grad Sch Biomed Sci & Engn, Orono, ME 04469 USA
来源
PATTERNS | 2024年 / 5卷 / 09期
基金
美国国家卫生研究院;
关键词
BODY-WEIGHT; MICE; ASSOCIATION; ANXIETY; ERROR;
D O I
10.1016/j.patter.2024.101039
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页数:14
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