Grants Funded

Abstract

Impact Statement: Our novel augmented reality (AR) model as applied to traumatic craniofacial fractures has the potential to increase diagnostic accuracy, improve spatial assessment, and decrease user cognitive load. This would improve pre-operative planning, precision of fracture reduction, and ultimately surgical outcomes. This validation study will lay the groundwork and preliminary data for prospective applications of the AR model to direct patient care. Our goal is to develop a technology that, if convincingly validated on a large scale, can supplement or even replace traditional image viewing modalities for craniofacial fractures.

Project Summary: Craniofacial trauma continues to be a significant cause of morbidity and mortality around the world. Accurate diagnoses and spatial assessment of craniofacial fractures is critical for pre-operative planning, precise fracture reduction, and reducing post-operative complications. Yet, in complex craniofacial trauma with multiple fractures, diagnoses can be difficult even for an experienced radiologist or surgeon. Augmented reality (AR) is an emerging tool that could provide surgeons with better spatial comprehension of these fractures than traditional computed tomography (CT) scans. The objective of this proposal is to test whether a novel AR model of complex craniofacial fractures, compared to traditional imaging modalities, increases diagnostic accuracy and spatial understanding while decreasing user cognitive load. To achieve these aims, a database of traumatic craniofacial fractures will be compiled to comprehensively represent the variety of well-described fracture patterns. This database will be converted into a novel AR model we have developed for preoperative planning. Thirty radiologists and surgeons involved in the care of patients with craniofacial trauma will then assess the database of craniofacial fractures and be evaluated on diagnostic accuracy, spatial displacement comprehension, and task cognitive load. These variables were selected as objective measures of the direct impact that our AR model has on important preoperative planning considerations. Univariate and multivariate statistical analyses stratified by provider level will be performed to compare AR models to traditional views on fracture diagnosis, spatial assessment, and cognitive load.

Biography
Solomon Lee, M.D., M.S. is a surgical resident (PGY-3) in the Division of Plastic and Reconstructive Surgery at the University of California, San Francisco (UCSF). He received his B.A. from Stanford University, his M.S. in Global Health Sciences at UCSF, and his M.D. at UCSF. He plans to pursue a craniofacial fellowship with the career goal of treating congenital and acquired deformities of the head and face in adults and children. His research experience includes basic science on tissue regeneration, clinical studies in plastic surgery, and public policy in global health. His current research on craniofacial surgery aligns with his career goals. He is specifically focused on the application of innovative technologies to improve craniofacial surgical outcomes.