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The Use of Patient-Specific Three-Dimensional Printed Surgical Models Enhances Plastic Surgery Resident Education in Craniofacial Surgery

Lobb, David C., MD; Cottler, Patrick, PhD; Dart, Dwight, MS; Black, Jonathan S., MD, FACS

doi: 10.1097/SCS.0000000000005322
Original Articles

Purpose: A significant challenge in surgical education is to provide a meaningful hands-on experience with the pathology the trainee will see in independent practice. Craniofacial anatomy is challenging and unfamiliar to the learner.

Methods: Using preoperative computed tomography data, the authors produced an accurately sized, three-dimensional (3D) printed model of the congenital craniofacial anatomy of patients treated by the same attending surgeon–PGY4 resident surgeon pair over the course of a 6-month rotation. A preoperative stepwise surgical plan was written by the attending and resident, and the plan was marked on the 3D model by the attending and resident separately. The written and marked plans were measured for accuracy and time to completion. The resident surgeon's applicable milestone levels were assessed.

Results: Seven congenital craniofacial anomalies met criteria for inclusion: 4 craniosynostosis cases, 2 mandibular distractions, and 1 LeFort I distraction. The number of inaccuracies of the written plan improved from 5 to 0 for sagittal synostosis and 4 to 0 for mandibular distraction. The time to complete the written plan decreased by 22% for sagittal synostosis and 45% for mandibular distraction. The number of inaccuracies of the marked plan decreased from 5 to 0 for sagittal synostosis and 2 to 0 for mandibular distraction. Time to completion of the marked plan decreased by 76% for sagittal synostosis and 50% for mandibular distraction. Milestone scores increased an average of 1.875 levels.

Conclusion: Three-dimensional printed craniofacial models are a positive addition to resident training and have been objectively quantified to improve the accuracy and time to completion of the surgical plan as well as progression in the plastic surgery milestones.

Department of Plastic Surgery, University of Virginia, Charlottesville, VA.

Address correspondence and reprint requests to Jonathan S. Black, MD, FACS, Department of Plastic and Maxillofacial Surgery, University of Virginia Health Systems, West Complex, Fourth Floor, 1300 Jefferson Park Ave., Charlottesville, VA 22908; E-mail:

Received 17 July, 2018

Accepted 24 December, 2018

Sources of funding: University of Virginia Graduate, Medical Education Innovation Grant.

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 (

Successful craniofacial surgery requires an advanced technical competence with complex anatomy that is difficult to visualize in the operating room. Each patient's pathologic skeletal anatomy is often vastly different from normal craniofacial anatomy and unique to the patient. The acquisition of this competence is further complicated by the small size of pediatric patients and rare disease presentation. Craniosynostosis, for example, is a relatively common craniofacial diagnosis, yet only presents in an average of 1 in 2500 live births.1 The combination of these factors often leads to incomplete education in craniofacial surgery and considerable issues with the learner's comfort with highly complex procedures at the completion of training.

To address this knowledge gap, models of craniofacial anatomy have been used to complement resident and fellow education. The use of simulators in surgical training has clear and proven benefits.2 However, the currently available generic models of craniofacial anatomy fail to accurately represent the patient-specific pathology encountered in reality and thus do not adequately develop the learner's surgical planning and execution skills. Improvement in these skills can also translate into safer, shorter operations with improved patient outcomes.

We present the use of patient-specific, three-dimensional (3D) models in the training of plastic surgery residents in craniofacial surgery at our institution. The models are made to scale for each patient with exact representation of each patient's pathologic skeletal anatomy.

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Local Institutional Review Board (IRB) approval was obtained for this study. Over a single Resident-Attending rotation period of 6 months from July 2017 to December 2017, all surgically treated presentations of craniosynostosis, micrognathia, and maxillary hypoplasia were included. Patients who presented within this 6-month period but whose surgery fell outside of this 6-month period (and were therefore not surgically treated by the same resident-attending team) were excluded. For full evaluation and comparison of variable described below, 2 or more of a similar procedure were necessary for inclusion.

Using diagnostic, preoperative computed tomography (CT) scans obtained as part of routine delivery of care, and two 3D printed models were manufactured for each patient. No additional CT scans were obtained specifically to facilitate the making of the models. Each patient's CT DICOM (Digital Imaging and Communications in Medicine) data was reprocessed to a 3D printable stereolithography (STL) file using Mimics software (Materialise NV, Leuven, Belgium). Within Mimics software, the craniofacial skeleton was segmented on axial slices using the threshold tool. A 3D image was made and the area of interest (in this instance, the craniofacial skeleton) was isolated using the region-growing tool. The edit mask tool was used to manually exclude tissue other than bone from the segmented image. Finally, the STL + tool was used to create the STL file. This file was exported to the 3D printer.

The models were formed using Acrylonitrile Butadiene Styrene plastic (ABS, a common thermoplastic polymer) in fused-deposition modeling (FDM) 3D printers (Stratasys, Eden Prairie, MN) (Fig. 1). The segmented STL file made, as above, was transferred to the printer station. The file was printed in layers of 0.010-inch thickness using a thermal tip heated to the melting point of ABS plastic. Each layer was sequentially added to the previous layer until the printed model is completed. A dissolvable support material surrounds the model to allow printing of fragile and detailed geometry without fracture of the model during postprocessing (Supplemental Digital Content, Video 1, A uPrint 3D printer was used for the small skulls, and a FORTUS 400 3D printer was used for the larger skulls (Stratasys, Eden Prairie, MN).



The day before the planned surgeries, the resident and attending separately completed timed, written step-wise surgical plans. In addition, the planned osteotomies were marked on the 3D printed model separately by the resident and attending and the time (secondary outcome measure) taken to complete this was recorded (Fig. 2). Both the written surgical plans and the marked plans 3D printed models were compared and the resident's plan was evaluated by the attending (JSB) for the number of inaccuracies (primary outcome measure) (ie, missing, misplaced or inappropriate major steps). With the written plan, missed steps were counted separately from mis-ordered steps with missed steps given double the weight in terms of inaccuracy. The combined score was added and termed “number of inaccuracies.” In order for a step to count, the concept had to be present rather than the resident providing perfect detail. The marked plan on the 3D model had the same criteria of counting missed steps. Rather than a “mis-ordered” step, if the marking was drawn in a location that would lead to a suboptimal result (e.g., osteotomies on a cranial vault leading to poor shape) this counted as an inaccuracy. The senior author's plan served as the standard plan for comparison with the resident. This was done to match what the resident would see intraoperatively. All the surgeries were then completed the following day by the same resident-attending team.



The resident surgeon's milestone levels (The Accreditation Council for Graduate Medical Education and The American Board of Plastic Surgery, Inc.) in the subcompetencies of Patient Care and Medical Knowledge for Congenital Anomalies and Maxillofacial Trauma were assessed separately and blindly by all teaching faculty members at the beginning and end of the 6-month period.

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Over the 6-month time period, 4 craniosynostosis cases (3 sagittal synostoses and 1 metopic synostosis), as well as 2 mandibular distractions and 1 LeFort I distraction met criteria for inclusion. The average cost of producing the 3D models was $281.61 ($154.60–552.90).

During this period, the number of inaccuracies on the written surgical plans decreased to zero from an average of 5 for the synostosis cases and to 0 from an average of 4 for the mandibular distraction cases. The time to complete the written surgical plan decreased by 22 seconds (22%) for the synostoses cases and by 2:02 minutes (45%) for the mandibular distraction cases. The number of inaccuracies of the marked plan on the 3D model decreased from 5 to 0 for sagittal synostosis and from 2 to 0 with mandibular distraction. The time to complete the marked plan on the 3D printed model decreased by 26 seconds (76%) for the synostosis cases and by 45 seconds (50%) for the mandibular distraction cases.

The milestone level for the resident surgeon increased an average of 1.875 levels across all domains with a 2-point increase in each of the subcategories of Patient Care and Medical Knowledge for Congenital Anomalies (1.5–3.5) and in Medical Knowledge of Maxillofacial Trauma (1–3).

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When the Next Accreditation System Milestones project was adopted into plastic surgical training in 2014, techniques that objectively measure and improve the competence of trainees became ever more valuable. Traditionally, written and/or oral examinations have comprised the primary mechanism for the objective measurement of competency. However, with the implementation of this new method of education monitoring came the understanding that alternative systems of teaching and competence measurement would be required. Added to this paradigm shift in education monitoring are the demands on education imposed by the work-hour restrictions, the expanding body of medical knowledge, and the increased service requirements. It has become very apparent that a wide range of tools must be used to facilitate successful and efficient resident surgical education.3 In answer to this problem, the use of simulation within medical education, both for training and measurement of competence, has gained traction. Indeed, the Accreditation Council for medical Education (ACGME) outlines the importance of simulation in medical education in their 2015 report.4

Defining a simulator, Rosen et al state that it is a device that simulates “a real-world system or apparatus, allowing users to gain experience.”2 One of the first medical simulators, and arguably the best known, is Resusci-Anne which was developed in the 1960s through a collaboration between 2 anesthesiologists, Dr Peter Safar of Baltimore City Hospital and Dr Bjorn Lind of Norway. They together with toy manufacturer, Asmund Laerdal, understood that the simulation of cardiopulmonary resuscitation would translate into improved patient outcomes.5 More recently and specific to the discipline of plastic surgery, simulators to advance the technical understanding of trainees in the surgeries of cleft palate repair and breast augmentation have been described.6,7 Finally, high-fidelity models that simulate the surgical steps required for successful remodeling of the skull in craniosynostosis surgery have also been published.8

Criticism of the use of simulators in medical training center often relates to their inability to provide the learner with a detailed and realistic experience. Cadaveric dissection is often employed in training as it provides excellent simulation of the tissue planes encountered in surgery. The primary drawback of cadaveric simulation is the lack of pathology to provide a realistic experience. To address this, advanced simulators, such as that produced by Coelho et al, have been developed at high cost. This simulator provides a realistic experience with a patient for scaphocephalic correction and was created using a single patient's CT data. This simulation experience, however, will not account for the differences seen between patients which is widely present and critical in the practice of craniofacial surgery.

Cost is a concern in plastic surgery education where research funding, funding for travel to meetings, and departmental activities already burden the training experience. However, this report details the use of a lower cost but effective simulation model with an average cost of $281.61 ($154.60–552.90) per model. The cost related to the amount of material needed to print and increased with older, larger patients with more skeletal thickness. The smaller patient (eg, infant) provides an excellent situation to simulate a surgery with a cheaper model based on an exact representation of their pathologic skeletal anatomy. Eventually, it is our goal to reimburse these models within the procedure billing analogous to what is currently done with virtual surgical planning. Our patient-specific models with precise, scaled anatomy demonstrate a considerable leap from current simulators based on single-patient models which are replicated for each use (eg, cleft palate and craniosynostosis models).

Limitations of this report include the small number of encountered cases during the 6-month period that were available for inclusion in this study. This number, however, reflects the relative infrequency of the encountered craniofacial anomalies seen at moderate volume centers. This low number actually therefore serves to underline the importance of the use of simulators in plastic surgical training9Resusci-Anne is used to teach skills that the vast majority of learners hope to never use but who will be glad to have if they find themselves in the relevant crisis. This same argument can be applied to the use of simulators in craniofacial surgery, where presentations are less frequent but higher level skills are required to address them and acquisition of these skills is facilitated by the use of a cost-effective 3D printed model.

An additional limitation is the lack of a control population such as another learner not using the 3D model. Our apprentice style of one-on-one training was not conducive to allow for another resident learner to be present. This could have been attempted with the subsequent resident on rotation; however, this would have been a poor control due both to the differences in cases and learners. Ideally, 2 learners of equally low experience in craniofacial surgery (eg, medical students) would provide a control group to further evaluate the impact of this single learning tool. The authors are currently involved in this, performing this follow-up study. Finally, the senior author was examining the resident which could have unintended bias. This was performed due to the senior author's knowledge of the plan and its markings without having another plastic surgeon at our facility with that knowledge. For this reason, the assessment data (inaccurate steps, time to completion) are objective and should be without much bias.

This report demonstrates objective improvement in a resident learner's technical knowledge of craniofacial surgery by means of milestone advancement, accuracy of the preoperative plan, and decreased time needed to formulate the plan. These patient-specific models replicated the pathological anatomy encountered by the resident learner during a 6-month period of training. The models provided the opportunity for the trainee to experience an exact replica of the patient's skeletal craniofacial anatomy just before participating in the actual procedure.

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1. Kutkowska-Kazmierczak A, Gos M, Obersztyn E. Craniosynostosis as a clinical and diagnostic problem: molecular pathology and genetic counseling. J Appl Genet 2018; 1:1–15.
2. Rosen JM, Long SA, McGrath DM, et al. Simulation in plastic surgery training and education: the path forward. Plast Reconstr Surg 2009; 123:729–738.
3. Knox ADC, Gilardino MS, Kasten SJ, et al. Competency-based medical education for plastic surgery: where do we begin? Plast Reconstr Surg 2014; 133:702–710.
4. Holmboe ES, Edgar L, Conforti L, et al. Reflections on the first 2 years of milestone implementation. J Grad Med Educ 2015; 2014:506–512.
5. Grenvik A, Schaefer J. From Resusci-Anne to Sim-Man: the evolution of simulators in medicine. Crit Care Med 2004; 32 (2 Suppl.):S56–S57.
6. Vadodaria S, Watkin N, Thiessen F, et al. The first cleft palate simulator. Plast Reconstr Surg 2007; 120:259–261.
7. Kazan R, Courteau B, Cyr S, et al. A novel mammoplasty part-task trainer for simulation of breast augmentation: description and evaluation. Simul Healthc 2016; 11:60–64.
8. Coelho G, Warf B, Lyra M, et al. Anatomical pediatric model for craniosynostosis surgical training. Child's Nerv Syst 2014; 30:2009–2014.
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3D; craniofacial surgery; model; simulator; surgical training; three-dimensional

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© 2019 by Mutaz B. Habal, MD.