Secondary Logo

Institutional members access full text with Ovid®

A Craniomaxillofacial Surgical Assistance Workstation for Enhanced Single-Stage Reconstruction Using Patient-Specific Implants

Murphy, Ryan J. PhD; Liacouras, Peter C. PhD; Grant, Gerald T. DMD, MS; Wolfe, Kevin C. PhD; Armand, Mehran PhD; Gordon, Chad R. DO, FACS

doi: 10.1097/SCS.0000000000003106
Original Articles

Background: Craniomaxillofacial reconstruction with patient-specific, customized craniofacial implants (CCIs) is ideal for skeletal defects involving areas of aesthetic concern—the non-weight-bearing facial skeleton, temporal skull, and/or frontal-forehead region. Results to date are superior to a variety of “off-the-shelf” materials, but require a protocol computed tomography scan and preexisting defect for computer-assisted design/computer-assisted manufacturing of the CCI. The authors developed a craniomaxillofacial surgical assistance workstation to address these challenges and intraoperatively guide CCI modification for an unknown defect size/shape.

Methods: First, the surgeon designed an oversized CCI based on his/her surgical plan. Intraoperatively, the surgeon resected the bone and digitized the resection using a navigation pointer. Next, a projector displayed the limits of the craniofacial bone defect onto the prefabricated, oversized CCI for the size modification process; the surgeon followed the projected trace to modify the implant. A cadaveric study compared the standard technique (n = 1) to the experimental technique (n = 5) using surgical time and implant fit.

Results: The technology reduced the time and effort needed to resize the oversized CCI by an order of magnitude as compared with the standard manual resizing process. Implant fit was consistently better for the computer-assisted case compared with the control by at least 30%, requiring only 5.17 minutes in the computer-assisted cases compared with 35 minutes for the control.

Conclusion: This approach demonstrated improvement in surgical time and accuracy of CCI-based craniomaxillofacial reconstruction compared with previously reported methods. The craniomaxillofacial surgical assistance workstation will provide craniofacial surgeons a computer-assisted technology for effective and efficient single-stage reconstruction when exact craniofacial bone defect sizes are unknown.

Supplemental Digital Content is available in the text

*Johns Hopkins University Applied Physics Laboratory, Laurel

Walter Reed National Military Medical Center, Bethesda, MD

Oral Health and Rehabilitation Department, University of Louisville Dental School, Louisville, KY

§Department of Mechanical Engineering, Johns Hopkins University

||Department of Orthopedic Surgery

Departments of Plastic Surgery and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD.

Address correspondence and reprint requests to Chad R. Gordon, DO, FACS, Assistant Professor of Plastic Surgery and Neurosurgery, Johns Hopkins University School of Medicine, Co-director, Multidisciplinary Adult Cranioplasty Center, The Johns Hopkins Hospital, Baltimore, MD, 21287; E-mail:

Received 27 May, 2016

Accepted 19 July, 2016

The views expressed in this article are those of the authors and do not necessarily reflect the official policy, position, or endorsement of the Department of the Navy, Army, Department of Defense, nor the U.S. Government.

Dr CRG's salary support was kindly provided, in part, by the 2012 to 2014 Mary Lou and David Furnas Academic Scholar Award provided by the American Association of Plastic Surgeons (AAPS).

Additional grant funding included the Johns Hopkins Institute for Clinical and Translational Research's Accelerated Translational Incubator Pilot (ATIP) Award (funded by the National Institute of Health), Independent Research and Development (IRAD) funds from the Johns Hopkins Applied Physics Laboratory, the Abell Foundation Prize/Johns Hopkins Alliance for Science and Technology development, and a Maryland Innovation Initiative Phase I grant from the State of Maryland's Technology development Corporation (TEDCO).

This publication was made possible by the Johns Hopkins Institute for Clinical and Translational Research (ICTR) which is funded in part by the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the Johns Hopkins ICTR, NCATS or NIH [NCATS grant # UL1TR000424-06].

This paper was presented, in part, at the 37th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC), Milano, Italy.

The authors report no conflicts of interest.

Supplemental digital contents are available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (

© 2016 by Mutaz B. Habal, MD.