Abstract
BackgroundThis study aims to capture the 3D shape of the human skull in a healthy paediatric population (0–4 years old) and construct a generative statistical shape model. MethodsThe skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (CT) images. 29 anatomical landmarks were placed on the 3D skull reconstructions. Rotation, translation and size were removed, and all skull meshes were placed in dense correspondence using a dimensionless skull mesh template and a non-rigid iterative closest point algorithm. A 3D morphable model (3DMM) was created using principal component analysis, and intrinsically and geometrically validated with anthropometric measurements. Synthetic skull instances were generated exploiting the 3DMM and validated by comparison of the anthropometric measurements with the selected input population. ResultsThe 3DMM of the paediatric skull 0–4 years was successfully constructed. The model was reasonably compact - 90% of the model shape variance was captured within the first 10 principal components. The generalisation error, quantifying the ability of the 3DMM to represent shape instances not encountered during training, was 0.47 mm when all model components were used. The specificity value was <0.7 mm demonstrating that novel skull instances generated by the model are realistic. The 3DMM mean shape was representative of the selected population (differences <2%). Overall, good agreement was observed in the anthropometric measures extracted from the selected population, and compared to normative literature data (max difference in the intertemporal distance) and to the synthetic generated cases. ConclusionThis study presents a reliable statistical shape model of the paediatric skull 0–4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, thus presenting a solution to limited availability of normative data in this field.
Highlights
In silico medicine refers to the development and use of computa tional models that realistically mimic and simulate patients’ biology and medical interventions in a virtual environment (Pappalardo et al, 2019)
The greatest difference between the two datasets was observed for the intertemporal distance: as growth in the skull region is rapid for children in the assessed age range, some of the measurement differences may be attributed to the different age distri butions of the two study populations (Supplementary Materials)
This study presents a 3D morphable model (3DMM) of the paediatric skull (0–4 years), constructed from 178 normal computed tomography (CT) scans
Summary
In silico medicine refers to the development and use of computa tional models that realistically mimic and simulate patients’ biology and medical interventions in a virtual environment (Pappalardo et al, 2019). In silico medical modelling can complement medical device/drug development by reducing, refining or partially replacing the three traditional sources of evidence - bench testing, animal testing and human clinical trials - to establish device/drug safety and effectiveness, at significantly lower costs (Viceconti et al, 2021) This can be of particular benefit for those medical fields that focus on rare diseases, such as craniofacial syn dromes, where availability of data is limited, relevant animal models are lacking, enrolling large cohorts of real patients is unfeasible and the market is overall too small for the medical device/pharma industry to. Conclusion: This study presents a reliable statistical shape model of the paediatric skull 0–4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, presenting a solution to limited availability of normative data in this field
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