Project description
A new way to operate surgical robots
Will surgical robots ever be able to operate autonomously? Computer-controlled machines used in surgery are directly operated by skilled surgeons. Today there is no robotic autonomy involved. While modern robotic approaches target absolute geometric precision, functional accuracy (relative to targeting anatomic and functional structures) is what matters in surgery. The EU-funded FAROS project is developing new ways to embed physical intelligence in surgical robotics. Specifically, it will construct a functional representation of the surgical task fusing key non-visual sensing. Also, deep machine learning will interpret intraoperative data. The project will demonstrate this new way to operate surgical robots in autonomously executed critical steps in spine surgery.
Objective
FAROS aims at improving functional accuracy through embedding physical intelligence in surgical robotics. A key motivation for introducing robots in operating rooms has been their ability to deliver superhuman performance. However, for the vast majority of surgical procedures, robotic positioning precision alone is not sufficient to realize the “right gesture”. Indeed, surgical accuracy is a different concept from standard engineering notions such as geometric precision, resolution or sensitivity. This arises from the essence of the surgical tasks: surgeons do not let their gestures be dictated by pure geometric objectives; rather, functional objectives are what they pursue. FAROS explores venues to efficiently embody surgeon-like autonomous behaviour at different levels of granularity. The following key ingredients are foreseen: (1) a rich set of non-visual sensors that form a multifaceted representation of the surgical task; (2) functional models that relate non-conventional sensor signals to functional parameters (e.g. tissue type, quality of tissue or bone, condition of tissue/fluid, tissue damage, perfusion, implant stability, etc.); and (3) functional controllers, obtained through reinforcement learning, that encode physical intelligence and produce sensible autonomous robot actions geared at closing knowledge gaps or optimizing functional performance. This new concept, which we refer to as Functionally Accurate RObotic Surgery (FAROS), will be showcased on two critical spine surgery use cases, namely: pedicle screw placement and endoscopic lumbar discectomy. A compact yet multi-disciplinary team consisting of academics, industry and end-users will collaborate closely to build up robotic controllers that are better suited at delivering functional accuracy in the presence of large variability and disturbances inherent to every surgical act.
Fields of science
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- medical and health sciencesclinical medicinesurgeryrobotic surgery
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
3000 Leuven
Belgium