Descripción del proyecto
Una nueva manera de manejar robots quirúrgicos
¿Serán capaces alguna vez de funcionar de forma autónoma los robots? Las máquinas controladas por ordenador que se utilizan para cirugías son controladas directamente por cirujanos cualificados. En la actualidad, este proceso no implica ningún tipo de autonomía robótica. Si bien los métodos de la robótica moderna se dirigen hacia la precisión geométrica absoluta, lo que realmente importa en una cirugía es la precisión funcional (en relación con la región anatómica objetivo y las estructuras funcionales). El proyecto financiado con fondos europeos FAROS está desarrollando maneras nuevas de integrar la inteligencia física en robots quirúrgicos. En concreto, elaborará una representación funcional de la tarea quirúrgica integrando elementos clave de señales no visuales. Por otra parte, se utilizará aprendizaje automático profundo para interpretar los datos intraoperatorios. El proyecto demostrará esta nueva manera de manejar robots quirúrgicos mediante la ejecución autónoma de pasos críticos en la cirugía de la columna vertebral.
Objetivo
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.
Ámbito científico
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- medical and health sciencesclinical medicinesurgeryrobotic surgery
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-ICT-2020-2
Régimen de financiación
RIA - Research and Innovation actionCoordinador
3000 Leuven
Bélgica