Researchers are adopting Formula 1 principles in surgical procedures by offering surgeons real-time feedback during robot-assisted operations.
By recording and analysing every movement of the surgical robot, the digitisation concept helps robotic surgery become more reproducible and precise.
In Formula 1, everything revolves around data. Not only are positions in the race, track times and corner speeds recorded, but also hundreds of sensors in the car continuously measure steering, tyre wear and brake temperature.
This combined data is sent directly to the engineers to process, allowing them to intervene during the race – or adjust strategy.
After the race, aspects that shape the performance are analysed to boost the car’s performance and improve tactics for the next time.
Behind every lap is a coordinated effort involving race engineers, data analysts, strategists, mechanics and technicians, each with a specific role in optimising performance.
Now, inspired by the telemetry systems used in Formula 1, researchers at the LUMC are incorporating data-driven decision support into an already well-coordinated surgical team to introduce a new layer of insight.
PhD student Kateryna Pirkovets explained: ‘We collected data from four surgeries in which the prostate was removed using the Da Vinci robot – robotic-assisted laparoscopic prostatectomy (RALP). Each procedure lasted around three hours, during which data for 44 different parameters were measured. We looked at how fast and smoothly the instruments moved, their location within the body and relative to each other, and how long each step took. We used artificial intelligence to process and help interpret the data.’
She said that surgical procedures are much like a Formula 1 race, unfolding in distinct phases, with each one placing different demands on surgeon technique: sometimes speed is key, other times precision is crucial.
This means surgeons also need to adjust their approach depending on the phase.
The researchers found that these different surgical tasks for different phases are reflected in the instrument movements.
Pirkovets said: ‘By recognising movement patterns, we can now, for the first time, objectively determine which phase of the operation the surgeon is in, much like a F1 team follows the car’s position on the track. This matters because every step counts in cancer surgery. Until now, the quality of three-hour long RALP surgeries was assessed by colleagues reviewing the video afterwards – a process that is time consuming and is prone to human errors.’
Fijs van Leeuwen, professor in molecular imaging and image-guided therapy, added: ‘Automating this process not only makes it faster and more reproducible. It also provides a higher level of feedback that helps surgeons learn and evolve, just like F1 teams better their skills after each race.’
Surgical trainees also benefit from this technique, with feedback that is data-driven and objective.
The initial results are promising. The researchers are now working towards a system that, in the future, will not only analyse surgical data afterwards but also provide actionable feedback during the procedure.
Just as a Formula 1 driver receives updates on track position, corner speed or tyre pressure during the race, the aim is for surgical teams to eventually get real-time information to sharpen decision-making while operating and produce ‘wins’.
Van Leeuwen said: ‘To analyse this huge amount of data at the required speed, we need serious computing power. We are partnering not only with medtech companies like Intuitive and Barco, but also with data-processing experts like Capgemini and Oracle, the latter also known for their work in Formula 1. The goal? Not just learning to interpret data afterwards, but to ensure that in the future every surgical team, like a Formula 1 team, can rely on smart technology to support them in delivering the best possible patient care.’
