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

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.

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Available for download on Friday, June 12, 2026

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