Digital monitoring of laboratory mice to reduce suffering

Rikke Bonnichsen, Danish Technological Institute

In Denmark, there are numerous legal requirements when using mice for medical and toxicological experiments. When carrying out experiments, there is an obligation to monitor the animals and minimize suffering.

Monitoring is typically performed by frequent inspections and recording of symptoms until a predetermined Humane Endpoint (HE) is reached and the animals are removed from the experiment. These inspections only provide snapshots of the experimental period but does not tell anything about the duration of the symptoms or when progressive symptoms begin. The method also presents challenges in terms of ensuring that the mice are removed immediately when the HE is reached. This especially because it can be difficult to carry out sufficient supervision at night, when the mice are most active. In addition, disturbing the mice during an inspection can result in mice either hiding or "hiding" their symptoms because of their natural escape instinct. The project aims to optimize the monitoring of laboratory mice using a camera and artificial intelligence (AI). The solution will continuously analyse the development of the animals' behaviour and clinical symptoms.

Continuous monitoring will provide several advantages. It will make it possible to find the HE at the earliest possible moment and thereby to remove the mice immediately. It will provide more accurate and objective test results, because a larger amount of more uniform data is created without disturbing the mice. This method will eventually make it possible to design an experiment in which fewer mice are needed to achieve a result. The method will thus reduce discomfort and stress by refining the current inspections procedures, while at the same time creating opportunities to reduce the number of mice used for experiments. To avoid unnecessary use of laboratory mice, the system will be developed based on ongoing experiments and in collaboration with the Pharmaceutical Industry. An experimental model of lower severity is used as case in the project, but the project will simultaneously demonstrate the potential of using continuous monitoring and AI to improve animal welfare in models with higher severity classification.


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