In the Digital Vivarium, all your cages are daily evaluated to assess their level of moisture within the bedding and suggest a better cage change regime based on real cage conditions and not anymore based on a fixed schedule.
Employing a machine learning approach, the DVC® system is capable of fine-tuning the daily cage bedding evaluations thanks to your initial learning phase and your continuous feedback. In more detail, the learning phase is a specific task, usually performed just after the installation and before the normal running phase. During this task, staff prepare several clean cages with different numbers of animals each and then decide to change the bedding, days after, only those filling your criteria of a dirty cage. In other words, at installation time, the DVC® system is a blank canvass and follows your expertise and decision making.
Your criteria to define a dirty cage can be based on different parameters, not just moisture content. You can decide to change a cage because of the level of ammonia; you can change a cage based on the numbers of fecal pellets, or any other internal Facility criteria. In any case, all these criteria are always correlated with the level of urine (i.e., moisture) in the cage. In conclusion, from a DVC® Learning Phase perspective, what is fundamental is to provide similar cage environments suited to the number of animals
.. The DVC® system will provide details on various parameters such as moisture distribution in each cage, number of animals, time since the cage was cleaned, and others. In the end, the DVC® system will automatically look for the same dirty cage conditions you have suggested during the learning phase to trigger a cage change.
What are the major benefits in following this approach?
Massive cage change reduction, which immediately reduces running costs, improves animal welfare (reduce animal stress), reduces the operator’s workload, and potential ergonomic issues related to this tedious and most repetitive task.
Many existing Digital Facilities are already experiencing these benefits and improving their economic, animal welfare, and human resource management.