BounTI (boundary-preserving threshold iteration): A user-friendly tool for automatic hard tissue segmentation.

X-ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub-parts and can be a time-consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open-access and user-friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High-resolution microCT images resulted in more accurate boundary-preserved segmentation, nonetheless significantly lower-quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add-on, a stand-alone Windows executable, and a Python library. We believe this accessible and user-friendly segmentation tool can benefit the wider anatomical community.

Geometric growth of the normal human craniocervical junction from 0 to 18 years old.

The craniocervical junction (CCJ) forms the bridge between the skull and the spine, a highly mobile group of joints that allows the mobility of the head in every direction. The CCJ plays a major role in protecting the inferior brainstem (bulb) and spinal cord, therefore also requiring some stability. Children are subjected to multiple constitutive or acquired diseases involving the CCJ: primary bone diseases such as in FGFR-related craniosynostoses or acquired conditions such as congenital torticollis, cervical spine luxation, and neurological disorders. To design efficient treatment plans, it is crucial to understand the relationship between abnormalities of the craniofacial region and abnormalities of the CCJ. This can be approached by the study of control and abnormal growth patterns. Here we report a model of normal skull base growth by compiling a collection of geometric models in control children. Focused analyses highlighted specific developmental patterns for each CCJ bone, emphasizing rapid growth during infancy, followed by varying rates of growth and maturation during childhood and adolescence until reaching stability by 18 years of age. The focus was on the closure patterns of synchondroses and sutures in the occipital bone, revealing distinct closure trajectories for the anterior intra-occipital synchondroses and the occipitomastoid suture. The findings, although based on a limited dataset, showcased specific age-related changes in width and closure percentages, providing valuable insights into growth dynamics within the first 2 years of life. Integration analyses revealed intricate relationships between skull and neck structures, emphasizing coordinated growth at different stages. Specific bone covariation patterns, as found between the first and second cervical vertebrae (C1 and C2), indicated synchronized morphological changes. Our results provide initial data for designing inclusive CCJ geometric models to predict normal and abnormal growth dynamics.

Artificial intelligence-based diagnosis in fetal pathology using external ear shapes.

OBJECTIVE: Here we trained an automatic phenotype assessment tool to recognize syndromic ears in two syndromes in fetuses-=CHARGE and Mandibulo-Facial Dysostosis Guion Almeida type (MFDGA)-versus controls. METHOD: We trained an automatic model on all profile pictures of children diagnosed with genetically confirmed MFDGA and CHARGE syndromes, and a cohort of control patients, collected from 1981 to 2023 in Necker Hospital (Paris) with a visible external ear. The model consisted in extracting landmarks from photographs of external ears, in applying geometric morphometry methods, and in a classification step using machine learning. The approach was then tested on photographs of two groups of fetuses: controls and fetuses with CHARGE and MFDGA syndromes. RESULTS: The training set contained a total of 1489 ear photographs from 526 children. The validation set contained a total of 51 ear photographs from 51 fetuses. The overall accuracy was 72.6% (58.3%-84.1%, p < 0.001), and 76.4%, 74.9%, and 86.2% respectively for CHARGE, control and MFDGA fetuses. The area under the curves were 86.8%, 87.5%, and 90.3% respectively for CHARGE, controls, and MFDGA fetuses. CONCLUSION: We report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool.

A European multicenter outcome study on the different perioperative airway management policies following midface surgery in syndromic craniosynostosis: a proposal for a Standard Operating Procedure.

BACKGROUND: Perioperative airway management following midface advancements in children with Apert and Crouzon/Pfeiffer syndrome can be challenging, and protocols often differ. This study examined airway management following midface advancements and postoperative respiratory complications. METHODS: A multicenter, retrospective cohort study was performed to obtain information about the timing of extubation, perioperative airway management, and respiratory complications after monobloc / le Fort III procedures. RESULTS: Ultimately, 275 patients (129 monobloc and 146 Le Fort III) were included; 62 received immediate extubation and 162 delayed extubation; 42 had long-term tracheostomies and nine perioperative short-term tracheostomies. Short-term tracheostomies were in most centers reserved for selected cases. Patients with delayed extubation remained intubated for three days (IQR 2 - 5). The rate of no or only oxygen support after extubation was comparable between patients with immediate and delayed extubation, 58/62 (94%) and 137/162 (85%) patients, respectively. However, patients with immediate extubation developed less postoperative pneumonia than those with delayed, 0/62 (0%) versus 24/161 (15%) (P = 0.001), respectively. Immediate extubation also appeared safe in moderate/severe OSA since 19/20 (95%) required either no or only oxygen support after extubation. The odds of developing intubation-related complications increased by 21% with every extra day of intubation. CONCLUSIONS: Immediate extubation following midface advancements was found to be a safe option, as it was not associated with respiratory insufficiency but did lead to fewer complications. Immediate extubation should be considered routine management in patients with no/mild OSA and should be the aim in moderate/severe OSA after careful assessment.

Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome.

The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes.

Introduction: Mandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes. Methods: The training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set. Results: We trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838-0.999] (p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648-0.920] (p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544-0.960] (p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses). Conclusion: This is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.

Assessing craniofacial growth and form without landmarks: A new automatic approach based on spectral methods.

We present a novel method for the morphometric analysis of series of 3D shapes, and demonstrate its relevance for the detection and quantification of two craniofacial anomalies: trigonocephaly and metopic ridges, using CT-scans of young children. Our approach is fully automatic, and does not rely on manual landmark placement and annotations. Our approach furthermore allows to differentiate shape classes, enabling successful differential diagnosis between trigonocephaly and metopic ridges, two related conditions characterized by triangular foreheads. These results were obtained using recent developments in automatic nonrigid 3D shape correspondence methods and specifically spectral approaches based on the functional map framework. Our method can capture local changes in geometric structure, in contrast to methods based, for instance, on global shape descriptors. As such, our approach allows to perform automatic shape classification and provides visual feedback on shape regions associated with different classes of deformations. The flexibility and generality of our approach paves the way for the application of spectral methods in quantitative medicine.

Normal human craniofacial growth and development from 0 to 4 years.

Knowledge of human craniofacial growth (increase in size) and development (change in shape) is important in the clinical treatment of a range of conditions that affects it. This study uses an extensive collection of clinical CT scans to investigate craniofacial growth and development over the first 48 months of life, detail how the cranium changes in form (size and shape) in each sex and how these changes are associated with the growth and development of various soft tissues such as the brain, eyes and tongue and the expansion of the nasal cavity. This is achieved through multivariate analyses of cranial form based on 3D landmarks and semi-landmarks and by analyses of linear dimensions, and cranial volumes. The results highlight accelerations and decelerations in cranial form changes throughout early childhood. They show that from 0 to 12 months, the cranium undergoes greater changes in form than from 12 to 48 months. However, in terms of the development of overall cranial shape, there is no significant sexual dimorphism in the age range considered in this study. In consequence a single model of human craniofacial growth and development is presented for future studies to examine the physio-mechanical interactions of the craniofacial growth.

Icex: Advances in the automatic extraction and volume calculation of cranial cavities.

The use of non-destructive approaches for digital acquisition (e.g. computerised tomography-CT) allows detailed qualitative and quantitative study of internal structures of skeletal material. Here, we present a new R-based software tool, Icex, applicable to the study of the sizes and shapes of skeletal cavities and fossae in 3D digital images. Traditional methods of volume extraction involve the manual labelling (i.e. segmentation) of the areas of interest on each section of the image stack. This is time-consuming, error-prone and challenging to apply to complex cavities. Icex facilitates rapid quantification of such structures. We describe and detail its application to the isolation and calculation of volumes of various cranial cavities. The R tool is used here to automatically extract the orbital volumes, the paranasal sinuses, the nasal cavity and the upper oral volumes, based on the coordinates of 18 cranial anatomical points used to define their limits, from 3D cranial surface meshes obtained by segmenting CT scans. Icex includes an algorithm (Icv) for the calculation of volumes by defining a 3D convex hull of the extracted cavity. We demonstrate the use of Icex on an ontogenetic sample (0-19 years) of modern humans and on the fossil hominin crania Kabwe (Broken Hill) 1, Gibraltar (Forbes' Quarry) and Guattari 1. We also test the tool on three species of non-human primates. In the modern human subsample, Icex allowed us to perform a preliminary analysis on the absolute and relative expansion of cranial sinuses and pneumatisations during growth. The performance of Icex, applied to diverse crania, shows the potential for an extensive evaluation of the developmental and/or evolutionary significance of hollow cranial structures. Furthermore, being open source, Icex is a fully customisable tool, easily applicable to other taxa and skeletal regions.

A preliminary analysis of replicating the biomechanics of helmet therapy for sagittal craniosynostosis.

PURPOSE: The aim of this study was to investigate the biomechanics of endoscopically assisted strip craniectomy treatment for the management of sagittal craniosynostosis while undergoing three different durations of postoperative helmet therapy using a computational approach. METHODS: A previously developed 3D model of a 4-month-old sagittal craniosynostosis patient was used. The strip craniectomy incisions were replicated across the segmented parietal bones. Areas across the calvarial were selected and constrained to represent the helmet placement after surgery. Skull growth was modelled and three variations of helmet therapy were investigated, where the timings of helmet removal alternated between 2, 5, and 8 months after surgery. RESULTS: The predicted outcomes suggest that the prolonging of helmet placement has perhaps a beneficial impact on the postoperative long-term morphology of the skull. No considerable difference was found on the pattern of contact pressure at the interface of growing intracranial volume and the skull between the considered helmeting durations. CONCLUSION: Although the validation of these simulations could not be performed, these simulations showed that the duration of helmet therapy after endoscopically assisted strip craniectomy influenced the cephalic index at 36 months. Further studies require to validate these preliminary findings yet this study can lay the foundations for further studies to advance our fundamental understanding of mechanics of helmet therapy.

Chiari malformation type I surgery in children: French multicenter 10-year cohort.

OBJECTIVE: Chiari malformation type I (CM-I) is frequent in children and remains a surgical challenge. Several techniques have been described for posterior fossa decompression. No decision algorithm has been validated, and strategies are highly variable between institutions. The goal of this study was to define therapeutic guidelines that take into consideration patient specificities. METHODS: The authors retrospectively collected data from patients who were < 18 years of age, were diagnosed with CM-I, and were treated surgically between 2008 and 2018 in 8 French pediatric neurosurgical centers. Data on clinical features, morphological parameters, and surgical techniques were collected. Clinical outcomes at 3 and 12 months after surgery were assessed by the Chicago Chiari Outcome Scale. The authors used a hierarchical clustering method to define clusters of patients by considering their anatomical similarities, and then compared outcomes between surgical strategies in each of these clusters. RESULTS: Data from 255 patients were collected. The mean age at surgery was 9.6 ± 5.0 years, syringomyelia was reported in 60.2% of patients, the dura mater was opened in 65.0% of patients, and 17.3% of patients underwent a redo surgery for additional treatment. The mean Chicago Chiari Outcome Scale score was 14.4 ± 1.5 at 3 months (n = 211) and 14.6 ± 1.9 at 12 months (n = 157). The hierarchical clustering method identified three subgroups with potentially distinct mechanisms underlying tonsillar herniation: bony compression, basilar invagination, and foramen magnum obstruction. Each cluster matched with specific outcomes. CONCLUSIONS: This French multicenter retrospective cohort study enabled the identification of three subgroups among pediatric patients who underwent surgery for CM-I, each of which was associated with specific outcomes. This morphological classification of patients might help in understanding the underlying mechanisms and providing personalized treatment.

A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis.

The neonate skull consists of several bony plates, connected by fibrous soft tissue called sutures. Premature fusion of sutures is a medical condition known as craniosynostosis. Sagittal synostosis, caused by premature fusion of the sagittal suture, is the most common form of this condition. The optimum management of this condition is an ongoing debate in the craniofacial community while aspects of the biomechanics and mechanobiology are not well understood. Here, we describe a computational framework that enables us to predict and compare the calvarial growth following different reconstruction techniques for the management of sagittal synostosis. Our results demonstrate how different reconstruction techniques interact with the increasing intracranial volume. The framework proposed here can be used to inform optimum management of different forms of craniosynostosis, minimising the risk of functional consequences and secondary surgery.

Secondary Le Fort III after Early Fronto-Facial Monobloc Normalizes Sleep Apnea in Faciocraniosynostosis: A Cohort Study.

BACKGROUND: This study aims to assess the improvement of sleep apnea after secondary Le Fort III facial advancement with distraction (LF3) in faciocraniosynostosis (FCS) patients with sleep apnea who have previously undergone fronto-facial monobloc advancement (FFMBA) with distraction. METHODS: Patients having undergone secondary LF3 were selected from a cohort of FCS patients with documented sleep apnea who had previously undergone fronto-facial monobloc advancement. Patient charts and polysomnographic records were reviewed. Apnea-hypopnea index (AHI) was recorded before and at least 6 months after secondary LF3. The primary outcome was normalization of AHI (less than 5/h was considered normal). Hierarchical multilevel analysis was performed to predict postoperative AHI evolution. RESULTS: Seventeen patients underwent a secondary LF3, 7.0 ± 3.9 years after the primary FFMBA. The mean age was 9.6 ± 3.9 years. A total of 15 patients (88%) normalized their AHI. Two of four patients were decannulated (50%). There was a statistically significant decrease in AHI (preoperative AHI 21.5/h vs. 3.9/h postoperatively, p=0.003). Hierarchic multilevel modeling showed progressive AHI decrease postoperatively. CONCLUSION: Secondary LF3 improves residual or relapsing sleep apnea in FCS patients who have previously had FFMBA.

Growth patterns and shape development of the paediatric mandible - A 3D statistical model.

Background/aim: To develop a 3D morphable model of the normal paediatric mandible to analyse shape development and growth patterns for males and females. Methods: Computed tomography (CT) data was collected for 242 healthy children referred for CT scan between 2011 and 2018 aged between 0 and 47 months (mean, 20.6 ± 13.4 months, 59.9% male). Thresholding techniques were used to segment the mandible from the CT scans. All mandible meshes were annotated using a defined set of 52 landmarks and processed such that all meshes followed a consistent triangulation. Following this, the mandible meshes were rigidly aligned to remove translation and rotation effects, while size effects were retained. Principal component analysis (PCA) was applied to the processed meshes to construct a generative 3D morphable model. Partial least squares (PLS) regression was also applied to the processed data to extract the shape modes with which to evaluate shape differences for age and sex. Growth curves were constructed for anthropometric measurements. Results: A 3D morphable model of the paediatric mandible was constructed and validated with good generalisation, compactness, and specificity. Growth curves of the assessed anthropometric measurements were plotted without significant differences between male and female subjects. The first principal component was dominated by size effects and is highly correlated with age at time of scan (Spearman's r = 0.94, p < 0.01). As with PCA, the first extracted PLS mode captures much of the size variation within the dataset and is highly correlated with age (Spearman's r = -0.94, p < 0.01). Little correlation was observed between extracted shape modes and sex with either PCA or PLS for this study population. Conclusion: The presented 3D morphable model of the paediatric mandible enables an understanding of mandibular shape development and variation by age and sex. It allowed for the construction of growth curves, which contains valuable information that can be used to enhance our understanding of various disorders that affect the mandibular development. Knowledge of shape changes in the growing mandible has potential to improve diagnostic accuracy for craniofacial conditions that impact the mandibular morphology, objective evaluation, surgical planning, and patient follow-up.

FGFR3 overactivation in the brain is responsible for memory impairments in Crouzon syndrome mouse model.

Crouzon syndrome with acanthosis nigricans (CAN, a rare type of craniosynostosis characterized by premature suture fusion and neurological impairments) has been linked to a gain-of-function mutation (p.Ala391Glu) in fibroblast growth factor receptor 3 (FGFR3). To characterize the CAN mutation's impact on the skull and on brain functions, we developed the first mouse model (Fgfr3A385E/+) of this syndrome. Surprisingly, Fgfr3A385E/+ mice did not exhibit craniosynostosis but did show severe memory impairments, a structurally abnormal hippocampus, low activity-dependent synaptic plasticity, and overactivation of MAPK/ERK and Akt signaling pathways in the hippocampus. Systemic or brain-specific pharmacological inhibition of FGFR3 overactivation by BGJ398 injections rescued the memory impairments observed in Fgfr3A385E/+ mice. The present study is the first to have demonstrated cognitive impairments associated with brain FGFR3 overactivation, independently of skull abnormalities. Our results provide a better understanding of FGFR3's functional role and the impact of its gain-of-function mutation on brain functions. The modulation of FGFR3 signaling might be of value for treating the neurological disorders associated with craniosynostosis.

Management of sagittal craniosynostosis: morphological comparison of eight surgical techniques.

The aim of this study was to carry out a retrospective multicentre study comparing the morphological outcome of 8 techniques used for the management of sagittal synostosis versus a large cohort of control patients. Computed tomographic (CT) images were obtained from children CT-scanned for non-craniosynostosis related events (n = 241) and SS patients at preoperative and postoperative follow-up stages (n = 101). No significant difference in morphological outcomes was observed between the techniques considered in this study. However, the majority of techniques showed a tendency for relapse. Further, the more invasive procedures at older ages seem to lead to larger intracranial volume compared to less invasive techniques at younger ages. This study can be a first step towards future multicentre studies, comparing surgical results and offering a possibility for objective benchmarking of outcomes between methods and centres.

Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis.

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. They are powerful tools for photographic analysis but 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. 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). 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 make it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting.

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.

Predicting and comparing three corrective techniques for sagittal craniosynostosis.

Sagittal synostosis is the most occurring form of craniosynostosis, resulting in calvarial deformation and possible long-term neurocognitive deficits. Several surgical techniques have been developed to correct these issues. Debates as to the most optimal approach are still ongoing. Finite element method is a computational tool that's shown to assist with the management of craniosynostosis. The aim of this study was to compare and predict the outcomes of three reconstruction methods for sagittal craniosynostosis. Here, a generic finite element model was developed based on a patient at 4 months of age and was virtually reconstructed under all three different techniques. Calvarial growth was simulated to predict the skull morphology and the impact of different reconstruction techniques on the brain growth up to 60 months of age. Predicted morphology was then compared with in vivo and literature data. Our results show a promising resemblance to morphological outcomes at follow up. Morphological characteristics between considered techniques were also captured in our predictions. Pressure outcomes across the brain highlight the potential impact that different techniques have on growth. This study lays the foundation for further investigation into additional reconstructive techniques for sagittal synostosis with the long-term vision of optimizing the management of craniosynostosis.

Using Sensitivity Analysis to Develop a Validated Computational Model of Post-operative Calvarial Growth in Sagittal Craniosynostosis.

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 in vivo data from the same patient. Sensitivity tests to the choice of various material properties highlighted that the defined elastic modulus for the craniotomies appears to have the greatest influence on the predicted overall skull morphology. The bone formation modeling approach across the sutures/craniotomies had a considerable impact on the level of contact pressure across the brain with minimum impact on the overall predicated morphology of the skull. Including the effect of CSF (based on the approach adopted here) displayed only a slight reduction in brain pressure outcomes. The sensitivity tests performed in this study set the foundation for future comparative studies using FE method to compare outcomes of different reconstruction techniques for the management of craniosynostosis.