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

Considering the Medical Device Regulation (MDR 2017/745), this study aims to evaluate the accuracy of three commonly used 3D printing technologies—Fused Filament Fabrication (FFF), Stereolithography (SLA), and Material Jetting (MJ)—within a clinically realistic in-house 3D printing workflow. In this study, the accuracy of 3D printers was assessed using highly accurate anatomical models to mimic an in vitro clinical workflow, unlike previous validation studies that relied on geometric calibration models or simplified shapes. Accuracy was defined through precision (intra- and inter-build variability) and trueness (deviation from the digital reference model), quantified using root mean square (RMS) error. To analyse the data structure—comprising repeated prints, multiple models, and timepoints—a Linear Mixed Model (LMM) was used, which enabled evaluation of fixed effects (printer type, anatomical model, and comparison type) while correcting for internal clustering of data. All printers achieved clinically acceptable accuracy. MJ was significantly more accurate (RMS 67 μm) compared to SLA (109 μm) and FFF (130 μm). The Linear Mixed Model showed that accuracy was influenced by printer type and anatomical complexity. This is the first study in 3D printing accuracy research to apply a Linear Mixed Model, providing a statistically robust analysis framework. Among the evaluated technologies, MJ achieved the highest accuracy in clinically realistic conditions, supporting its use in applications that require high anatomical fidelity within the MDR framework.

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Publication Details

Journal
3D Printing in Medicine
Published
2026-09-11
DOI
https://doi.org/10.1186/s41205-026-00344-8
Primary Topic
Anatomy and Medical Technology
Type
article
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article

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

Renaat Coopman, Mauranne Lievens, Tom Goffin, Wim Van Paepegem et al.
3D Printing in Medicine
Anatomy and Medical Technology
article

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

Renaat Coopman, Mauranne Lievens, Tom Goffin, Wim Van Paepegem, Geert Villeirs
article en

Abstract

Considering the Medical Device Regulation (MDR 2017/745), this study aims to evaluate the accuracy of three commonly used 3D printing technologies—Fused Filament Fabrication (FFF), Stereolithography (SLA), and Material Jetting (MJ)—within a clinically realistic in-house 3D printing workflow. In this study, the accuracy of 3D printers was assessed using highly accurate anatomical models to mimic an in vitro clinical workflow, unlike previous validation studies that relied on geometric calibration models or simplified shapes. Accuracy was defined through precision (intra- and inter-build variability) and trueness (deviation from the digital reference model), quantified using root mean square (RMS) error. To analyse the data structure—comprising repeated prints, multiple models, and timepoints—a Linear Mixed Model (LMM) was used, which enabled evaluation of fixed effects (printer type, anatomical model, and comparison type) while correcting for internal clustering of data. All printers achieved clinically acceptable accuracy. MJ was significantly more accurate (RMS 67 μm) compared to SLA (109 μm) and FFF (130 μm). The Linear Mixed Model showed that accuracy was influenced by printer type and anatomical complexity. This is the first study in 3D printing accuracy research to apply a Linear Mixed Model, providing a statistically robust analysis framework. Among the evaluated technologies, MJ achieved the highest accuracy in clinically realistic conditions, supporting its use in applications that require high anatomical fidelity within the MDR framework.

3D Printing in Medicine
Ghent University Hospital (BE), Ghent University (BE)
Openalex Percentile: Top 21%
Anatomy and Medical Technology
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