ReviewMolecular syndromology2019
An Overview of Modelling Craniosynostosis Using the Finite Element Method.
Review in Molecular syndromology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed, 32 citations in OpenAlex.
- Optimising Fronto-Orbital Distraction for Management of Unicoronal Craniosynostosis.Annals of biomedical engineering · 2026Article
- Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery.Journal of Korean Neurosurgical Society · 2026Review
- Predicting surgical outcomes in spring assisted cranioplasty via finite element analysis and animal experiments.Scientific reports · 2025Article
- Sphenoid sinus hyperpneumatization: anatomical variants, molecular blueprints, and AI-augmented roadmaps for skull base surgery.Frontiers in endocrinology · 2025Review
- Geometric growth of the normal human craniocervical junction from 0 to 18 years old.Journal of anatomy · 2024Article
- Article
- Understanding the influence of surgical parameters on craniofacial surgery outcomes: a computational study.Royal Society open science · 2024Article
- The biomechanics of chewing and suckling in the infant: A potential mechanism for physiologic metopic suture closure.PLoS computational biology · 2023Article
- Biomechanical Investigation of Hallux Valgus Deformity Treated with Different Osteotomy Methods and Kirschner Wire Fixation Strategies Using the Finite Element Method.Bioengineering (Basel, Switzerland) · 2023Article
- 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
- Automated surgical planning in spring-assisted sagittal craniosynostosis correction using finite element analysis and machine learning.PloS one · 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
- [Simulation method of skull remodellingsurgeryfor infant with craniosynostosis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis.Frontiers in cell and developmental biology · 2021Article
- Computational modelling of patient specific spring assisted lambdoid craniosynostosis correction.Scientific reports · 2020Article
- Modeling the effect of brain growth on cranial bones using finite-element analysis and geometric morphometrics.Surgical and radiologic anatomy : SRA · 2020Article
- Predicting calvarial morphology in sagittal craniosynostosis.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Craniosynostosis is a medical condition caused by the early fusion of the cranial joint. The finite element method (FEM) is a computational technique that can answer a variety of "what if" questions in relation to the biomechanics of this condition. The aim of this study was to review the current literature that has used FEM to investigate the biomechanics of any aspect of craniosynostosis, being its development or its reconstruction. This review highlights that a relatively small number of studies (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.