Correlation between head shape and volumetric changes following spring-assisted posterior vault expansion.

The aim of the study was to investigate whether different head shapes show different volumetric changes following spring-assisted posterior vault expansion (SA-PVE) and to investigate the influence of surgical and morphological parameters on SA-PVE. Preoperative three-dimensional skull models from patients who underwent SA-PVE were extracted from computed tomography scans. Patient head shape was described using statistical shape modelling (SSM) and principal component analysis (PCA).

Spring-assisted posterior vault expansion-a single-centre experience of 200 cases.

Purpose: Children affected by premature fusion of the cranial sutures due to craniosynostosis can present with raised intracranial pressure and (turri)brachycephalic head shapes that require surgical treatment. Spring-assisted posterior vault expansion (SA-PVE) is the surgical technique of choice at Great Ormond Street Hospital for Children (GOSH), London, UK. This study aims to report the SA-PVE clinical experience of GOSH to date.

Local Soft Tissue and Bone Displacements Following Midfacial Bipartition Distraction in Apert Syndrome - Quantification Using a Semi-Automated Method.

Patients with Apert syndrome experience midfacial hypoplasia, hypertelorism, and downslanting palpebral fissures which can be corrected by midfacial bipartition distraction with rigid external distraction device. Quantitative studies typically focus on quantifying rigid advancement and rotation postdistraction, but intrinsic shape changes of bone and soft tissue remain unknown. This study presents a method to quantify these changes.

Statistical shape modelling for the analysis of head shape variations.

The aim of this study is, firstly, to create a population-based 3D head shape model for the 0 to 2-year-old subjects to describe head shape variability within a normal population and, secondly, to test a combined normal and sagittal craniosynostosis (SAG) population model, able to provide surgical outcome assessment. 3D head shapes of patients affected by non-cranial related pathologies and of SAG patients (pre- and post-op) were extracted either from head CTs or 3D stereophotography scans, and processed. Statistical shape modelling (SSM) was used to describe shape variability using two models - a normal population model (MODEL1) and a combined normal and SAG population model (MODEL2).

A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery.

Current computational tools for planning and simulation in plastic and reconstructive surgery lack sufficient precision and are time-consuming, thus resulting in limited adoption. Although computer-assisted surgical planning systems help to improve clinical outcomes, shorten operation time and reduce cost, they are often too complex and require extensive manual input, which ultimately limits their use in doctor-patient communication and clinical decision making. Here, we present the first large-scale clinical 3D morphable model, a machine-learning-based framework involving supervised learning for diagnostics, risk stratification, and treatment simulation.