Digital twin aims to further understanding of bladder dysfunction

Sheffield University and the University of Pittsburgh are developing the digital twin to simulate how the bladder’s structure and ability to fill/void changes in response to an obstruction.

The project - A Digital Twin for Designing Bladder Treatment informed by Bladder Outlet Obstruction Mechanobiology (BOOM) - has been awarded a $3.2m NIH-R01 grant, with around $500k allocated to Sheffield University.

Bladder Outlet Obstruction (BOO) is a contributory factor to lower urinary tract symptoms (LUTS) that affect ageing men and is characterised by a blockage at the base of the bladder, with symptoms including abdominal pain, difficulty urinating and a continuous feeling of a full bladder. Around 50 - 75 per cent of men aged over 50 experience LUTS due to BOO, rising to 80 per cent for over 70s.

In a statement, Dr Paul Watton, head of Sheffield University’s Department of Computer Science’s Complex Systems Modelling research group, said: “This is something that affects more than 200 million people across the world and as the population continues to age, it’s only going to become more prevalent.

“Currently, the success rate of bladder surgeries in treating the condition stands at only 70 per cent, highlighting an urgent need to comprehensively understand the underlying issues and develop more effective treatment strategies.

“If you were suffering from bladder dysfunction and a clinician told you they are recommending surgical intervention, but then explained there’s a 30 per cent chance it won’t work, you might opt to continue with a bladder that doesn’t function properly.”

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Researchers will use studies of BOO in a rat model to drive the development of the new technology. The digital twin aims to enable a far greater understanding of how changes to the bladder cause progressive dysfunction and how it can be better treated through new combinations of pharmacological treatments and surgery.

Using computational tools, the digital twin would be calibrated in real-time with personalised, clinical data so it could be used to predict the likelihood of success of a particular course of treatment. The outcomes of these predictions are then fed back into the computational models to improve the accuracy of future predictions.

“Our limited understanding of the condition and the bladder more generally is in stark contrast to, for example, the heart for which we have sophisticated biomechanical models,” said Dr Watton. “We have an opportunity here to capitalise and build on the tools and computational approaches that have already been developed for other organs to rapidly increase our understanding of bladder biomechanics.”

The work underpinning the digital twin project is described in Science Direct.