Early diagnosis of peripheral lung cancer remains a major clinical challenge due to the limited reach of conventional bronchoscopes and the heavy reliance on fluoroscopy or electromagnetic tracking, exposing both patients and clinicians to ionizing radiation. This talk presents a unified robotic and AI-driven framework for safe, precise, and radiation-free navigation of flexible instruments inside the lung airways.
The presentation integrates two complementary technological directions developed and validated by our research group:
- a compact robotic platform (RoboCath) for precise manipulation of long, flexible catheters during bronchoscopy, designed to seamlessly integrate with standard bronchoscopes. The robot enables controlled translation and rotation of instruments beyond the bronchoscope tip, significantly improving access to peripheral lung regions while reducing procedural complexity and X-ray dependency. Extensive feasibility testing in anatomically accurate lung phantoms demonstrated reliable navigation across multiple bronchial generations with a small operating-room footprint and sterilization-compatible design
- a novel shape-sensing navigation system based on Fiber Bragg Grating (FBG) technology and artificial intelligence (AIrShape), capable of tracking the entire catheter shape in real time. By matching the measured catheter geometry to precomputed airway centerlines using a multi-view convolutional neural network, the system identifies the active airway without fluoroscopy or electromagnetic tracking. Experimental validation showed a mean airway identification accuracy of approximately 91%, enabling safe navigation toward peripheral lung lesions beyond the reach of conventional imaging guidance
Together, these results demonstrate how robotic actuation, optical shape sensing, and AI-based anatomical reasoning can be combined into next-generation bronchoscopy platforms. The proposed technologies pave the way for radiation-free, cost-effective, and scalable robotic systems for early lung cancer diagnosis, with strong potential for future preclinical and clinical translation.
Lucian Georghe GRUIONU
Professor, PhD, Eng. (Habil.), Faculty of Mechanics – University of Craiova, Romania
Research field: Medical Robotics, Image-Guided Interventions, AI in Medicine
Lucian Gheorghe Gruionu, Professor, PhD, Eng., habil., is a senior academic and researcher in mechanical and biomedical engineering at the Faculty of Mechanics, University of Craiova, Romania. His research activity over more than two decades has been focused on medical robotics, image-guided interventions, biomechanics, and the development of advanced medical instruments and intelligent diagnostic systems.
Professor Gruionu has extensive international research experience, having worked as a researcher and collaborator at prestigious institutions in the United States, including Johns Hopkins University, Georgetown University, and Indiana University School of Medicine. His work has been carried out in close collaboration with clinicians, addressing real clinical needs in minimally invasive procedures, particularly in bronchoscopy, endoscopy, interventional radiology, and oncologic diagnostics.
He has served as principal investigator and project director for numerous national and international research grants, including large collaborative projects funded through European and Norwegian mechanisms, focusing on artificial intelligence, robotic navigation, and innovative medical devices. His research has led to the design and validation of novel robotic platforms for flexible instrument navigation, as well as AI-based systems for radiation-free guidance in complex anatomical environments.
Professor Gruionu is the author and co-author of over one hundred peer-reviewed journal articles, conference papers, and book chapters, and he is an inventor of patented medical devices in the field of medical robotics and image-guided interventions. In parallel with his academic activity, he is co-founder and CEO of an R&D-oriented company, actively involved in the translation of research results toward clinical and industrial applications.
His current research interests lie at the intersection of robotics, sensing technologies, and artificial intelligence, with the objective of developing safer, smarter, and clinically relevant systems that enhance physician capabilities and improve patient outcomes in minimally invasive medicine.