Lucian GRUIONU
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.
Abstract
An Open-Source AI-Assisted Workflow for Bone Defect Segmentation, Implant Planning and Biomechanical Loading Integration
Lucian Gheorghe GRUIONU
Background: Personalized biodegradable magnesium-based bone implants require the integration of medical imaging, anatomical reconstruction, implant design and biomechanical loading analysis. In the MagReBone project, previous activities established an AI-assisted workflow for CT-based bone defect segmentation, 3D reconstruction of femoral defects, lattice implant design and biomechanical data analysis.
Objective: We propose OpenMagReBone AI Planner, an open-source AI-assisted research prototype for integrating bone defect segmentation, 3D implant planning and biomechanical loading data in the development of personalized magnesium-based implants.
Methods: The proposed workflow combines 3D Slicer and MONAI Label for CT-based bone and defect segmentation, CAD-derived 3D models for magnesium lattice implant visualization, and an open-source biomechanical layer based on OpenSim. Markerless or low-cost motion analysis can be performed using OpenCap, Pose2Sim or FreeMoCap, providing an alternative to proprietary motion capture systems. The prototype imports femur, defect and implant models, calculates geometric descriptors of the defect, visualizes the implant relative to the reconstructed anatomy, imports biomechanical loading curves and generates AI-assisted recommendations for finite element analysis preparation.
Expected Results: The system is designed to generate a structured demonstrator including 3D visualization, defect metrics, representative biomechanical loading profiles, implant-related design comments and an automatically generated technical report. The prototype does not replace clinical or engineering validation, but supports reproducible integration of imaging, implant design and biomechanical data.
Conclusion: OpenMagReBone AI Planner provides a feasible open-source pathway for transforming MagReBone results into a demonstrable AI-assisted platform for personalized biodegradable magnesium implant planning. This approach improves reproducibility, reduces dependence on proprietary licenses and creates a foundation for future patient-specific validation.
BiomMedD' 2026
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