ArticleFrontiers in cell and developmental biology2021
Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 11 citations in OpenAlex.
- When Virchow's law fails: Crouzon syndrome as a test case for quantitative suture closure-endocranial shape relationships.Journal of anatomy · 2026Article
- Article
- Functional adaptation of the infant craniofacial system to mechanical loadings arising from masticatory forces.Proceedings. Biological sciences · 2024Article
- A preliminary analysis of replicating the biomechanics of helmet therapy for sagittal craniosynostosis.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2023Article
- A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis.Frontiers in bioengineering and biotechnology · 2022Article
- Predicting and comparing three corrective techniques for sagittal craniosynostosis.Scientific reports · 2021Article
- Isolated Effects of Bone, Spring and Surgical Parameters on the Surgical Outcome in Spring-Assisted Sagittal Synostosis Correction.Healthcare technology lettersArticle
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Craniosynostosis is the premature fusion of one or more sutures across the calvaria, resulting in morphological and health complications that require invasive corrective surgery. Finite element (FE) method is a powerful tool that can aid with preoperative planning and post-operative predictions of craniosynostosis outcomes. However, input factors can influence the prediction of skull growth and the pressure on the growing brain using this approach. Therefore, the aim of this study was to carry out a series of sensitivity studies to understand the effect of various input parameters on predicting the skull morphology of a sagittal synostosis patient post-operatively. Preoperative CT images of a 4-month old patient were used to develop a 3D model of the skull, in which calvarial bones, sutures, cerebrospinal fluid (CSF), and brain were segmented. Calvarial reconstructive surgery was virtually modeled and two intracranial content scenarios labeled "CSF present" and "CSF absent," were then developed. FE method was used to predict the calvarial morphology up to 76 months of age with intracranial volume-bone contact parameters being established across the models. Sensitivity tests with regards to the choice of material properties, methods of simulating bone formation and the rate of bone formation across the sutures were undertaken. Results were compared to the
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