Surgical innovation at King's Health Partners

King’s Health Partners has a long history of surgical simulation. Inspired by training in aviation, the aim is to improve safety by training away from patients. However lack of evidence has delayed it’s widespread application, and it’s still not compulsory in urology.

With the increased learning curve due to new technology and restrictions on the number of hours, doctors are able to work, the benefits of simulation training are even more needed. To establish a clear evidence base, researchers at King’s initiated the SIMULATE trial.

SIMULATE stands for SIMULAtion in urological Training and Education. In hospitals across Europe, Asia and North America, 65 trainees were selected, with half-trained traditionally, and half also training with simulation, to perform a ureterorenoscopy (for treating stones. They each followed over 25 procedures for over 18 months. Results have been published and indicate a higher proficiency for those using simulation in their training.

Beyond the SIMULATE trial, the team at the Institute for Robotic Surgery, continue to innovate in new training methods as well as new systems to support the surgeon and improve results and patient safety.

Athletes performing at a high level have long optimized their performance by working with sports psychologists, yet cognitive training has not been a part of training surgeons. The MIND trial looks at mental imagery techniques, specifically Motor Imaging or MI – the imaging of the performance of a motor task without actual execution. Using functional MRI to assess the effects of MI on certain parts of the brain has already shown positive results.

At the same time, advances in technology are providing surgeons other options to improve the work they do, and these new approaches are exemplified by the newly launched Centre for Doctoral Training in Surgical & Interventional Engineering.

Artificial Intelligence has changed the game in terms of pattern recognition, and there have always been hopes that AI could be used to recognise and label areas for surgical intervention – but limited labelled datasets for AI to learn from have prevented this from being practical.

Researchers are working on AutoProstate, a deep-learning powered framework which uses multi-parametric MRI to automatedly segment the prostate. Many men are missed by radiologists using mpMRI as it can be difficult to measure prostate volume – this kind of automation shows promise in being able to improve on this.

Researchers have also established MONAI Label, a framework for AI-assisted interactive labelling of 3D medical images, which allows people to develop annotation applications and make them available to surgeons, to enable better automatic medical image segmentation.

Through better training and technology to support surgeons, and the research needed to provide an evidence base for any new approaches, King’s Health Partners are enabling better surgeons and enhancing patient safety in urology – and beyond.