Key result
3D printing pipelines in congenital heart disease propagate cumulative errors across imaging, segmentation, and fabrication.
Why the study?
The clinical validity of 3D-printed models in congenital heart disease depends on accuracy across a multistage pipeline, yet technical and human discrepancies at each stage can accumulate and alter final model fidelity, particularly in paediatric applications with narrow permissible tolerances.
This integrative review outlines how technical and operator-dependent errors accumulate across the 3D printing pipeline in congenital heart disease and provides a standardized quality-assurance framework and inter-observer audit protocol to safeguard clinical decision-making.
Answers are grounded in this paper and cite their sources.
Cumulative errors may compromise 3D-printed congenital heart models; leaves open standardized protocols before clinical integration.
Three-dimensional (3D) printing has gained prominence in the management of congenital heart disease (CHD) by augmenting spatial understanding, facilitating preoperative planning, and supporting education for clinicians and families. The clinical validity of printed models, however, is contingent upon accuracy maintained across a multistage pipeline that spans image acquisition, segmentation and modelling, printer-specific processing, and post-processing with subsequent clinical interpretation. Technical and human factors introduce discrepancies at each stage; these inaccuracies may accumulate and propagate, thereby altering the geometric and qualitative fidelity of the final model. This integrative review examines the principal sources of error across the pipeline, outlines mechanisms of propagation, and considers implications for decision-making in CHD, with particular emphasis on paediatric applications where permissible tolerances are narrow. Drawing upon contemporary literature, we provide recommendations for protocol standardization, validation procedures, operator training, and transparent documentation. An inter-observer experiment is proposed to quantify segmentation-induced variability using similarity and surface distance metrics, linking measurable divergence to potential clinical impact. A pragmatic error-minimizing pipeline is also described to improve reliability and promote reproducibility in clinical practice.
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Balaji et al. (2026) conducted a review in Congenital heart disease (particularly pediatric applications). 3D printing pipeline (image acquisition, segmentation, modelling, printer-specific processing, and post-processing) was evaluated on Geometric and qualitative fidelity, error propagation, and clinical interpretation of 3D-printed heart models. Three-dimensional printing in congenital heart disease is subject to cumulative error propagation across image acquisition, segmentation (inter-observer DSC ~0.70–0.95), and fabrication.
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