Configuration testing of an artificial pancreas system using a digital twin: an evaluative case study

The recent growth in popularity of wearable medical devices has improved the quality of life of people with medical conditions. Testing such devices may require users to configure these systems using physical trials, putting themselves in potentially dangerous scenarios. Misconfiguration of such devices has caused disease misdiagnoses and incorrect drug prescriptions. Digital twins have been proposed as an opportunity to reduce such risks of testing system configurations in simulated environments, decoupling the user from the system under test. In this paper, we perform an evaluative case study to assess the use of a digital twin for configuration testing of an artificial pancreas system (APS) control algorithm. These systems regulate the blood glucose levels in people with type 1 diabetes mellitus, and so misconfigurations can cause severe hypoglycaemia or hyperglycaemia, which can be life-threatening. We tested the OpenAPS control algorithm against 156 people's clinical data. We found that our digital twin provided an accurate simulation environment to perform configuration testing and accurately predict blood glucose–insulin behaviour. We evaluated different APS configurations, identifying a potentially unsafe configuration without the risks associated with a physical trial. We identified the challenges associated with modelling clinical data, which could lead to misinterpretations in configuration testing and the reduction of test reliability when modelling stochastic body dynamics.