Descripción del proyecto
Utilizar la robótica para redefinir la eficacia de la colonoscopia
La eficacia de la colonoscopia para detectar el cáncer depende de la destreza del especialista que la realiza, lo que plantea retos a medida que aumenta la demanda de revisiones, especialmente en los países europeos con programas nacionales. En este sentido, el equipo del proyecto IRE, financiado con fondos europeos, pretende transformar la tecnología endoscópica tradicional. En concreto, fusionará datos de especialistas, modelización biomecánica innovadora, retroalimentación sensorial y entrenamiento con maniquíes robóticos blandos. En el proyecto se aprovecha un amplio conjunto de datos de más de dos mil colonoscopias reales, combinando la experiencia de la vida real con la formación simulada en modelos biomecánicos. El resultado serán robots inteligentes capaces de navegar por los entresijos de la anatomía humana de forma autónoma, lo que mejorará el nivel de la detección precoz del cáncer.
Objetivo
In Intelligent Robotic Endoscopes (IRE) for Improved Healthcare Services we envision creating intelligent robotics solutions, extending current endoscope technology with robotics control that is based on learning from currently collected human operator data, coupled with novel bio-mechanical modeling techniques, and sensory feedback as well as soft robotics phantom for training.
The challenge with colonoscopy is that the success rate of detecting cancer depends on the skills of the clinician that operates the endoscope. From a health and societal perspective, the number of colonoscopies is bound to increase as they are the only way to screen patients for early cancer detection. Many European countries have national screening programs. This is a very big market in need of improved technology.
IRE enables a new generation of intelligent robots that through data, simulation and learning can interact with the interior of a living human while communicating with a human operator. The huge variation of human anatomy and the dynamic effect of human physiology make it a complicated navigational task to use endoscopes. Entanglement, haemorrhage, and perforation risks create a critical and difficult environment to navigate autonomously in where even trained human operators meet challenges. We exploit one of the largest datasets on real-life colonoscopies with more than 2,000 operations to learn safe navigation, combined with simulated training on a population of biomechanical models of the abdominal region.
IRE boosts the design and configuration of the robotic endoscope using digital twins and simulation, and careful inclusion of clinicians will speed up the process of integration. IRE will raise the level of autonomy by building upon simulation, imaging, and learning to yield an increased interpretation and understanding of the complex real- world environments, capable of anticipating the effect of human motions, adapting and replanning to avoid entanglement.
Ámbito científico
- medical and health sciencesbasic medicineanatomy and morphology
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticssoft robotics
- medical and health sciencesclinical medicineoncology
- medical and health sciencesbasic medicinephysiology
- medical and health scienceshealth scienceshealth care services
Palabras clave
Programa(s)
Convocatoria de propuestas
HORIZON-CL4-2023-DIGITAL-EMERGING-01
Consulte otros proyectos de esta convocatoriaRégimen de financiación
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinador
1165 Kobenhavn
Dinamarca