Descrizione del progetto
Un nuovo modo di far funzionare robot chirurgici
I robot chirurgici saranno mai in grado di funzionare autonomamente? Le macchine computerizzate utilizzate in chirurgia sono manovrate direttamente da chirurghi esperti. Attualmente non è coinvolta alcuna autonomia robotica. Mentre gli approcci della moderna robotica hanno come obiettivo l’assoluta precisione geometrica, l’accuratezza funzionale (relativa all’intervenire sulle strutture anatomiche e funzionali) è l’aspetto fondamentale nella chirurgia. Il progetto FAROS, finanziato dall’UE, sta sviluppando nuovi modi per incorporare l’intelligenza fisica nella robotica chirurgica. Nello specifico, costruirà una rappresentazione funzionale del compito chirurgico unita al rilevamento non visivo chiave. Inoltre, l’apprendimento automatico profondo interpreterà i dati intraoperativi. Il progetto dimostrerà questo nuovo modo di far funzionare robot chirurgici in passaggi critici eseguiti autonomamente nella chirurgia vertebrale.
Obiettivo
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.
Campo scientifico
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- medical and health sciencesclinical medicinesurgeryrobotic surgery
Programma(i)
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
3000 Leuven
Belgio