Date Approved
6-11-2024
Embargo Period
6-12-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.)
Department
Electrical and Computer Engineering
College
Henry M. Rowan College of Engineering
Funder
IEEE
Advisor
Nidhal Bouaynaya, Ph.D.
Committee Member 1
Gina Tang, Ph.D.
Committee Member 2
Ravi Ramachandran, Ph.D.
Committee Member 3
Charalampos Papachristou, Ph.D.
Committee Member 4
Sean McMillan, D.O.
Keywords
Automated alignment;Femur fractures;Femur landmarks segmentation;Image-guided methods;Surgical robotic system
Subject(s)
Orthopedic surgery--Automation; Machine learning
Disciplines
Biomedical Engineering and Bioengineering | Electrical and Computer Engineering | Engineering
Abstract
Long-bone fractures, particularly femur fractures, are prevalent and necessitate surgical intervention due to traumatic forces. Manual reduction procedures pose challenges, including excessive traction forces and reliance on limited X-ray imaging, resulting in malalignment-related complications and repeated surgeries. Current methods for femur fracture reduction are prone to inaccuracies, prolonged surgical times, and postoperative complications. This research introduces image-guided methods to automate a surgical robotic system, Robossis, to enhance femur alignment accuracy, reduce procedure times, and improve patient outcomes. To address these challenges, a marker-based approach utilizing a few X-ray images is developed to detect the spatial relative position of the femur fragments. This information plays a key role in guiding surgical robots for femur fracture alignment surgeries. Utilizing machine learning techniques, a U-Net model is tailored and trained on a cutting-edge dataset to delineate distinct femur landmarks, serving as critical guides for the robot’s navigation and aiding in alignment assessments. The developed Robossis system integrates optical tracking with a 6-DOF parallel robot, allowing real-time spatial data acquisition and automated alignment. Laboratory and cadaver experiments demonstrate its submillimeter accuracy in achieving femur alignment. This integrated approach advances femur fracture surgery by automating alignment, reducing intraoperative radiation exposure, and improving surgical precision.
Recommended Citation
Saeedi-Hosseiny, Marzieh Sadat, "TRANSFORMING ORTHOPEDIC SURGERY: AUTONOMOUS IMAGE-GUIDED TECHNIQUES FOR FEMUR FRACTURE ROBOTIC SURGERY" (2024). Theses and Dissertations. 3239.
https://rdw.rowan.edu/etd/3239
Included in
Biomedical Engineering and Bioengineering Commons, Electrical and Computer Engineering Commons