The research, published in Global Surgical Education, the Journal of the Association for Surgical Education, took a phenomenological approach, interviewing ten orthopaedic surgeons at a training hospital who had each used 3D printed models on between three and twelve cases. The team transcribed in-depth, semi-structured interviews and analysed them inductively using ATLAS.ti software, with three researchers coding independently before comparing and refining themes through an iterative process. Two main themes emerged from the data: the perceived benefits of working with printed models, and surgeons’ attitudes toward the technology itself.

The technical backdrop is important. Traditional pre-operative planning in orthopaedics relies on visual interpretation of X-rays, computed tomography and magnetic resonance imaging scans, essentially flat images that the surgeon must mentally translate into three-dimensional anatomy. Yet orthopaedic surgery is fundamentally a discipline of touch. Surgeons depend on haptic perception, the combination of tactile feedback from contact and kinesthetic feedback about how objects move and respond to pressure. When a surgeon must integrate visual information with an imagined sense of touch, the brain’s working memory, the limited-capacity system that holds and manipulates short-term information, can become overloaded, a phenomenon described in education research as cognitive load.

This is where the printed models appear to make their most profound contribution. Every surgeon in the study reported that holding a physical model of the patient’s anatomy positively influenced their preparation. Most described the model as an adjunct to image interpretation, revealing information that was not clearly visible on scans. Several described literally grasping the pathology, with one spine surgeon noting it was far easier to get to grips with exactly what a deformity involved when holding it in hand. A foot and ankle surgeon even placed a model of a deformed foot on the floor to assess how the forefoot related to the ground, a proxy that directly informed the goal of achieving a plantigrade, flat-standing foot.

Beyond perception, the models enabled genuine rehearsal. Several participants reported physically practising a procedure more than once before surgery, trialling different strategies and implant sizes against the patient’s native anatomy. For others, simply handling the model was enough to finalise a plan, with one upper limb surgeon describing how holding the glenoid allowed a physical model to form in the mind. Three of the ten surgeons also found the models valuable for communicating complex pathology to team members and patients, in every case linked to the difficulty of verbalising three-dimensional deformities in words alone.

The benefits carried into the operating theatre. Surgeons consistently described increased confidence and a sense of déjà vu when the real procedure matched the rehearsed one, with one paediatric orthopaedic surgeon calling the combination of mental and physical rehearsal a game changer. Some explicitly reported decreased cognitive load and reduced stress during operations, alongside perceived decreases in theatre time and increases in surgical accuracy, though the researchers caution these impressions may be subjective. Several surgeons used sterilised models inside the theatre as haptic maps, feeling their way through unusual anatomy when visibility was limited, while others described the models as guard rails that helped them detect unintended deviations from the surgical plan early.

The study also surfaced a compelling idea about how the technology might shift surgical practice. The authors suggest that planning with a printed model moves critical decision-making points from the stressful intra-operative environment into the calmer pre-operative phase, reducing the cognitive load a surgeon carries in theatre. For junior surgeons, those critical moments arise even in routine cases; for veterans, they arise only with unfamiliar, highly complex pathology. Notably, seven of the ten surgeons anticipated shorter theatre times and improved accuracy, yet nobody mentioned reductions in blood loss, fluoroscopic use or hospital stay, the objective outcomes that dominate the quantitative literature, hinting that the technology’s most immediate value may be cognitive rather than physiological.

Attitudes toward the technology were strongly positive and forward-looking. Surgeons frequently discussed how models could be modified to make rehearsal more realistic, such as using dual-material printing with elastic connectors to mimic ligaments holding bone fragments together. Most expected the technology to develop further and expressed intentions to proactively engage with it. Several voiced concern that over-regulation could price medical 3D printing out of everyday practice, arguing that a low-cost, do-it-yourself approach should be protected so that models can be made for almost every patient. Two participants said they would even postpone complex surgeries to allow time for a model to be manufactured, and one regretted not doing exactly that after feeling lost during a difficult case.

The findings also carry implications for surgical training. Participants widely viewed the models as a valuable tool for accelerating competence without patient harm, with one spine surgeon predicting that young surgeons would reach proficiency faster without being de-skilled. Others argued the technology raises the bar for the entire field, expanding the scope of what can be operated on competently in millimetre-and-degree measurable outcomes. The recurring overlap between discussions of training and of experienced surgeons preparing for novel cases underscores, the authors note, that surgical practice is a continuous embodied process punctuated by increasingly complicated challenges.

The study has limitations, including its small sample, coverage of only a few orthopaedic sub-specialties and a single-centre setting in a developing country, and the researchers observed that different sub-specialties use models differently, with spine surgeons favouring haptic maps and arthroplasty surgeons trialling implants. Still, the message is clear: as 3D printing becomes cheaper and more accessible, its perceived benefits among surgeons are overwhelmingly cognitive and educational, moving decision-making earlier, lightening mental load in theatre and potentially compressing the long apprenticeship of surgical experience. The authors call for future research linking these perceived benefits to established models of cognitive load and fatigue, and ultimately to measurable patient outcomes.

Subject of Research: Surgeons' experiences and perceived benefits of using 3D printed anatomical models for orthopaedic pre-operative planning and training

Article Title: Orthopaedic surgeons using 3D printed models to prepare for surgery: a qualitative investigation

Article References: Venter, R. G., Vogel, L., Visser, M., & Rabie, S. (2026). Orthopaedic surgeons using 3D printed models to prepare for surgery: a qualitative investigation. Global Surgical Education – Journal of the Association for Surgical Education, 5(1), Article 180. https://doi.org/10.1007/s44186-026-00587-3

Image Credits: AI Generated

DOI: 10.1007/s44186-026-00587-3

Keywords: 3D printing, orthopaedic surgery, pre-operative planning, surgical training, cognitive load, haptic perception, working memory, anatomical models, qualitative research, patient-specific models, surgical education, Orthopaedic

The research, led by Mauranne Lievens of the Department of Oral & Maxillofacial Surgery at Ghent University Hospital, together with Wim Van Paepegem, Tom Goffin, Geert Villeirs and Renaat Coopman, addresses a long-standing gap in the validation of hospital 3D printing programs. Earlier validation studies typically relied on geometric calibration artifacts, standardized test shapes, or simplified phantom objects—objects that behave well on a build plate but share little with the irregular, thin-walled, undercut-rich anatomy of a human skull. The Ghent team instead designed the study around highly accurate anatomical models, deliberately mimicking the actual workflow a hospital follows when a surgeon requests a patient-specific replica for preoperative planning, implant shaping, or patient counseling.

Accuracy in this context is not a single number but a composite of two distinct properties. The first is trueness, which measures how closely a printed model matches the digital reference design—the deviation between the physical object and the source file that clinicians use. The second is precision, which captures reproducibility: how consistent a printer is when producing the same model repeatedly within a single build (intra-build variability), and how consistent it is across separate print jobs run at different times (inter-build variability). Both dimensions matter clinically. A printer that faithfully reproduces anatomy once but drifts between builds is as problematic as one that is consistently wrong. The team quantified both using root mean square (RMS) error, a standard metric that aggregates surface deviation across the entire model rather than relying on a handful of caliper measurements at convenient points.

The statistical design of the study is one of its most notable contributions. Because the dataset comprised repeated prints of multiple anatomical models at multiple timepoints, the observations were inherently clustered—prints from the same build and models from the same printer are not independent of one another. Conventional statistical tests that ignore this structure can overstate significance. To handle the complexity, the researchers employed a Linear Mixed Model (LMM), a framework widely used in agriculture, ecology and clinical trials but, according to the authors, never before applied in 3D printing accuracy research. The mixed model allowed the team to evaluate fixed effects—printer type, anatomical model, and comparison type—while correctly accounting for the internal clustering of repeated measurements, yielding a statistically robust picture of where true differences lie.

The headline result is that every technology tested achieved accuracy within clinically acceptable limits, an encouraging finding for hospitals that have invested in lower-cost equipment. But the ranking was clear and statistically significant. Material Jetting, which builds objects by jetting tiny droplets of photopolymer in extremely thin layers, achieved a mean RMS error of 67 micrometers—the finest accuracy in the study. Stereolithography, which cures liquid resin layer by layer with a light source, followed at 109 micrometers. Fused Filament Fabrication, the thermoplastic extrusion process behind most affordable desktop printers, came in at 130 micrometers. In practical terms, these differences span roughly the width of one to two human hairs, yet in surgery that margin can matter: when a model is used to pre-bend titanium plates or rehearse the osteotomy of a complex orbital or mandibular reconstruction, sub-millimeter deviations propagate directly to the operating table.

Just as important as the ranking was what the Linear Mixed Model revealed about the drivers of error. Printer type was a significant fixed effect, confirming that the technology choice itself—not random variation—explains much of the accuracy difference. But anatomical complexity also exerted a significant influence, meaning that the shape being printed matters nearly as much as the machine printing it. Models with delicate facial structures, thin bony walls and deep undercuts stressed each technology differently: resin- and jetting-based photopolymer processes handled fine detail more gracefully, while extrusion-based FFF showed characteristic stair-stepping and filament-path limitations on curved, intricate surfaces. This interaction underscores why calibration artifacts alone cannot predict clinical performance; a flat test coupon tells a hospital little about how a printer will cope with a zygomatic arch or a thin orbital floor.

The study’s framing around the Medical Device Regulation gives it particular weight for European hospitals. MDR 2017/745 classifies many in-house manufactured products as devices and requires health institutions to demonstrate that their production, under the responsibility of a single legal manufacturer, meets safety and performance expectations—including verification of accuracy appropriate to the intended clinical use. Point-of-care 3D printing has often grown organically inside surgical departments, with validation practices varying wildly from one hospital to the next. By defining accuracy through trueness and precision, quantifying it with RMS error against digital references, and analyzing it with a defensible statistical model, the Ghent team has effectively published a template for the kind of documented, reproducible validation that regulators and hospital quality officers can build upon.

For hospital administrators weighing procurement decisions, the results offer a nuanced cost-benefit picture. Material Jetting’s superior fidelity comes with higher machine and material costs, and photopolymer processes require resin handling and post-processing protocols. FFF remains the most affordable and accessible option, and the study confirms its output is still clinically acceptable—suggesting it can retain a role for models where extreme fidelity is not essential, such as patient education or approximate planning. SLA sits between the two, offering strong performance at moderate cost. The finding that all three cleared the clinical acceptability bar means the choice can legitimately be driven by the intended application, budget and workflow, rather than by fear that cheaper technology is automatically unsafe. For applications demanding the highest anatomical fidelity within the MDR framework, however, the evidence now points decisively toward Material Jetting.

The research also carries implications well beyond craniomaxillofacial surgery. In-house and point-of-care 3D printing is expanding rapidly into orthopedics, cardiothoracic surgery, dental applications and surgical training, and virtually every one of those programs faces the same questions: how accurate is our printer, how repeatable is it, and can we prove it? The study’s methodological chain—realistic anatomical models, digitized reference comparisons, RMS-based quantification of trueness and precision, and mixed-model statistics that respect the structure of repeated manufacturing data—offers a portable blueprint for any institution seeking to validate its own fleet. The authors note it is the first application of a Linear Mixed Model in this field, and if the approach is adopted broadly, it could standardize how hospitals across jurisdictions demonstrate regulatory compliance. Published open access in the journal 3D Printing in Medicine, with no specific external funding and no declared competing interests, the study was reviewed with the involvement of the Ghent University Hospital Medical Ethics Committee, receiving official approval on July 2, 2024, and received on 16 September 2025, accepted on 16 August 2026, and published on 11 September 2026.

Subject of Research: Comparative accuracy of FFF, SLA and Material Jetting 3D printing for in-house anatomical models in craniomaxillofacial surgery

Article Title: Clinical relevance of accuracy in in-house 3D printing in craniomaxillofacial surgery: a comparative study of FFF, SLA, and MJ technologies

Article References: Lievens, M., Van Paepegem, W., Goffin, T., Villeirs, G., & Coopman, R. (2026). Clinical relevance of accuracy in in-house 3D printing in craniomaxillofacial surgery: a comparative study of FFF, SLA, and MJ technologies. 3D Printing in Medicine. https://doi.org/10.1186/s41205-026-00344-8

Image Credits: AI Generated

DOI: 10.1186/s41205-026-00344-8

Keywords: 3D printing, craniomaxillofacial surgery, anatomical models, FFF, SLA, Material Jetting, Medical Device Regulation, accuracy validation, Linear Mixed Model, point-of-care manufacturing, Clinical, relevance