Prof. Pott, why does your research explore robotic flexible endoscopy?
Prof. Peter Pott: We want to support medicine in using robot technology to speed up, simplify, and make flexible endoscopic interventions safer. Our focus is on developing actuation and sensor technologies that are small and powerful, yet cost-effective to also permit the robotic-assisted application of single-use endoscopes. Single use in this setting is key because it is otherwise very expensive and elaborate to make these systems reusable or to at least provide adequate sterilization to meet hygiene standards for human application. Despite the lament over the use of resources and raw materials, which must be disposed after the intervention, it is smart to rely on single-use medical devices in this instance. That is why we focus on the small, lightweight and affordability aspects.
What was your basic scientific objective at the start of your work three years ago?
Pott: We pondered where we could apply our actuation technology to benefit a maximum number of people. Flexible robotics is a suitable application. Endoscopy is a procedure that has been used for over 50 years to diagnose and treat problems in the gastrointestinal tract. Since then, the process has essentially been the same – physicians use a manually controlled endoscope. Over the years, the latter has been modernized thanks to new camera technology, new light sources, and instruments. However, it remains a procedure that is complex for the physicians and uncomfortable for the patients. Robotics aims to make this intervention easier and safer for medical professionals and safer and gentler for the patient. By using slim and flexible structures, we foster smaller incisions and create systems that reduce trauma. In a nutshell: robotics in medicine is designed to make things better (and – let’s face it – also less expensive).
That is why we are studying the actuation technology of the relevant systems and are trying to facilitate certain movements, produce specific forces, and achieve explicit movement patterns at a specified speed. This is our benchmark.