Three-dimensional (3D) printed models represent educational tools of high quality compared with traditional teaching aids. Colored skull models were produced by 3D printing technology. A randomized controlled trial (RCT) was conducted to compare the learning efficiency of 3D printed skulls with that of cadaveric skulls and atlas. Seventy-nine medical students, who never studied anatomy, were randomized into three groups by drawing lots, using 3D printed skulls, cadaveric skulls, and atlas, respectively, to study the anatomical structures in skull through an introductory lecture and small group discussions. All students completed identical tests, which composed of a theory test and a lab test, before and after a lecture. Pre-test scores showed no differences between the three groups. In post-test, the 3D group was better than the other two groups in total score (cadaver: 29.5 [IQR: 25–33], 3D: 31.5 [IQR: 29–36], atlas: 27.75 [IQR: 24.125–32]; p = 0.044) and scores of lab test (cadaver: 14 [IQR: 10.5–18], 3D: 16.5 [IQR: 14.375–21.625], atlas: 14.5 [IQR: 10–18.125]; p = 0.049). Scores involving theory test, however, showed no difference between the three groups. In this RCT, an inexpensive, precise and rapidly-produced skull model had advantages in assisting anatomy study, especially in structure recognition, compared with traditional education materials.

We present an available and cost-effective method for producing both digital resources and printed models. The choice of modality in medical images and the processing approach is important when reproducing soft tissue organs models. The accuracy of the printed model is determined by the quality of organ models and 3DP. With the ongoing improvement of printing techniques and the variety of materials available, 3DP will become an indispensable tool in medical training and surgical planning.

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Previous studies have used principal component analysis (PCA) to investigate the craniofacial relationship, as well as sex determination using facial factors. However, few studies have investigated the extent to which the choice of principal components (PCs) affects the analysis of craniofacial relationship and sexual dimorphism. In this paper, we propose a PCA-based method for visual and quantitative analysis, using 140 samples of 3D heads (70 male and 70 female), produced from computed tomography (CT) images. There are two parts to the method. First, skull and facial landmarks are manually marked to guide the model's registration so that dense corresponding vertices occupy the same relative position in every sample. Statistical shape spaces of the skull and face in dense corresponding vertices are constructed using PCA. Variations in these vertices, captured in every principal component (PC), are visualized to observe shape variability. The correlations of skull- and face-based PC scores are analysed, and linear regression is used to fit the craniofacial relationship. We compute the PC coefficients of a face based on this craniofacial relationship and the PC scores of a skull, and apply the coefficients to estimate a 3D face for the skull. To evaluate the accuracy of the computed craniofacial relationship, the mean and standard deviation of every vertex between the two models are computed, where these models are reconstructed using real PC scores and coefficients. Second, each PC in facial space is analysed for sex determination, for which support vector machines (SVMs) are used. We examined the correlation between PCs and sex, and explored the extent to which the choice of PCs affects the expression of sexual dimorphism. Our results suggest that skull- and face-based PCs can be used to describe the craniofacial relationship and that the accuracy of the method can be improved by using an increased number of face-based PCs. The results show that the accuracy of the sex classification is related to the choice of PCs. The highest sex classification rate is 91.43% using our method.