- Open Access
Rapid customization system for 3D-printed splint using programmable modeling technique – a practical approach
© The Author(s) 2018
- Received: 22 January 2018
- Accepted: 6 May 2018
- Published: 25 May 2018
Traditional splinting processes are skill dependent and irreversible, and patient satisfaction levels during rehabilitation are invariably lowered by the heavy structure and poor ventilation of splints. To overcome this drawback, use of the 3D-printing technology has been proposed in recent years, and there has been an increase in public awareness. However, application of 3D-printing technologies is limited by the low CAD proficiency of clinicians as well as unforeseen scan flaws within anatomic models.
A programmable modeling tool has been employed to develop a semi-automatic design system for generating a printable splint model. The modeling process was divided into five stages, and detailed steps involved in construction of the proposed system as well as automatic thickness calculation, the lattice structure, and assembly method have been thoroughly described. The proposed approach allows clinicians to verify the state of the splint model at every stage, thereby facilitating adjustment of input content and/or other parameters to help solve possible modeling issues. A finite element analysis simulation was performed to evaluate the structural strength of generated models. A fit investigation was applied on fabricated splints and volunteers to assess the wearing experience.
Manual modeling steps involved in complex splint designs have been programed into the proposed automatic system. Clinicians define the splinting region by drawing two curves, thereby obtaining the final model within minutes. The proposed system is capable of automatically patching up minor flaws within the limb model as well as calculating the thickness and lattice density of various splints. Large splints could be divided into three parts for simultaneous multiple printing.
This study highlights the advantages, limitations, and possible strategies concerning application of programmable modeling tools in clinical processes, thereby aiding clinicians with lower CAD proficiencies to become adept with splint design process, thus improving the overall design efficiency of 3D-printed splints.
- Programmable modeling
- Fracture immobilization
Upper-limb splints are employed in the treatment of immobilizing fractures, congenital deformities, and chronically degenerating orthopedic conditions. Plaster and thermoplastic sheets are primary materials employed in conventional fracture immobilization treatments. During the splinting process, the splint-fitting effect is greatly dependent on the skill and experience of the clinician because of the irreversibility of these materials and body-based contact models. Consequently, patient satisfaction levels during treatment also significantly vary depending on the clinician’s skill when performing splinting [1–3]. Inexperienced fitters may cause more pain or lead to poor immobilization. In addition, conventional splints are bulky and unsightly, thereby causing an obvious inconvenience to patients during treatment. Maintaining splints clean and dry is difficult: hence, the risk of infection spread also increases [3, 4].
In recent years, the introduction of 3D-printing techniques in orthopedic and rehabilitation practices has been extensively discussed because the use of such techniques renders it possible to customize orthoses as well as enhance patient treatment satisfaction levels [3–6]. Varieties of 3D-printed splints, which have recently been reported in media, are lightweight, well-ventilated, waterproof, and aesthetically pleasing, thereby addressing nearly all deficiencies of conventional splints [4, 6–9].
acquiring splint mesh model from the patient’s affected limb surface by means of a 3D scanner.
designing the splint model using computer-aided design (CAD) software tools and exporting fabrication data.
fabricating a physical splint by using of a 3D printing device.
Nonetheless, several issues exist in the above mentioned digitization processes. The quality and accuracy of the scan of the patient’s affected limb plays a critical role in determining the success rate of the split model subsequently designed. Occurrence of irregular holes in the scan are a common sight on the dark side of the limb model and skin wrinkles are observed between fingers where the scanning light rays cannot reach . In addition, it is difficult for an injured person to maintain the required posture the during 3D-scanning exercise, and even slight uncontrollable shaking of the patient’s limb can result in partial deformations or distortions appearing in the final scan. When employing the deformable-alignment technique [14–16], acquiring a complete result during the scanning process requires use of additional software, relevant techniques, and post-processing; this invariably involves increased investment of time and cost as well as specialized training to be provided to clinicians. Several CAD modeling approaches [3, 6, 14, 17] have been proposed for constructing splint models. The conventional splint-model construction technique involves dozens of steps, and the total time required depends on the operator’s CAD skills. Clinicians are invariably required to integrate the necessary design and medical knowledge and come up with a design feasible for use in the treatment. Actual interactions that occur between the clinician, CAD interface, and system feedback are not sufficiently clear; it is, therefore, difficult to evaluate the operational knowledge required by the clinician to eliminate errors that may occur during the design process. Although application of software-based tools for automatic generation of printable models has been claimed with regard to certain conceptual prototype designs of novel 3D-printed splints, the construction methodology employed and modeling mechanism have not yet been proposed [7, 8]. Finally, the printing stage takes approximately 10 h to complete splint fabrication [3, 14, 18]. In comparison, conventional splinting processes can be completed within 20 min; state-of-the-art 3D-printing solutions are, therefore, is still relatively time-consuming.
The proposed study describes development of a precompiled customization system to help clinicians design 3D-printed splints using a programmable modeling tool used in conjunction with a CAD software, thereby applying the modeling technique to patch-up small flaws in the anatomic model. The system has been designed to generate splint models for immobilization of distal radial/ulnar as well as carpal fractures. However, pathological/open fractures and fractures requiring internal fixation are excluded from proposed splint applications . The complex modeling sequence in splint design has been integrated into the automatic system, and the clinician does not need to repeat lengthy modeling operations. Furthermore, operations remain virtually unaffected by CAD skills of the operator. In addition, the parametric environment enables automatic calculation of the thickness and lattice pattern of various splints and divides the splints into multiple components to facilitate efficient printing.
This section presents operational guidelines to simply tasks involved at the 3D-scanning stage. Detailed steps and procedures followed in the development of the proposed automatic system for splint design through use of a programmable modeling tool, to address problems encountered during other stages, have also been discussed.
Holes existing in the model may be repairable during the modeling process; however, it is difficult to restore deformations and distortions, caused by shaking, to their correct form. In this respect, flaw-tolerant scanning is highly beneficial in completing a scan more quickly, hence reducing the possibility of errors induced owing to uncontrollable shaking.
Additional post-production procedures after scanning must be avoided, and only simple clipping must be performed to remove unnecessary environmental background and/or body regions.
CAD environment, modeling goal, and program overview
System designer and design agent
The proposed study is not aimed at generating a tedious manual model to be employed by the clinician during treatment. The modeling task has been compartmentalized to be implemented via two roles—the system designer and design agent. An engineer or designer familiar with the use of CAD software and programming languages can follow the detailed methodology below to create an automated customization system in advance, in the capacity of a system designer. A clinician is the end user of the precompiled system, and plays the role of the design agent—to execute splint design in accordance with patients’ conditions. The clinician does not need to know how the program works, and can, therefore, instead focus on the design and evaluation of splint models.
Software options for digital splint designs have been listed and comparisons between self-developed and existing CAD software have been performed in [20, 21]. Development and maintenance of self-developed software are more difficult and time-consuming. Considering programmability requirements for designing such a system, Rhinoceros 3D Version 5.0 (Robert McNeel & Associates) was used as the primary modeling environment jointly operated along with a visual programming tool—Grasshopper 3D (Robert McNeel & Associates). Rhinoceros 3D employs node-based graphics to edit and express parametric input–output relationships; it represents the primary program language employed in this study to accomplish automated modeling.
Splint feature definition
Division: The proposed system divides the splint into a 2- or 3-part set depending on the splint size. If 2 or 3 3D printers are available for concurrent use, the splint fabrication time could be reduced to 1/2 or 1/3 the original build time, respectively.
Lattice structure: Splint lattice patterns are created by means of a diamond structure to reduce weight and support material during printing as well as increase ventilation .
Assembly method: Screw seats are generated along long edges of each divided splint part to facilitate assembly by means of plastic M3 L10 flat-point Phillips screw sets with prefabricated screw caps (Fig. 2(b)). The number of screw seats used depends on the edge length.
Modeling workflow and program overview
All above stages are not executed in a single continuous process. Data flow during each stage is controlled by the three checkpoints depicted in Fig. 3(b). At each checkpoint, clinicians examine the splint status and confer a true value before continuing onto the next step or modifying the input area. Therefore, splint models is not generated instantaneously when the input is fulfilled; it gradually takes shape as the modeling procedure progresses. If the entire splint model were to be constructed using a single automated process, the concerned computation would be completed within tens of seconds. In that case, however, the modeling process may fail to generate a valid result if unforeseen problems are detected during modeling or computations involved in one of the stages. Furthermore, in the event of such a case, clinicians would not be able to identify the step that caused the system to fail. By using checkpoints, however, clinicians can decide to modify input curves, pattern-density parameters, and/or position of screw seats depending on the stage at which the failure occurred. Various reasons that potentially contribute to failure and corresponding appropriate responses are described in the next section. An advantage of the five-stage strategy is that it consumes a very little time to perform required computations at each stage, and the stage at which problems occur can be easily identified.
System programing process
An automated customization system was constructed using Grasshopper 3D as per the following procedure.
Limb-model import and immobilization-area assignment
For simulation of limb swelling, the mesh model is offset by approximately 2–3 mm (Fig. 5(a)) . Several gradual lines are generated in the immobility area between the two input lines, as depicted in Fig. 5(b), and their distribution density is determined based on the splint length. The spacing between the lines, as observed in this study, was of the order of 1–2 cm. The density level was sufficient for displaying most arm features; in the case at hand, 12 curves were inserted. The gradual lines extend upwards, as surfaces along the Z-axis, to intersect with the limb and subsequently generate cross-sectional curves for the U input of the network component in Grasshopper 3D (Fig. 5(c, d)).
If line A is drawn across the thumb web-space, this implies that dual cross-sections of the palm and thumb may appear in few projections near line A. Network modeling, however, allows projections of only single cross-sections. A procedure was, therefore, designed to merge together dual cross-sections and fix the thumb by means of a small gap, as depicted in Fig. 6. Upon detection of dual cross-sections, a line would pass through central points located on separate cross-sections and offset on both sides with distance of 5 mm as a rectangle (Fig. 6(a, b, c)). A new shape would, therefore, be obtained, via combination of the rectangle and connected cross-sections, and subsequently smoothed by means of the “Interpolate Curve” command (Fig. 6(d)). The shapes, thus obtained, would replace dual cross-sections appearing in the U input of the network component, and the design of a slim gap between cross-sections can be used to fix the thumb (Fig. 6(d)).
However, there may exist modeling flaws in the arm scan, such as presence of a hole in Sample A, as depicted in Fig. 1(b), which could result in the presence of unclosed curves at the intersection; these curves could, in turn, generate serious distortions on the basic surface(Fig. 6(e1)). To fix this problem, 16 points extracted from the unclosed curve could be used to regenerate the closed curve through use of the interpolation command (Fig. 6(e2, e3)), and the corresponding repaired cross-sections could generate the entire covering surface. Such techniques [15, 16, 23] help overcome issues encountered during scanning. However, if the observed hole is large or if the immobilization area overlaps with the edge, there still exists a chance that the covering surface would be distorted. In such situations, the clinician can fix the problem by slightly moving the curves or simplifying them by using fewer defining points to avoid generation of a distorted surface.
After the cross-sectional curves are ready, extreme points corresponding to each section on the XZ and XY planes were extracted to form curves for the V input (Fig. 7(a, b)). The network component can generate a parametric surface within the immobilization region (Fig. 7(c)). Cross-section curves run along the U direction of the surface while long edges run along the V direction.
Division and thickness generation
The wrist-splint default design is a two-part set, wherein the system evaluates the square measure of the basic surface and divides it into three equal parts if the total area is greater than a specified reference value, which in this case, was set as 260 cm2, as depicted in Fig. 8(b, c). The said reference value nearly equals the square measure of the covering surface of an adult palm. A larger splint used for ulnar-radius fractures is also divided into three equal parts. The system divides the edge length along the U-direction into three domains, as depicted in Fig. 8(d1, d2), and extracts isoparametric subsurfaces. Different colors are used to distinguish between the three surfaces depicted in the figure. If the splint area assumes a value nearly equal to 260 cm2, the clinician can adjust the size of the referred area to determine the portion.
Splint thickness are calculated through use of the Remap component (Fig. 8(a)) per the splint area, which may be as small as a child’s palm or as large as an adult’s forearm, i.e., ranging from 150 to 600 cm2. Based on the area domain under consideration, the splint thickness ranges from 2.8–4 mm. This conversion, however, represents only a rough estimation, and accurate calculation of the surface thickness that provides sufficient strength to the model requires further mechanical validation.
After determination of the surface thickness, peripheral surfaces are offset by the system, with respect to divided surfaces, by this distance. The edge-line of the two surfaces generates a band-shaped surface through use the “Sweep 2 rails” command. A solid shell is formed when the two surfaces are connected by means of the sweeped surfaces (Fig. 9(a, b, c)).
Lattice pattern and structure
The system uses a diamond tessellation array to generate the lattice pattern, which—if the model is placed in the vertical orientation—serves to reduce both, the amount of support material consumed and printing time.
In the 2D pattern preview, average lengths of the U and V edges—Ua and Va, respectively—corresponding to the three divided surfaces are used as the width and length, respectively, of a 2D rectangle (Fig. 10(a1, a2)), and offset an inner one with the M margin. After projection, the spacing from the M margin generates an external frame around the lattice structure, and the diamond tessellation pattern is generated within the inner rectangle.
The tessellation pattern is generated by the “Diamond Panel” component in Grasshopper 3D and required inputs—the U and V divisions. The diamond array within the pattern is determined by how the rectangle is divided by oblique lines along U and V directions, and amounts of diamonds are in proportion to Ua and Va of rectangle. In the sample depicted in Fig. 10(a1), U and V divisions are given by Ua/20 and Va/28, respectively. These coefficients are, however, not absolute, and the rule is to reduce the support material generated within the lattice structure during printing. U/V division numbers used in this example were set as five and seven, respectively. Each diamond in the tessellation was offset by the N margin and provided width to the structure truss.
Figure 11(a) depicts the 2D pattern projected onto the inner and outer surfaces of each splint shell. The pattern could be used to cut holes in the lattice. The loft command can be used to create green surfaces between two-hole edges, as depicted in Fig. 11(b). Finally, all remaining surfaces, including lofted ones, could be joined together to form a solid latticed shell (Fig. 11(c)). If the system fails to engrave the shell, values of parameters U and V in the divided area could be decreased to enlarge pattern holes, thereby avoiding generation of tiny holes that cause operation failure.
Rounded-edge and screw-seat generation
For the round edge, two tubes were developed along the U direction edge of the splint surface through use of the “Sweep” command (Fig. 12(a)). Two isocurves of edge surfaces on the U direction of each splint were extracted to serve as the sweep path, and the main segment of isocurves are extracted with the curve domain 0.01–0.99 (Fig. 12(a1 and a2)). Perpendicular planes at the ends of the edge lines were simultaneously defined. Two circles were drawn on these planes, with diameters roughly large than the splint thickness about 1.5 mm, to serve as tube cross-sections (Fig. 12(a3)).
The splint was assembled by fastening several M3 screws. Several planes are set for placing the screw seats to precise positions on edge surfaces of V direction, The screw seat plane was duplicated from the original position onto two points on isocurves with parametric position 0.1 and 0.9 on each V edge surface, as depicted in Fig. 12(b1, b2). If the V-edge length exceeded 180 mm, an extra screw seat plane was added at midpoints along V edges.
An embedded model of the M3 screw seat, part O and I were installed on the XY plane (Fig. 13(a1 and a2)) and duplicated onto the planes. The screw seat was created via Boolean subtraction of two parts, wherein the part I was subtracted from the part O, thereby creating space for containing screw nuts. The screw sets work by constraining nuts, since the screw threads were too tiny to be printed in the prototype. Finally, each splint shell was combined with two tubes and 4–6 screw seats through use of the Boolean union and difference commands (Fig. 13(b)).
The entire splint-modeling process of the system can be described by means of the above steps. The proposed system can, therefore, assist the clinician in generating a feasible splint model within few minutes. However, system failures are still possible. As such, it is important to explain to the clinician the operating principles of the system and remedial measures to be employed during different stages in the event of a system failure.
Mechanical strength testing
Splint fit investigation
Time required for splint-model generation
Time required for splint-model generation during each stage of five samples
Required time (seconds) of calculation in each stage
Offset solid shell
Round edge & screw seats
Lost-mesh fixing performance
Fabrication data statistics, including the printing time, weight, and height of all the splint components calculated by the slicer software
build time (h:m)
weight ( g )
height ( mm )
build time ( h : m )
weight ( g )
height ( mm )
Statistics result of marked times on each splint area based on the marked positions on questionnaires. The discomfort feedback of “Critical” is marked as ●, and “Slight” is marked as ○. Feedback of “Comfort” remained as blank. No critical discomfort was reported
Based on the proposed approach and subsequent verifications preformed in this study, a detailed description of the performance has been provided as well as related limitations have been discussed and addressed.
With currently available scanning technologies and limited compliance of the patient in terms of maintaining the required posture during scanning, presence of holes and deformations within the limb model is a major problem. The proposed system has the potential to remedy these flaws through use of a simple programmable modeling technique sans use of high-accuracy scanners and complex post-production procedures. The clinician can, therefore, ignore the presence of small holes that occur during the scanning process, thereby finishing the task much faster. With shorter scanning durations, patient trembling can be avoided. The proposed digital design technique simplifies the scanning process for clinicians and eliminates the need for them to learn additional post-production tools. However, the effect of the hole-filling function depends on the scale of gaps formed on unclosed cross-sections. If the gaps are large or if certain detailed features are located at these gaps, the command to fix concerned cross-sections via interpolation cannot be used, and the original shape cannot be accurately recreated. Indeed, clinicians must be aware of such a possibility.
In addition, computed tomography (CT), which captures medical images in the digital imaging and communication (DICOM) format has, at times, been utilized in fracture treatments—to facilitate further investigations in cases concerning hard-tissue traumas—and can serve as an alternate source for obtaining accurate external 3D surfaces of injured limbs sans the defects involved in the original 3D-scanning process. Through use of medical-image-viewer software, DICOM files could be transformed into STL models and provided as input to the proposed system for splint design, with the limb model appearing as a single object in the STL file to satisfy input requirements. However, CT imaging involves higher costs compared to 3D optical scanning and prescription necessities depend on the clinician’s diagnosis.
Rapid design and training
Most CAD software are designed for creation of endless geometric diversities; however, splint design is a regular task involving generation of a model that exhibits specific features. Based on extant studies [3, 14], massive manual-modeling steps involved in the splint design and initial scanning tasks generally require up to 20 min to 3 h of completion time, which through use of the proposed technique, can be reduced to approximately 2–3 min.
The proposed programmable modeling tool is a key factor enabling the system designer to reduce the aforementioned manual operations in CAD to a few steps. The said tool also automatically performs required computations, thereby directly providing parameters of interest to the clinician, so that the splint design process could be completed within few minutes without the need for complex operations and/or training. The division-control method enables the system to effectively perform computations and allows clinicians to monitor splint generation at each stage. Basic viewport navigation, curve drawing, and parameter modification are the only skills required on part of the clinician, and these can be taught and/or memorized within 20 min. However, if the scanned model itself is of a poor quality, the clinician is more likely to obtain invalid results. This, in turn, would require more time to test different parameters and input curves. Nevertheless, if the solution fails, the 3D-scanning process can be repeated.
Presently, the printing process occupies the most time involved in the overall design and production of 3D-printed splints—approxiamtely10 h, in general. The proposed study, demonstrates the possibility of reducing the printing time to a few hours through use of the modeling technique, wherein the system divides the splint into two or three compassable pieces and simultaneously generates all parts using multiple 3D printers. The volume of support material utilized during printing can be economized via optimum model placement on the printing bed. Additionally, the structural thickness can be adjusted in accordance with the splint, area and corresponding computation can be embedded within the system. Surface areas of the 10 splints listed in Table 1 were automatically generated by the system after due calculation of their thicknesses and lattice-structure densities. However, attainment of the required splint strength requires further mechanical validation for revising parametric calculations concerning the thickness and lattice pattern.
Swelling acclimation and comfort
Although FEA simulations confirm attainment of the minimum strength required during splint modeling, and no critical discomfort was detected during fit tests, fracture recovery usually takes several weeks to months. A long-term splint-wearing experiment must, therefore, be performed to accurately assess material stability and changes of patient comfort. Such experiments are intended to be performed as part of further research. Patient skin perspiration, limb compression, and occurrence of minor accidents during treatment may cause the splint material to age or wear and even partially break. In view of these, regular follow-up and replacement of splint parts on a monthly or biweekly is highly recommended.
Design openness and extended applications
This study proposes a programmable modeling tool for splint customization to overcome scanning- and modeling-process-related problems encountered during a digitized process. For designers and engineers interested in the development of similar systems, the study demonstrates the exact step-by-step building process and describes the necessary modeling logic, possible issues caused by scanning flaws, and corresponding solutions. A comprehensive discussion of the calculation process enables one to realize how the system determines splint thickness, lattice-structure pattern, and assembly method to response to requirements of different limbs wjilst reducing the overall process duration. The study also facilitates clinicians to accomplish splint designs within few minutes through use of the semi-automatic tool without the need for prior CAD knowledge and/or post-production skills. Although the proposed method reduces the duration of 3D-scanning, CAD manipulation, and printing stages to a few hours, the total duration of the design process still exceeds that of transitional splinting, which can be accomplished within 20 min. Therefore, design-development and generation of simple prefabricated splints must be considered for providing immediate and temporary immobilization before 3D-printed splints could be made available.
The work described in this paper was supported by the Doctorate Student Grant-in-Aid Program (Graduate School Recommendation, Shannon Fujisawa Campus) 2017 of the Keio University, Japan.
Availability of data and materials
Clinicians, medical engineers, and designers are welcome to contact the corresponding author to obtain the program.
JL: Sample correction, parametric modeling, and manuscript revision. HT: Study design, methodology, draft preparation, and manuscript revision. Both authors have thoroughly read and approve of the final draft of the manuscript.
The authors declare that they have no competing interests.
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