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Researchers at Oxford’s Nuffield Department of Surgical Sciences (NDS) and the Department of Physiology, Anatomy and Genetics (DPAG) have received several awards and accolades for their work on a novel software algorithm that analyses kidney stones.

Graph shows a comparison between two kidney stones using the software algorithm © Daniel Stevens
Graph shows a comparison between two kidney stones using the software algorithm

The new technology analyses the features of kidney stones on a CT scan and makes an assessment of the likelihood that they will fracture during lithotripsy treatment. A pilot study with 126 patients has been conducted and the team are now working on a larger study of 800 patients.

The project, which is led by Daniel Stevens (Cancer Research UK Clinical Research Training Fellow and DPhil candidate at DPAG) and Helen Cui (Urology Clinical Research Fellow) and supervised by Mr Ben Turney (Clinical Lecturer in Urology at NDS), has been attracting a lot of attention.

In March of this year, the team won ‘Best Presentation’ at the East Meets West Joint Meeting of East and West Midlands Urologists in Leicester, and were awarded 'Best Poster in Session' and highlighted as ‘exciting new technology’ at the European Association of Urology 2016 Annual Congress in Munich. Also, Daniel was awarded the Malcolm Coptcoat Spring Prize by the Royal Society of Medicine for his work on the project.

In addition, last year the team were highlighted as a 'Flagship Poster' at the Challenges in Endourology International Meeting in Paris.

Commenting on the project’s success to date, Daniel said: ‘Lithotripsy only works two-thirds of the time which means patients undergo unnecessary treatments and the NHS does not make best use of its resources. The software we have developed gives additional information on top of the predictive clinical factors we already know about. We now hope that the promising preliminary results we have seen will be confirmed in our larger study. We are delighted that this work has been recognised and ultimately hope that we can benefit both patients and the NHS by using our algorithm in the clinic as soon as possible.’

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