Paxton 3 D Printing in Medicine (2023) 9:9 3D Printing in Medicine https://doi.org/10.1186/s41205-023-00175-x RESEARCH Open Access Navigating the intersection of 3D printing, software regulation and quality control for point-of-care manufacturing of personalized anatomical models Naomi C. Paxton1* Abstract 3D printing technology has become increasingly popular in healthcare settings, with applications of 3D printed anatomical models ranging from diagnostics and surgical planning to patient education. However, as the use of 3D printed anatomical models becomes more widespread, there is a growing need for regulation and quality control to ensure their accuracy and safety. This literature review examines the current state of 3D printing in hospitals and FDA regulation process for software intended for use in producing 3D printed models and provides for the first time a comprehensive list of approved software platforms alongside the 3D printers that have been validated with each for producing 3D printed anatomical models. The process for verification and validation of these 3D printed products, as well as the potential for inaccuracy in these models, is discussed, including methods for testing accuracy, limits, and standards for accuracy testing. This article emphasizes the importance of regulation and quality control in the use of 3D printing technology in healthcare, the need for clear guidelines and standards for both the software and the printed products to ensure the safety and accuracy of 3D printed anatomical models, and the opportunity to expand the library of regulated 3D printers. Background to 3D Printing anatomical models manufacturing paradigm unlocks tremendous design 3D printing, more accurately known as additive manu- freedom and makes the technology ideally suited for fab- facturing, is playing an increasingly disruptive role in ricating patient-specific anatomic models or devices that healthcare [1]. Broadly speaking, the fabrication tech- typically entail complex geometries. 3D printing software nology uniquely lends itself to the clinical need to fab- often requires a CAD model as the input, which is ‘sliced’ ricate one-off products matching individual patient into 2D layers and sequentially printed to form the 3D anatomy, and does not require high volumes to break- object [3]. even as per traditional manufacturing [2]. 3D printing Over the last decade, 3D printing is being increasingly techniques rely on the additive deposition or fusion of used for fabricating 3D models of patient anatomy, pro- material, layer-by-layer, to form 3D objects. This additive viding an added dimension to medical scan data visuali- zation previously unachievable at the point-of-care using screen-based visualization technologies [4]. Advances in *Correspondence: accessible 3D printing technology, in parallel to data han- Naomi C. Paxton npaxton@uoregon.edu dling and integrated storage systems, known in health- 1 Phil & Penny Knight Campus for Accelerating Scientific Impact, care settings as ‘picture archiving and communication University of Oregon, Eugene, OR, USA systems’ (PACS), are enabling  hospitals and healthcare © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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Paxton 3 D Printing in Medicine (2023) 9:9 Page 2 of 12 facilities to now rapidly translate imaging data out of the planning models”, “anatomic models”, “medical models” digital domain and into the physical domain (Fig. 1) [5]. or, common to regulatory information, “physical repli- To produce a 3D printed model from patient scan data, cas of 3D models” referring to the physical production one must first obtain the scan data in a compatible for- of models from digital 3D models generated using 3D mat, such as a DICOM file, generated as the output view- modelling software [3, 10–12]. In this article, “3D printed ing format from a variety of medical imaging techniques, anatomical models” has been adopted as a general and such as computed tomography (CT) or magnetic reso- universally inclusive term for these models, regardless of nance imaging (MRI) (Fig. 1, SCAN) [6]. Next, the scan application or intended use. data must be digitally segmented, which involves isolat- Due to their use in healthcare, with the opportunity to ing and extracting the relevant anatomy from the rest inform patient diagnosis, management, or treatment as of the scan data and background. This can be done by diagnostic tool, these 3D printed anatomical models are manually selecting the regions of interest on successive of interest to regulatory bodies such as the US Food and images, or through the use of automated algorithms or Drug Administration (FDA). Currently, whilst 3D printed artificial intelligence (AI) driven tools that can extrapo- anatomical models  prepared at the point-of-care are lated between multiple slices with a high degree of accu- not currently considered medical devices themselves, the racy [7, 8]. Frequently, segmentation is performed using a FDA has required that any 3D printed anatomical mod- combination of automated and manual tools (semi-auto- els marketed for diagnostic use, meaning those advertised matic). Once the relevant anatomy has been isolated, it for sale for the purposes of being used by a healthcare can be processed and converted into a format that can be professional to diagnose a condition, should be prepared used by a 3D printer, typically an STL or OBJ file (Fig. 1, using software that has received FDA clearance [11]. MODEL). Finally, the 3D printer can be used to fabri- Therefore, only a limited number of software platforms cate the physical model using a variety of materials, most exist that have suitable clearance for the generation of typically plastics fabricated via stereolithography (SLA), anatomical models that can be produced in combination fused filament fabrication (FFF) or binder jetting (BJ) with validated 3D printers. Whilst the intended use of the due to low cost and accessibility in standard lab settings software to produce physical replicas for diagnostic use (Fig. 1, PRINT) [9]. is contained within a software’s 510(k) clearance docu- 3D printed models of regions of patient anatomy have mentation, there is no consolidated reporting mechanism many, often interchangeable names, such as “surgical for the specific combination of 3D printers and materials Fig. 1 Overview of the process to design and fabricate 3D printed anatomical models, including acquisition of patient scan data in the form of DICOM, segmentation of the anatomy of interest, 3D modelling of the anatomy and CAD, 3D printing of a physical part and post-processing to clean, cure or remove support structures as necessary. Validation between specific outputs during the workflow is used to confirm the accuracy of specific processes P axton 3 D Printing in Medicine (2023) 9:9 Page 3 of 12 that have been validated using that software and details de novo applications. The requirement for new software are sparsely reported by individual software or 3D printer platforms to be subjected to some form of regulatory manufacturers. Further, this list of cleared printers and oversight is important because the use of 3D printed ana- materials in combination with the segmentation software tomical models produced from digital 3D models gener- is often developed for specific clinical indications and/or ated using these software platforms can have significant anatomic regions. This information is vital to healthcare consequences for patient health and treatment if used for professionals seeking to adopt 3D printing into surgical diagnosis or surgical decision making, and it is important planning workflows and expand the accessibility of 3D to ensure that they are produced reliably and accurately. printed anatomical models to improve patient care. The current absence of a consolidated list containing FDA‑Cleared software for producing 3D printed models information on cleared software and validated 3D printer Currently, there are seven software platforms on the combinations impairs accessibility and understand- market that have FDA clearance for producing 3D ing of the landscape of 3D printing workflows suitable printed anatomical models. Table  1 summarizes these for clinical use. Therefore, the aim of this review article software platforms, with reference to FDA clear- is to comprehensively survey software platforms that ance  documentation provided in Reference column. have been cleared by the FDA for the production of 3D Each of these software include the generation of 3D printed anatomical models, alongside the range of 3D printed anatomical models within their ‘intended use’ printers that have been validated for use to produce 3D in combination with specific 3D printer brands, listed printed anatomical models for diagnostic use. Addition- in column 3. 3D printed anatomical models produced ally, this review aims to examine the suitability of current using five of the software platforms have been cleared verification and validation methodology for the genera- for diagnostic use “in conjunction with other diagnostic tion of such models, as well as to explore the potential for tools and expert clinical judgement” [14] for a range of expanding the range of 3D printers that are validated for clinical applications, namely orthopaedics (also referred use with approved software. to as musculoskeletal), craniomaxillofacial (incl. crani- ofacial and maxillofacial), and cardiovascular areas. Medical device regulation for 3D modelling However, AVIEW Modeler (Coreline Software Com- software pany) and Simpleware ScanIP (Synopsis) may only be US Software regulation for radiological software used for “visualization and educational purposes” and Like many software platforms used in healthcare, 3D do not currently  possess clearance for diagnostic use. modelling software used to translate patient scan data This means the models cannot be used by a healthcare into 3D models suitable for 3D printing is regulated by professional to diagnose a patients’ condition based on the FDA if they are intended to be used for diagnostic or the 3D printed model, however they may still be used for therapeutic purposes [13]. Given the similarities in func- other activities within a healthcare setting such as surgi- tionality to generic radiographic software, both types of cal training and patient education [15, 16]. software are used to create visual representations of med- Materialise products (Mimics, Mimics InPrint and ical data that can be used for diagnostic or therapeutic Mimics Medical) have played a critical role in establish- purposes, and as such, they have the potential to signifi- ing a benchmark for the safety and efficacy of these soft- cantly impact patient health and treatment. In terms of ware platforms, with all other software platforms using their risk profile, radiographic software, as well as those a Materialise product as either a predicate or reference with 3D printing-specific outputs, are generally classi- device for comparison of their safety and performance, fied as moderate risk (Class II) medical devices with in and assessment of substantial equivalence (Fig. 2). Their the ‘LLZ’ classification product code, depending on their 3D visualisation technology is underpinned by their plat- intended use and the potential for harm if they do not form 3D image viewing and surgical planning software function correctly. This process typically involves submit- developed in the 1990s for dental surgery applications. ting a premarket notification, also known as a 510(k), to SIMPLANT remains in routine clinical use for dental the FDA, which includes data demonstrating the safety surgery planning and surgical guide design after being and effectiveness of the software compared to an exist- acquired by a US dental equipment manufacturer, Dent- ing product on the market, known as a ‘predicate’. The sply Sirona [27]. FDA reviews this data and determines whether the soft- The selection of validated 3D printers has largely been ware meets the necessary standards and can be cleared established through partnerships between software and for sale. Alternatively, if a product has new features for 3D printing hardware manufacturers [21, 25], leading to which there is no predicate device already on the mar- a bespoke list of 3D printers being available for use in a ket, other application pathways may be required, such as validated and ‘on-label’ context. This list of 3D printers Paxton 3 D Printing in Medicine (2023) 9:9 Page 4 of 12 Table 1 List of 3D modelling with the intended use of producing 3D printed anatomical models, cleared by the FDA (Class II 510(k) pathway) for specific clinical applications: Orthopaedic (Ortho), Craniomaxillofacial (CMF), Gastrointestinal (GI), Genitourinary (GU), and Neurological (Neuro) Company Software Validated with Intended for Intended Use Applications Validation Reference Specific 3D Printers Diagnostic Use Accuracy Ortho CMF Cardiovascular GI GU Neuro Materialise Mimics Formlabs FORM Yes ✓ ✓ ✓ < 0.2 mm [14, 17] Medical 3/3B/3BL Mimics Stratasys J5 MEDIJET, InPrint J750/ J735/J850, OBJET30 PRIME HP MJF 580 Ultimaker S5 Synopsis Simpleware Formlabs FORM No Not reported [18] ScanIP 3B/3BL Stratasys J5 MEDIJET, J750/J850 HP MJF 580 Rize XRIZE 3D Systems D2P 3D Systems ProJet Yes ✓ ✓ ✓ ✓ ✓ ✓ Not reported [19, 20] CKP 660Pro 3D Systems ProJet 7000 HD 3D Systems ProJet MJP 5600 3D Systems ProX SLS 6100 Ricoh Ricoh 3D Stratasys J5 MEDIJET, Yes ✓ ✓ Not reported [21, 22] Anatomical J750 Models Axial 3D Axial3D Formlabs FORM 3B Yes ✓ ✓ ✓ Not reported [23, 24] Cloud Seg- Stratasys J5 MEDIJET, mentation J750/J735 Service HP MJF 580, 540 Coreline AVIEW Mod- Stratasys Objet260 No Not reported [15] Software eler Connex3 Company Medviso Segment Formlabs FORM 3B + Yes ✓ ✓ ✓ < 1 mm [25, 26] 3DPrint Formlabs Fuse 1 P axton 3 D Printing in Medicine (2023) 9:9 Page 5 of 12 Fig. 2 Timeline of 510(k) clearance for medical imaging software for producing 3D printed anatomical models. The company name, software name and 510(k) number are provided on a timeline, as well as arrows indicating a software application’s references to other software as a predicate or reference device in their 510(k) application introduced in Table  1 has been expanded and reorgan- extend beyond the scope of the aforementioned indica- ized in Table 2 to further explore trends in the growing tions for use for anatomical models. selection, fabrication modalities and material availability. In addition to the seven software platforms mentioned FormLabs and Stratasys are the most widely validated in Table  1, there are other programs that have similar 3D printer brands, with their vat polymerization (VP) capabilities for converting patient scan data into digital 3D and material jetting (MJ) technology being marketed models that can be used for 3D printing. However, these and applied widely for their capacity to produce accu- software platforms do not specifically describe the physi- rate, flexible, multicoloured, or multi-component ana- cal fabrication of models as an intended use of the soft- tomical models [28–30]. Whilst the mean cost for one ware in their FDA clearance documentation (Table S1). of the printers on the list is just under $100,000 USD These platforms include Advantage Workstation (AW) ($98,612.50 USD, n = 16), several low-cost 3D printers (GE Heathcare), that has been validated with Formlabs are available, including the Ultimate S5 fused filament FORM 3B and 3BL printers [36], and Vitrea Advanced fabrication (FFF) system for use with PLA within the cat- Visualization (Canon), validated with Stratasys Objet260 egory of material extrusion (MEX) which, importantly, Connex3. IntelliSpace Portal 10 (Philips) and Synapse 3D does not require any peripheral post-processing materials (FUJIFILM) both market their software with 3D print- necessary for VP fabrication [30]. However, variation in ing output capability [37, 38], whilst Dolphin 3D Surgery the surface quality and material finish of each technique (Patterson Dental Supply), iNtuition (TeraRecon), Osirix may render some techniques more suitable that others in MD (Pixmeo Sarl) and Syngo.via (Siemens) have demon- addition to the accessibility of the price point. Intuitively, strated use for producing 3D printed anatomical models as 3D Systems is the only company to appear on both the in the academic literature [39–43] (Table S1). list of software manufacturers and 3D printer manufac- It is also necessary to distinguish between 3D printed turers, they have exclusively validated their D2P software anatomical models produced by a manufacturer for sale with several of their 3D printers [19]. Several printers in the US, compared to those produced in-house by a on the list,  including the FormLabs Fuse 1, HP580, 540, hospital or other healthcare provider that are not mar- and, 3D Systems ProX SLS 6100, are capable of fabricat- keted and sold. FDA regulation currently extends only ing parts from nylon (PA11 or PA12) which is commonly to products produced for marketing and sale in the US used as a biocompatible material for tissue-interfacing and therefore, whilst it is best practice for hospitals pro- applications such as surgical guides [31], however the ducing 3D printed anatomical models to follow the FDA regulatory complexities for producing such surgical tools guidance requiring 3D printed anatomical models to Paxton 3 D Printing in Medicine (2023) 9:9 Page 6 of 12 Table 2 List of validated 3D printers for the production of anatomical models using FDA-cleared software and estimates of their price points. (Binder Jetting (BJ), Material Extrusion (MEX), Material Jetting (MJ), Powder Bed Fusion (PBF), Vat Polymerization (VP)) Company 3D Printer Typea Approx. Materialise Simpleware ScanIP D2P Ricoh 3D Axial3D Cloud AVIEW Modeler Segment 3DPrint Printer Mimics Medical & Anatomical Segmentation Costb [USD] InPrint Models Service FormLabs FORM 3 VP $3.5 k ✓ (a) ✓ (b) FORM 3B VP $4.3 k ✓ (a) ✓ (a) FORM 3B + VP $3.8 k ✓ (q) FORM 3BL VP $13 k ✓ (a) ✓ (a) FUSE 1 PBF $18.5 k ✓ (p) Stratasys J5 MEDIJET MJ $60 k ✓ (c,e,g,h) ✓ (d,e,g,h) ✓ (q) ✓ (c,e,g,h) J750 MJ $250 k ✓ (d,f ) ✓ (d,f,g,h) ✓ (q) ✓ (d,f ) J735 MJ $200 k ✓ (d,f ) ✓ (d,f ) J850 MJ $200 k ✓ (d,f ) ✓ (d,f,g,h) OBJET30 PRIME MJ $36 k ✓ (g) OBJET260 Connex3 MJ $110 k ✓ (n) HP MJF 580 PBF $110 k ✓ (i) ✓ (i) ✓ (i) MJF 540 PBF ✓ (i) Ultimaker S5 MEX $6 k ✓ (j) Rize XRIZE MJ + MEX $55 k ✓ (k) 3D Systems ProJet CJP 660Pro BJ $60 k ✓ (l) ProJet 7000 HD VP $100 k ✓ (m) ProJet MJP 5600 MJ $70 k ✓ (n) ProX SLS 6100 PBF $300 k ✓ (o) Validated M aterialsc [17] [16] [19] [11] [23] [15] [32, 33] a According to terminology defined in ISO/ASTM 52,900 Standard Terminology for Additive Manufacturing – General Principles – Terminology [34] b Approximate starting price point based on public information [35] c Each software platform has validated each printer with various combinations of materials. References are provided to the specific documentation to find the specific materials that have undergone validation testing (a) FormLabs v4: Grey, Clear & White. (b) FormLabs v4: White, Clear; FL v2: Draft; FormLabs v1 Flexible. (c) VeroVivid™ Cyan, Magenta, Yellow; DraftWhite, VeroUltraClear™. (d) VeroBlackPlus, VeroClear, VeroCyan, VeroGrey, VeroMagenta, VeroPureWhite, VeroYellow. (e) Elastico™ Clear. (f ) Agilus. (g) MED610. (h) MED615RGD. (i) Nylon 12: 3D HR CB PA 12. (j) PLA. (k) Rizium GF. (l) VisiJet® PXL™ + ColorBond™ infiltrant. (m) Accura® ClearVue™. (n) VisiJet CR-WT 200, VisiJet CE-NT. (o) DuraForm® ProX PA. (p) Nylon 11 Powder. (q) Not specified P axton 3 D Printing in Medicine (2023) 9:9 Page 7 of 12 be produced using cleared software, it is not presently each stage of the 3D printed anatomical model genera- a requirement. This nuanced guidance from the FDA tion workflow (Fig. 1) requires careful analysis to deter- is likely to undergo significant change over the com- mine the presence of controlled or uncontrolled sources ing years as the role of medical device manufacturer is of inaccuracy and therefore motivation for regulatory clarified in the context of the growing trend and return oversight. towards point-of-care manufacturing [44]. Thought Firstly, the image quality generated from CT and MRI leaders in the 3D printing for medical application space scanning modalities is largely well-characterised, how- strongly advocate for the use of approved software cou- ever the impact of imaging quality and parameters such pled with validated 3D printers in the interests of main- as the choice of reconstruction kernel or slice recon- taining “very high standards” and minimizing risk to struction interval (SRI) have been shown to impact the patient safety [45]. mean absolute error between original models and 3D printed models [47]. Next, the digital process steps have 3D Printed product validation the potential to introduce inaccuracy in the model design Inaccuracies in model design & fabrication and interpretation of anatomical structures, particu- Reproducible dimensional accuracy is crucial for qual- larly when performed by non-experts [48, 49]. Figure  3 ity control of 3D printed anatomical models, particularly demonstrates the source of estimation and inaccuracy since they may be used to inform diagnosis and surgical between the original CT scan data of a femur versus decision-making that may impact patient safety and qual- the segmentation selection, ‘part’ and exported STL file. ity of care. Since these models are not considered medi- Whilst little difference is perceivable in the macroscopic cal devices, no harmonized quality control standards views of the 3D models, at high magnification, the inter- currently exist. Research teams and 3D printing facilities pretation of the segmented pixel selection into a part and around the world have therefore developed and reported STL file yields a potential source of inaccuracy between a variety of quality management methods, focusing on the patient anatomy and produced model (Fig. 3). Several establishing reproducible dimensional accuracy of 3D CAD tools are commonly used to prepare the part for printed parts. Dimensional accuracy is defined as the final production, including the use of ‘wrap’ tools to close agreement between the measured and designed dimen- small holes in the 3D model, or mesh reduction to reduce sion of the 3D-printed part [46], and has vital clinical and improve the quality of triangles comprising the STL relevance for the quantitative use of these 3D models for model. These tools, in combination with the vast range of characterising pathologies, such as tumours, aneurysms adaptation and manipulation tools available in CAD soft- or other pathologies where dimensional fidelity strongly ware such as 3-matic (Materialise) may impact the qual- determines treatment pathway and prognosis. Therefore, ity and accuracy of the 3D model compared to the patient Fig. 3 Comparison of 3D model morphology of a femur at high magnification. CT scan data (greyscale) was segmented (red) in Mimics (Materialise), converted to a ‘part (green) and exported to an STL file (blue) after ‘wrapping’ and floating body removal Paxton 3 D Printing in Medicine (2023) 9:9 Page 8 of 12 anatomy and original scan data. This is consistent with evaluate the accuracy of anatomic models across a more previous reports demonstrating that different segmenta- generalised range of anatomical regions [46]. tion and part generation algorithms produce models with statistically significant variation in physical dimensions Validation & quality control methods [50, 51]. This also further reinforces the accepted stand- Whilst formalised quality control systems for 3D printing ard of practice for point-of-care 3D printing facilities to anatomical models in hospital have not yet been man- use software platforms cleared by the FDA in combina- dated by the FDA, several methodologies have been pro- tion with validated 3D printers, since critical inaccuracies posed in the academic literature, ranging from versatile could step from several aspects of the workflow when guidance for routine manufacturing workflows, through using non-cleared and validated products, particularly to systematic academic studies reporting vital fundamen- when performed by non-radiologists, such as 3D printing tal validation where the true anatomical accuracy has technicians that do not have formal medical training. been directly measured from cadaveric samples [42, 52]. Finally, dimensional accuracy of the final 3D printed Since the true patient anatomy is rarely accessible dur- models may be evaluated using a range of technolo- ing routine clinical cases, the DICOM scan data is widely gies, including callipers, photographic measurements, accepted as the ground truth, to which the STL file and surface scanning, photogrammetry, coordinate meas- 3D printed part are compared (Fig. 1). Comparison of the uring machines (CMMs), or CT scans, summarised in DICOM file to STL file provides validation information Fig. 4 [42]. Many studies evaluating accuracy focus on a on the accuracy of the segmentation and CAD processes, single pathology or region of anatomy [9, 42, 47], and it validating the software tools used to generate the digital has been highlighted that further research is needed to 3D model. This validation is included in the validation Fig. 4 Summary of accuracy measurement techniques for validating the fabrication of 3D printed anatomical models. Linear measurements of anatomical features may be taken from a 3D scan of the 3D printed model or the physical model itself (blue) [42, 46, 52, 53], whilst optical or laser surface scanning allows 2D surface comparisons between anatomical features in the original scan data, STL file and physical model (green) [9, 42, 54, 55]. Finally, a ‘residual volume’ metric is proposed for 3D quantification of model accuracy (pink) [56] P axton 3 D Printing in Medicine (2023) 9:9 Page 9 of 12 and verification testing performed by FDA-cleared soft- physical part play a vital role in process establishment, ware platforms listed in Table 1 and validates the suitabil- enabling comparison from digital scan data of the printed ity of these platforms to accurately translate the 3D scan product compared to segmentation and STL data. These data into 3D models. At this stage, radiologist oversight is techniques are comprehensive and enable accuracy char- recommended to ensure the quality of the digital model acterisation of the accuracy of all features of the part, [57]. Next, the STL file is 3D printed to generate the notably thin internal features that may be inaccessible physical model, the accuracy of which compared to the for physical measurement. However, their role in routine STL file is intrinsic to the 3D printer itself, the material, quality control may be limited due to cost and time inef- the paired slicing software, printing mechanism, upkeep ficiency compared to physical measurements with calli- and maintenance, and may not be specific to the design pers [56]. being printed. This should be independently and rou- tinely validated using standardized models using manu- Conclusion & future directions facturer-specific guidance [58]. Full process validation is 3D Printed anatomical models driving hospital‑based therefore critical, ensuring that the final printed prod- manufacturing uct is within an acceptable tolerance from the original As technology and the technological competency of DICOM data (Fig. 1). healthcare providers for producing 3D printed anatomi- Since the DICOM file (sliced 2D images), STL file cal models continue to advance, it is likely that FDA guid- (3D digital model) and final printed part (3D physi- ance will evolve to reflect these changes. The FDA may cal model) exist in different spatial as well as physical consider several dynamic factors when updating its guid- or digital domains, several metrics for comparison have ance in the coming years, including the development of been utilized: 1D linear measurements, 2D surface meas- new applications, validation techniques, feedback from urements, and 3D volumetric measurements (Fig.  4). key stakeholders, such as surgeons, 3D printing experts Measurements on the final 3D printed part may be per- and patient groups, as well as changes in the interna- formed directly, in the case of linear measurements using tional regulatory landscape. This is particularly pertinent callipers, or via re-visualization of the part using 3D sur- given the proximity of the technologies underpinning 3D face scanning, such as optical, photogrammetry or laser printed anatomical model manufacturing to those capa- scanning, or CT scanning, offering a continuum of spa- ble of producing other personalised medical devices and tial information at a variety of resolutions depending on equipment that fall under medical device manufacturing the specific equipment used [59]. regulation. Industry leaders have widely supported the use of cal- The growing demand for personalised medical devices lipers to perform linear measurements directly on 3D such as surgical implants has strongly driven the require- printed outputs compared to digital linear measurements ment for point-of-care manufacturing, both to minimize performed on the DICOM and STL files for routine qual- lead times for manufacturing personalized devices, as ity control [46, 60]. These measurements are routinely well as cybersecurity concerns to reduce data-sharing performed on macroscopic dimensions of large compo- with third parties outside of the healthcare providers’ nents or wall thicknesses of hollow or tubular structures. systems in the process of designing and manufacturing These measurements may be compared to the STL file or personalized devices. These new challenges intrinsic to original DICOM dataset, as shown in Fig. 1, with a tol- the technological capability offered by 3D printing for erance of < 1 mm deviation between physical model and producing personalized devices are stimulating a grow- original data widely considered to be acceptable in the lit- ing conversation within regulatory bodies to reconsider erature for diagnostic models [9, 46, 61]. However, such how healthcare providers can also act as medical device measurements on specific anatomical features of per- manufacturers. sonalized models cannot be readily compared between cases. Therefore, the inclusion of standardized ‘landing Availability of 3D printers blocks’ of a specific dimension added into the 3D model Beyond regulatory considerations, the availability of 3D has been proposed by Ravi et al. (2022) to enable repro- printers that have been validated for use in conjunction ducible and comparable measurements between models with cleared 3D modelling software remains limited, as of varying geometry and clinical application [46]. The tol- demonstrated in Tables 1 and   2. Only a small subset of erance threshold is much higher for devices such as ana- the available types of 3D printing techniques are repre- tomic guides that have to fit on the target bony anatomy sented in the list of validated printers, as well as an even compared to anatomic models used for diagnostic pur- smaller cohort of the thousands of brands and models poses. Other more comprehensive techniques such as of 3D printers on the market capable of producing 3D surface and volume measurements based on scans of the printed anatomical models are validated and marketed Paxton 3 D Printing in Medicine (2023) 9:9 Page 10 of 12 for use in producing anatomical models. Strategic part- manufacturer [73], this poses an insightful estimate into nerships between software providers and 3D printer the feasible cost of routinely produced 3D printed ana- manufacturers have motivated the validation of specific tomical models based on the ability for the VHA to pro- printers with software platforms [16, 17, 62], however in duce and bill for these models in-house. However, the the absence of validation testing methods used by these comprehensive costs associated with producing anatomic providers and manufacturers in the public domain, the models maybe substantially higher as demonstrated list of available printers may remain limited. The preva- in a recent study where the average cost of producing lence of expensive (> $100,000 USD) printing equipment, anatomic models across 11 clinical indications at the with disproportionately few low cost options with respect point-of-care was $2180 and $2467 when outsourced to to the range available on the market is a limiting factor industry [74]. for the acceleration of 3D printing facility establishment Ultimately, further research, validation testing meth- in hospitals, despite low-cost models having similar clini- ods and regulatory oversight will accelerate the avail- cal relevance than those produced on high-cost equip- ability of validated and cleared workflows for producing ment [63–65]. personalized surgical planning models for point-of-care manufacturing, propelling 3D printed anatomical models Reimbursement & economics into routine clinical use. This article has sought to pro- Finally, a parallel challenge to accelerating the adoption vide a consolidated summary of FDA-cleared software of 3D printed anatomical models, in addition to regula- platforms specifically suited towards the generation of tory and technological considerations, is the economical 3D printed anatomical models, as well as the 3D printing proposal. This has recently been the topic of an excellent models currently validated for use with the FDA-cleared editorial by Prof Frank Rybicki (University of Cincinnati) software. The sources of inaccuracy contributing to the who examines the intersection of regulation and reim- risk profile of using non-cleared software and hardware bursement in the current landscape of hospital-based combinations are also discussed, finally summarizing the manufacturing [57]. In July 2019, the American Medical currently accepted techniques for validating the entire Association (AMA) defined four new Current Proce- scan-to-print pathway, alongside specific aspects of the dural Terminology (CPT®) codes relating to 3D printed manufacturing process to produce 3D printed anatomical anatomical models and surgical tools. CPT® codes are a models. This resource therefore seeks to enable further “uniform language for coding medical services and pro- adoption of safe and effective point-of-case 3D printing cedures to streamline reporting” [66], and the inclusion for surgical planning models and expand their application of specific codes relating to 3D printed models and guides towards routine adoption in healthcare settings globally. presents and exciting step forward towards routine adop- tion and use of 3D printed models in healthcare settings. Supplementary Information Specifically relating to 3D printed anatomical models, The online version contains supplementary material available at https:// doi. org/ 10. 1186/s 41205-0 23- 00175-x. “codes 0559 T and 0560 T represent reimbursement for the production of individually prepared 3D printed mod- Additional file 1: Figure S1. Decision tree for inclusion criteria for 3D els that can be made from one or more components and modelling and segmentation software into the consolidated list (Table 1) unique colors and materials” and can be used to bill for or supplementary list (Table S1). Table S1. FDA-cleared 3D modelling and segmentation software with the capability to generate STL files suitable the production of these products during patient care [66]. for 3D printing, however 3D printed models as outputs not listed as However, the codes are currently ‘temporary’ Category ‘intended use’ in FDA documentation. III codes and therefore health insurers are not obliged to reimburse for these codes, nor is a specific value assigned Acknowledgements to the code for reimbursement. It is therefore at the dis- The authors kindly thank Professor Paul Dalton (University of Oregon) and Dr cretion of individual health insurers whether they choose Prashanth Ravi (University of Cincinnati, College of Medicine) for their assis-tance in reviewing and editing the manuscript. to reimburse for 3D printed anatomical models and if so, for how much. A survey of the over 300 US health insur- Authors’ contributions ers’ [67] reimbursement schedules suggests that only 15 NCP performed the literature review, prepared figures, and wrote the manu-script. The author(s) read and approved the final manuscript. health insurers currently choose to reimburse for these specific CPT® codes, to an average value of $91.78 US Funding per model (n = 15) [68–70]. The Veterans Health Admin- NCP is supported by the Knight Campus-PeaceHealth Postdoctoral Fellowship Program and acknowledges financial support from the Dalton Lab and the istration reimburses the highest amount of the surveyed Joe and Clara Tsai Human Performance Alliance. insurers, to a maximum of $372.78 US [71]. Coupled with their nationally leading network of on-site 3D printing Availability of data and materialsAll data generated or analysed during this study are included in this published facilities [72], including as a compliant medical device article and its supplementary information files. P axton 3 D Printing in Medicine (2023) 9:9 Page 11 of 12 Declarations 17. Materialise (2023) Mimics Certification Program | Start Your Own Point-of- Care 3D Lab. https://w ww. mater ialise. com/ en/ health care/ hcps/ point- of- Ethics approval and consent to participate care- 3d-p rint ing/m imics- certi ficat ion. Accessed 9 Jan 2023. Deidentified patient data was supplied by PeaceHealth via the Knight Campus 18. U.S. Food and Drug Administration (2015) 510(k) Clearance for ScanIP, – PeaceHealth Center for Translational Biomedical Research (CTBR). The study K142779. was deemed exempt by the University of Oregon IRB (STUDY00000613) and 19. 3D Systems (2023) D2P Regulatory Information. https://w ww.3 dsys tems. PeaceHealth IRB (Project ID 1919704–2). com/d icom- to-p rint/ regul atory. Accessed 9 Jan 2023 20. U.S. Food and Drug Administration (2019) 510(k) Clearance for D2P, Consent for publication K183489. Not applicable. 21. Stratasys (2021) Stratasys Partners With Ricoh to Deliver Point-of-Care Anatomic Modeling Solution. https:// inves tors. strat asys. com/ news- Competing interests events/p ress- relea ses/ detail/7 57/s trata sys-p artne rs- with- ricoh- to-d eliv The authors declare that they have no competing interests. er- point- of-c are. Accessed 9 Jan 2023. 22. 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Ready to submit your research ? Choose BMC and benefit from: https:// forml abs. com/b log/u nders tandi ng-a ccura cy-p reci sion- toler ance- • fast, convenient online submission in- 3d- print ing/. Accessed 15 Jan 2023 59. Paxton NC, Nightingale RC, Woodruff MA. Capturing patient anatomy • thorough peer review by experienced rese archers in your field for designing and manufacturing personalized prostheses. Curr Opin • rapid publication on acceptance Biotechnol. 2022;73:282–9. https:// doi. org/ 10.1 016/j. copbio.2 021. 09. 004. • support for research data, including large and complex data types 60. Wake N, Johnson B, Leng S. Quality Assurance of 3D Printed Anatomic Models. Print Radiol. 2022;3D:89–98. https:// doi.o rg/1 0.1 016/ B978-0-3 23- • gold Open Access which fosters wider collaboration and increased citations 77573-1.0 0003-8. • maximum visibility for your research: over 100M website views per year 61. Brouwers L, Teutelink A, van Tilborg FAJB, et al. Validation study of 3D-printed anatomical models using 2 PLA printers for preoperative At BMC, research is always in progress. planning in trauma surgery, a human cadaver study. Eur J Trauma Emerg Surg. 2019;45:1013–20. https:// doi. org/ 10. 1007/ s00068- 018- 0970-3. Learn more biomedcentral.com/submissions