A novel RBF-based predictive tool for facial distraction surgery in growing children with syndromic craniosynostosis

PURPOSE: Predicting changes in face shape from corrective surgery is challenging in growing children with syndromic craniosynostosis. A prediction tool mimicking composite bone and skin movement during facial distraction would be useful for surgical audit and planning. To model surgery, we used a radial basis function (RBF) that is smooth and continuous throughout space whilst corresponding to measured distraction at landmarks. Our aim is to showcase the pipeline for a novel landmark-based, RBF-driven simulation for facial distraction surgery in children. METHODS: An individual's dataset comprised of manually placed skin and bone landmarks on operated and unoperated regions. Surgical warps were produced for 'older' monobloc, 'older' bipartition and 'younger' bipartition groups by applying a weighted least-squares RBF fitted to the average landmarks and change vectors. A 'normalisation' warp, from fitting an RBF to craniometric landmark differences from the average, was applied to each dataset before the surgical warp. The normalisation was finally reversed to obtain the individual prediction. Predictions were compared to actual post-operative outcomes. RESULTS: The averaged change vectors for all groups showed skin and bone movements characteristic of the operations. Normalisation for shape-size removed individual asymmetry, size and proportion differences but retained typical pre-operative shape features. The surgical warps removed the average syndromic features. Reversing the normalisation reintroduced the individual's variation into the prediction. The mid-facial regions were well predicted for all groups. Forehead and brow regions were less well predicted. CONCLUSIONS: Our novel, landmark-based, weighted RBF can predict the outcome for facial distraction in younger and older children with a variety of head and face shapes. It can replicate the surgical reality of composite bone and skin movement jointly in one model. The potential applications include audit of existing patient outcomes, and predicting outcome for new patients to aid surgical planning

Sporting activity after craniosynostosis surgery in children: a source of parental anxiety

Purpose: Craniosynostosis correction involves major skull surgery in infancy—a potential source of worry for parents when their treated children begin involvement in sports. Methods: Electronic multiple choice survey of parents of children who had undergone craniosynostosis surgery in infancy using 5-point Likert scales. Results: Fifty-nine completed surveys were obtained from parents of children who had undergone previous craniosynostosis surgery. Mean age of children was 7.8 years (range 3 months to 22 years), with 36 non-syndromic and 23 syndromic cases. The most common surgery was fronto-orbital remodelling (18). Fifty-two of 59 were involved in athletic activity. The most intense sport type was non-contact in 23, light contact in 20, heavy contact in 4 and combat in 5. Participation level was school mandatory in 12, school club in 17, non-school sport club in 21 and regional representative in 2. One child had been advised to avoid sport by an external physician. Mean anxiety (1–5 Likert) increased with sport intensity: non-contact 1.7, light contact 2.2, heavy contact 3.5 and combat 3.6. Twenty-nine of 59 parents had been given specific advice by the Craniofacial Team regarding athletic activity, 28 of which found useful. Three sport-related head injuries were reported, none of which required hospitalisation. Conclusion: Little information exists regarding sports for children after craniosynostosis surgery. This study suggests that parental anxiety remains high, particularly for high impact/combat sports, and that parents would like more information from clinicians about the safety of post-operative sporting activities

Enhanced neuro-ophthalmologic evaluation to support separation of craniopagus twins

Craniopagus conjoined twins are extraordinarily rare and present unique challenges to the multidisciplinary team. There is a paucity of literature on optimizing neuro-ophthalmologic evaluation in craniopagus twins. Herein, we present our enhanced neuro-ophthalmologic evaluation and management in 17-month-old male craniopagus twins, uniquely using handheld optical coherence tomography (OCT) plus portable slit-lamp biomicroscopy, indirect ophthalmoscopy and modified forced-choice preferential looking assessment. Staged surgical separation was supported by enhanced neuro-ophthalmologic evaluation, detailed radiology, three-dimensional printing and virtual reality simulation. This represents the fourth separation of craniopagus twins by our unit

Computational modelling of patient specific spring assisted lambdoid craniosynostosis correction

Lambdoid craniosynostosis (LC) is a rare non-syndromic craniosynostosis characterised by fusion of the lambdoid sutures at the back of the head. Surgical correction including the spring assisted cranioplasty is the only option to correct the asymmetry at the skull in LC. However, the aesthetic outcome from spring assisted cranioplasty may remain suboptimal. The aim of this study is to develop a parametric finite element (FE) model of the LC skulls that could be used in the future to optimise spring surgery. The skull geometries from three different LC patients who underwent spring correction were reconstructed from the pre-operative computed tomography (CT) in Simpleware ScanIP. Initially, the skull growth between the pre-operative CT imaging and surgical intervention was simulated using MSC Marc. The osteotomies and spring implantation were performed to simulate the skull expansion due to the spring forces and skull growth between surgery and post-operative CT imaging in MSC Marc. Surface deviation between the FE models and post-operative skull models reconstructed from CT images changed between ± 5 mm over the skull geometries. Replicating spring assisted cranioplasty in LC patients allow to tune the parameters for surgical planning, which may help to improve outcomes in LC surgeries in the future

Geometric morphometrics aided by machine learning in craniofacial surgery

Geometric morphometrics aided by machine learning provide detailed and accurate statistical models of facial form. They promise to be extremely effective tools in surgical planning and assessment; however, a clinical tool to use this information is still to be created

Maxillary Changes Following Facial Bipartition – A Three-Dimensional Quantification

INTRODUCTION: Children with Apert syndrome have hypertelorism and midfacial hypoplasia, which can be treated with facial bipartition (FB), often aided by rigid external distraction. The technique involves a midline osteotomy that lateralizes the maxillary segments, resulting in posterior cross-bites and midline diastema. Varying degrees of spontaneous realignment of the dental arches occurs postoperatively. This study aims to quantify these movements and assess whether they occur as part of a wider skeletal relapse or as dental compensation. METHODS: Patients who underwent FB and had high quality computed tomography scans at the preoperative stage, immediately postsurgery, and later postoperatively were reviewed. DICOM files were converted to three-dimensional bone meshes and anatomical point-to-point displacements were quantified using nonrigid iterative closest point registration. Displacements were visualized using arrow maps, thereby providing an overview of the movements of the facial skeleton and dentition. RESULTS: Five patients with Apert syndrome were included. In all cases, the arrow maps demonstrated initial significant anterior movement of the frontofacial segment coupled with medial rotation of the orbits and transverse divergence of the maxillary arches. The bony position following initial surgery was shown to be largely stable, with primary dentoalveolar relapse correcting the dental alignment. CONCLUSIONS: This study showed that spontaneous dental compensation occurs following FB without compromising the surgical result. It may be appropriate to delay active orthodontic for 6-months postoperatively until completion of this early compensatory phase

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

ABSTRACT: 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. Pre- and post-operative computed tomography scans from patients with Apert syndrome undergoing midfacial bipartition distraction with rigid external distraction device were collected. Digital Imaging and Communications in Medicine files were converted to three-dimensional bone and soft tissue reconstructions. Postoperative reconstructions were aligned on the preoperative maxilla, followed by nonrigid iterative closest point transformation to determine local shape changes. Anatomical point-to-point displacements were calculated and visualized using a heatmap and arrow map. Nine patients were included.Zygomatic arches and frontal bone demonstrated the largest changes. Mid-lateral to supra-orbital rim showed an upward, inward motion. Mean bone displacements ranged from 3.3 to 12.8 mm. Soft tissue displacements were relatively smaller, with greatest changes at the lateral canthi. Midfacial bipartition distraction with rigid external distraction device results in upward, inward rotation of the orbits, upward rotation of the zygomatic arch, and relative posterior motion of the frontal bone. Local movements were successfully quantified using a novel method, which can be applied to other surgical techniques/syndromes

The 3D skull 0–4 years: A validated, generative, statistical shape model

BACKGROUND: This 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. METHODS: The 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. RESULTS: The 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. 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, thus presenting a solution to limited availability of normative data in this field

Craniofacial Syndrome Identification Using Convolutional Mesh Autoencoders

Background: Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In general, these systems use 2D images and analyse texture and colour. While these are powerful tools for photographic analysis, they are not suitable for use with medical imaging modalities such as ultrasound, MRI or CT, and are unable to take shape information into consideration when making a diagnostic prediction. 3D morphable models (3DMMs), and their recently proposed successors, mesh autoencoders, analyse surface topography rather than texture enabling analysis from photography and all common medical imaging modalities, and present an alternative to image-based analysis. // Methods: We present a craniofacial analysis framework for syndrome identification using Convolutional Mesh Autoencoders (CMAs). The models were trained using 3D photographs of the general population (LSFM and LYHM), computed tomography data (CT) scans from healthy infants and patients with 3 genetically distinct craniofacial syndromes (Muenke, Crouzon, Apert). // Findings: Machine diagnosis outperformed expert clinical diagnosis with an accuracy of 99.98%, sensitivity of 99.95% and specificity of 100%. The diagnostic precision of this technique supports its potential inclusion in clinical decision support systems. Its reliance on 3D topography characterisation makes it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting. // Interpretation: Our study demonstrates the use of 3D convolutional mesh autoencoders for the diagnosis of syndromic craniosynostosis. The topological nature of the tool presents opportunities for this method to be applied as a diagnostic tool across a number of 3D imaging modalities