TY - JOUR
AU1 - Lichtenberger, John P
AU2 - Tatum, Peter S
AU3 - Gada, Satyen
AU4 - Wyn, Mark
AU5 - Ho, Vincent B
AU6 - Liacouras, Peter
AB - Abstract Objectives This work describes customized, task-specific simulation models derived from 3D printing in clinical settings and medical professional training programs. Methods Simulation models/task trainers have an array of purposes and desired achievements for the trainee, defining that these are the first step in the production process. After this purpose is defined, computer-aided design and 3D printing (additive manufacturing) are used to create a customized anatomical model. Simulation models then undergo initial in-house testing by medical specialists followed by a larger scale beta testing. Feedback is acquired, via surveys, to validate effectiveness and to guide or determine if any future modifications and/or improvements are necessary. Results Numerous custom simulation models have been successfully completed with resulting task trainers designed for procedures, including removal of ocular foreign bodies, ultrasound-guided joint injections, nerve block injections, and various suturing and reconstruction procedures. These task trainers have been frequently utilized in the delivery of simulation-based training with increasing demand. Conclusions 3D printing has been integral to the production of limited-quantity, low-cost simulation models across a variety of medical specialties. In general, production cost is a small fraction of a commercial, generic simulation model, if available. These simulation and training models are customized to the educational need and serve an integral role in the education of our military health professionals. Introduction This article reviews the benefits of utilizing three-dimensional (3D) printing (also known as additive manufacturing) to create medical simulation models and task trainers in the education and training of military health care professionals. Being able to produce these simulators in house has allowed providers to train/practice before they perform a procedure on a service member or veteran. Background The terms “additive manufacturing,” “rapid prototyping,” and the popular “3D printing” refer to using three-dimensional data to construct physical objects in layer-by-layer manufacturing process. This technology was originally developed in the 1980s to accelerate the production of small, custom-designed objects.1 Twenty years later, additive manufacturing was utilized to make dental implants and custom prosthetics. This technology now enables the construction of physical models derived from medical imaging data or computer-designed structures such as prosthetics and models.2 The benefits of virtual 3D reconstruction in medical imaging have traditionally centered on the improved ability to identify and communicate pathology, describe complex anatomic relationships, and plan intricate surgical intervention.3,4 Besides the well-documented benefit of individualized prosthetic and model creation, the advent of 3D Printing is revolutionizing the teaching of anatomy, complex pathology, and procedural skills by allowing key anatomic relationships with surgical relevance, such as the relationship of major vessels to pathology, to be physically re-created for surgical planning and even practice. The most common 3D printing technologies used in medical applications are vat photopolymerization, material jetting, binder jetting, powder bed fusion, and material extrusion. At our institution, we are fortunate to have most of these technologies at our disposal, which allows us the versatility to use whatever technology best fits the modeling request. The overall concept of 3D printing across various technologies is similar. In 3D printing, a material transition occurs via light, heat, or binders to transition a liquid or granular material into a solid 3D object. In most medical applications of 3D printing, the patient-specific input data are derived from surface capture or 2D imaging data in DICOM (Digital Imaging and Communications in Medicine) format, the standard format used in clinical radiology. Using commercially available image post-processing software, a set of DICOM images can be segmented based on gray-scale thresholding of the Hounsfield units. These segmentations can then be used to generate 3D volumes of the area(s) of interest. These 3D volumes are then exported as a stereolithography (STL) file. Other operations such as triangle reductions, smoothing algorithms, and cuts can be performed on these models after they are in STL form. In the medical arena, the most frequently used modality in 3D printing is computed tomography (CT) due to its spatial, temporal, and contrast resolution but also due to its speed, relatively lower cost, ease of use, and broad availability. However, MRI has distinct advantages in terms of tissue contrast and lack of patient radiation exposure, and physical 3D modeling of functional data, which may be particularly useful in neurosurgery applications.5 Simulation is an effective and frequently utilized method for training health care professionals to perform patient procedures. Task trainers are models of varied fidelities designed to simulate performance of specific procedural skills and are often utilized in medical simulation. However, these skill-specific models are expensive and are limited by market availability. Through inter-professional collaboration between the three-Dimensional Medical Applications Center, the Department of Radiology, the Department of Simulation, and the Naval Postgraduate Dental School at Walter Reed National Military Medical Center, we have developed several new simulation task trainers using digital constructs. These simulation models are easily customizable to fulfill a range of training goals and objectives, demonstrate a high degree of realism, and can be produced at a low cost. Methods As each task trainer is customized to the simulated procedure, there are no standard methods that can be repeated. The most important part of any simulation model is that the procedure being performed has a sense of realism, both visually and technically. Our current simulation models have originated from 3D reconstruction of CT data sets, 3D digital stereophotogrammetry, custom modeling in specialized digital software, computer-aided design (CAD) programs or utilizing pre-exiting files. Once the digital starting point and purpose of the simulation model are clearly defined, additional modeling and refinement of the design are necessary to incorporate all aspects of the simulation. When the digital manipulation is complete, additive manufacturing (3D printing) is used to create parts of the models directly and/or used to create tools for silicone molding. The availability of various methods to 3D print provides additional flexibility for a broader range of design and functionality for each model. In many cases, 3D printed parts, silicone rubber, and commonly available hardware supplies are combined to create the final realistic model. Simulation models then undergo initial in-house testing by medical specialists. Once completed, the simulation model is ready for larger scale beta testing. Feedback is then acquired to validate simulation model effectiveness and to guide future model improvements/enhancements in an iterative manner. Example Procedure: Task Trainer for Removing Ocular Foreign Bodies Three-dimensional digital stereophotogrammetry is used to acquire surface geometry data, which is exported as a STL or VRML file format (Fig. 1). The surface anatomy is used as an initial starting point to digitally model the soft tissue and underlying support structure (Fig. 2). Eyes were designed in CAD software and a texture (coloring) was added for realism. The task trainer eyes were 3D printed using a color binder jetting technology, infiltrated with cyanoacrylate, and then coated, via paintbrush, with polymethyl methacrylate to achieve a glossy look (Fig. 3). As no additive manufacturing machine can currently reproduce the soft mechanical behavior of human tissue, molds were designed from the digital soft tissue model and 3D printed using binder jetting (Fig. 4). Molds were packed with silicone to manufacture the soft silicone mask. After curing, the mask was removed from the mold and excess silicone trimmed from the edges (Fig. 5). The framework with through-slots for Velcro straps was 3D printed using material extrusion technology from ABS plastic. These Velcro straps allow the simulation model to be attached to the slit lamp. Molds for silicone lenses were designed using CAD and were then manufactured with material extrusion technology from ABS plastic. Silicone lenses were created using a transparent silicone (Fig. 6). The task trainer was assembled by placing the eyes in the framework followed by the placement of the lenses and debris. Other materials, such as reusable medical gel, have been used for the lenses. The silicone face was then laid over the framework and kept in place by through pins (Fig. 7). The simulator can then be attached to the slit lamp for task training. Using this realistic, low-cost, reusable model, providers are trained to use a needle to remove debris from the eye (Fig. 8). Lenses and eyes can be switched out for additional providers to train. Figure 1. Open in new tabDownload slide Three-dimensional digital stereophotogrammetry used to acquire surface geometry data for simulator. Figure 1. Open in new tabDownload slide Three-dimensional digital stereophotogrammetry used to acquire surface geometry data for simulator. Figure 2. Open in new tabDownload slide The surface anatomy is used as an initial starting point to digitally model soft tissue and underlying support structure. Figure 2. Open in new tabDownload slide The surface anatomy is used as an initial starting point to digitally model soft tissue and underlying support structure. Figure 3. Open in new tabDownload slide The task trainer eyes were 3D printed using a color binder jetting technology and then coated with polymethyl methacrylate to achieve a glossy look. Figure 3. Open in new tabDownload slide The task trainer eyes were 3D printed using a color binder jetting technology and then coated with polymethyl methacrylate to achieve a glossy look. Figure 4. Open in new tabDownload slide Molds were designed from the digital soft tissue model and 3D printed using a binder jetting process. Figure 4. Open in new tabDownload slide Molds were designed from the digital soft tissue model and 3D printed using a binder jetting process. Figure 5. Open in new tabDownload slide Molds were packed with silicone to manufacture soft silicone mask. Figure 5. Open in new tabDownload slide Molds were packed with silicone to manufacture soft silicone mask. Figure 6. Open in new tabDownload slide Molds for silicone lens were designed using computer-aided design and were then manufactured with material extrusion technology from ABS plastic. Silicone lenses were created using a transparent silicone. Figure 6. Open in new tabDownload slide Molds for silicone lens were designed using computer-aided design and were then manufactured with material extrusion technology from ABS plastic. Silicone lenses were created using a transparent silicone. Figure 7. Open in new tabDownload slide The task trainer was assembled by placing the eyes in framework followed by the placement of the lenses and debris. Figure 7. Open in new tabDownload slide The task trainer was assembled by placing the eyes in framework followed by the placement of the lenses and debris. Figure 8. Open in new tabDownload slide Providers are trained to remove eye debris, with a needle, on a more realistic, low-cost, reusable model. Figure 8. Open in new tabDownload slide Providers are trained to remove eye debris, with a needle, on a more realistic, low-cost, reusable model. Results Numerous custom simulation models have been successfully completed. Task trainers have been designed for procedures including removal of ocular foreign bodies, ultrasound-guided joint injections, nerve block injections, and various suturing and reconstruction procedures. These task trainers have been frequently utilized in the delivery of simulation-based training and demand for the creation of additional skill-specific task trainers continues to grow among our customer base. Several of these task trainers have undergone initial beta testing and improvements are under process. Discussion The study of anatomy, pathology, and the mastery of procedural tasks remain core elements of undergraduate and graduate medical education. Although the use of cadavers in medical education is currently under debate, the role of imaging and virtual technology in teaching anatomy and procedures is developing support.6,7 Three-dimensional printing technology may bridge the gap between the benefits of physical manipulation of cadavers in learning anatomy and the virtual anatomy constructs. In graduate medical education, a refined knowledge of anatomy is applied to disease intervention. As part of the medical training paradigm, interventionalists assume graduated levels of responsibility based on experience. This experience may be accelerated in a relatively risk-free environment with the use of models and phantoms, which closely simulate live patients. Simulation can also be used to ensure clinical competencies and the use of 3D printed models can ensure a universal experience and thus same standard of comparison for participants to ensure a common level for competency. With our ability to now provide customizable simulation models using 3D printing, many other applications such as our ocular foreign body simulation are being developed. Although previously dependent on fresh cadaver material for training, vascular interventionalists have now successfully created 3D training models to practice complex interventions in the ascending aorta.8,9 Models of the middle ear have been used to train complex procedures and magnified models of the same structure have been used for teaching.10 The primary obstacle to the clinical application of 3D printing in medical simulation and training is cost, both in terms of generating the physical product and the time, expertise involved in creating this product, and frankly creativity (Table I).11,12 These costs are decreasing, however, and reimbursement will follow the establishment of improved patient outcomes. Additionally, the cost of additive manufactured simulation models is a small fraction of other commercially available products. Furthermore, individual simulators can be easily adapted for modification or entirely different procedures. The downward trend of cost coupled with the increase in availability of 3D printers in medical facilities and universities implies a promising future of cost effectiveness on a much larger scale in professional and para-professional medical education and training. Table I. Ocular Foreign Body Simulator Cost One-Time Cost . Cost Per Individual Simulator . Face mold: $400 Frame/simulator backing: $50 Lens mold: $30 Eyes: $5/pair Other consumables: $20 Materials: $50 (face, lens, eyes) Consumables: $25 (Velcro, cyanoacrylate, silicone) Total one-time cost: $450 Total cost per simulator $130 One-Time Cost . Cost Per Individual Simulator . Face mold: $400 Frame/simulator backing: $50 Lens mold: $30 Eyes: $5/pair Other consumables: $20 Materials: $50 (face, lens, eyes) Consumables: $25 (Velcro, cyanoacrylate, silicone) Total one-time cost: $450 Total cost per simulator $130 Project cost: $580, for each additional simulator add $130. Open in new tab Table I. Ocular Foreign Body Simulator Cost One-Time Cost . Cost Per Individual Simulator . Face mold: $400 Frame/simulator backing: $50 Lens mold: $30 Eyes: $5/pair Other consumables: $20 Materials: $50 (face, lens, eyes) Consumables: $25 (Velcro, cyanoacrylate, silicone) Total one-time cost: $450 Total cost per simulator $130 One-Time Cost . Cost Per Individual Simulator . Face mold: $400 Frame/simulator backing: $50 Lens mold: $30 Eyes: $5/pair Other consumables: $20 Materials: $50 (face, lens, eyes) Consumables: $25 (Velcro, cyanoacrylate, silicone) Total one-time cost: $450 Total cost per simulator $130 Project cost: $580, for each additional simulator add $130. Open in new tab Conclusions The benefits that 3D printing (additive manufacturing) bring to clinical care, medical education, and training include an increase physician competency in specific procedural skills at a lower cost, a lower level of patient risk, and in some cases a lower morbidity due to complications in certain procedures. Additive manufacturing is an ideal solution to producing limited-quantity, low-cost, simulation models across many medical specialties. In general, production cost ranges in the hundreds of dollars, far less than the average price of a comparable simulation model, if available. Having the digital files available allows the flexibility to quickly modify the fidelity of the base model to develop new trainers that depict variations or increased complexity of the procedure. These simulation and training models are customized to the educational needs and serve an integral role in the education of our military providers. To date, we have created five specific task trainers, and our current models are in beta trials with the end goal of incorporating these training models into all relevant training curricula in support of our health professions training programs. Presentations Presented as a poster at the Military Health System Research Symposium, Kissimmee, FL, August 15–18, 2016 (abstract number: MHSRS-16-0753). References 1 Hull CW : Apparatus for production of three-dimensional objects by stereolithography. Google Patents; 1986 . Available at https://www.google.com/patents/US4575330; accessed February 7, 2017. 2 McGurk M , Amis AA, Potamianos P, Goodger NM: Rapid prototyping techniques for anatomical modelling in medicine . Ann R Coll Surg Engl 1997 ; 79 ( 3 ): 169 – 74 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 3 Lee EY , Boiselle PM, Shamberger RC: Multidetector computed tomography and 3-dimensional imaging: preoperative evaluation of thoracic vascular and tracheobronchial anomalies and abnormalities in pediatric patients . J Pediatric Surg 2010 ; 45 ( 4 ): 811 – 21 . Google Scholar Crossref Search ADS WorldCat 4 Fishman EK , Horton KM: The increasing impact of multidetector row computed tomography in clinical practice . Eur J Radiol 2007 ; 62 ( Supplement(0) ): 1 – 13 . Google Scholar Crossref Search ADS WorldCat 5 Spottiswoode BS , van den Heever DJ, Chang Y, et al. : Preoperative three-dimensional model creation of magnetic resonance brain images as a tool to assist neurosurgical planning . Stereotact Funct Neurosurg 2013 ; 91 ( 3 ): 162 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 6 Miles KA : Diagnostic imaging in undergraduate medical education: an expanding role . Clin Radiol 2005 ; 60 ( 7 ): 742 – 5 . Google Scholar Crossref Search ADS PubMed WorldCat 7 McLachlan JC , Bligh J, Bradley P, Searle J: Teaching anatomy without cadavers . Med Educ 2004 ; 38 ( 4 ): 418 – 24 . Google Scholar Crossref Search ADS PubMed WorldCat 8 Knox K , Kerber CW, Singel SA, Bailey MJ, Imbesi SG: Rapid prototyping to create vascular replicas from CT scan data: making tools to teach, rehearse, and choose treatment strategies . Catheter Cardiovasc Interv 2005 ; 65 ( 1 ): 47 – 53 . Google Scholar Crossref Search ADS PubMed WorldCat 9 Knox K , Kerber CW, Singel SA, Bailey MJ, Imbesi SG: Stereolithographic vascular replicas from CT scans: choosing treatment strategies, teaching, and research from live patient scan data . Am J Neuroradiol 2005 ; 26 ( 6 ): 1428 – 31 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 10 Suzuki M , Ogawa Y, Kawano A, Hagiwara A, Yamaguchi H, Ono H: Rapid prototyping of temporal bone for surgical training and medical education . Acta Oto-Laryngol 2004 ; 124 ( 4 ): 400 – 2 . Google Scholar Crossref Search ADS WorldCat 11 Tack P , Victor J, Gemmel P, Annemans L: 3D-printing techniques in a medical setting: a systematic literature review . BioMed Eng OnLine 2016 ; 15 . Available at https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-016-0236-4; accessed February 7, 2017. Google Scholar OpenURL Placeholder Text WorldCat 12 Thomas DS , Gilbert SW: Costs and cost effectiveness of additive manufacturing: a literature review and discussion. In: NIST Special Publication 1176, National Institute of Standards and Technology, Gaithersburg MD, 20899. Available at http://dx.doi.org/10.6028/NIST.SP.1176; accessed December 14, 2016. Author notes The views expressed in this article are those of the authors and do not necessarily represent the official position or policy of the U.S. Government, the Department of Defense, or the Department of the Air Force. The identification of specific products or scientific instrumentation does not constitute endorsement or implied endorsement on the part of the author, DoD, or any component agency. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US.
TI - Using 3D Printing (Additive Manufacturing) to Produce Low-Cost Simulation Models for Medical Training 
JF - Military Medicine
DO - 10.1093/milmed/usx142
DA - 2018-03-01
UR - https://www.deepdyve.com/lp/oxford-university-press/using-3d-printing-additive-manufacturing-to-produce-low-cost-g4kWzfAG8M
SP - 73
EP - 77
VL - 183
IS - suppl_1
DP - DeepDyve
ER -