Which 3D Printer Wins for Skull Surgery? New Study Ranks FFF, SLA and Jetting

Hospitals that print their own anatomical models for craniomaxillofacial surgery now have some of the most rigorous evidence yet about which machines they can trust. A new peer-reviewed study from Ghent University Hospital and Ghent University in Belgium has compared the three most common 3D printing technologies used at the point of care—Fused Filament Fabrication (FFF), Stereolithography (SLA), and Material Jetting (MJ)—and found that while all three can produce clinically acceptable models, Material Jetting delivers the highest accuracy under realistic clinical conditions. The findings arrive at a pivotal moment, as the European Medical Device Regulation (MDR 2017/745) places growing demands on hospitals that manufacture patient-specific devices and models in-house, requiring documented evidence of quality and accuracy rather than assumptions based on manufacturer brochures.

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