Abstract
Background: Three-dimensional (3D) printing is promising in medical applications, especially presurgical planning and the simulation of congenital heart disease (CHD). Thus, it is clinically important to generate highly accurate 3D-printed models in replicating cardiac anatomy and defects. The present study aimed to investigate the accuracy of the 3D-printed CHD model by comparing them with computed tomography (CT) images and standard tessellation language (STL) files. Methods: Three models were printed, comprising different CHD pathologies, including the tetralogy of Fallot (ToF), ventricular septal defect (VSD) and double-outlet right-ventricle (DORV). The ten anatomical locations were measured in each comparison. Pearson’s correlation coefficient, Bland–Altman analysis and intra-class correlation coefficient (ICC) determined the model accuracy. Results: All measurements with three printed models showed a strong correlation (r = 0.99) and excellent reliability (ICC = 0.97) when compared to original CT images, CT images of the 3D-printed models, STL files and 3D-printed CHD models. Conclusion: This study demonstrated the high accuracy of 3D-printed heart models with excellent correlation and reliability when compared to multiple source data. Further investigation into 3D printing in CHD should focus on the clinical value and the benefits to patients.
Highlights
Congenital heart disease (CHD) is the most common congenital abnormality, responsible for high infant mortality globally [1,2]
All measurements with three printed models showed a strong correlation (r = 0.99) and excellent reliability (ICC = 0.97) when compared to original computed tomography (CT) images, CT images of the 3D-printed models, standard tessellation language (STL) files and 3D-printed congenital heart disease (CHD) models
This study demonstrated the high accuracy of 3D-printed heart models with excellent correlation and reliability when compared to multiple source data
Summary
Congenital heart disease (CHD) is the most common congenital abnormality, responsible for high infant mortality globally [1,2]. The diagnosis of CHD is mainly based on two-dimensional (2D) images, using computed tomography (CT), magnetic resonance imaging (MRI) or echocardiography imaging; it is difficult to present the complex structure of CHD on traditional 2D or three-dimensional (3D) visualisation due to the wide variability of the pathologies [4]. This limitation of the method is overcome with. Pearson’s correlation coefficient, Bland–Altman analysis and intra-class correlation coefficient (ICC) determined the model accuracy
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