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Abstract 3D ultrasound imaging of fetal faces has been predominantly confined to qualitative assessment. Many genetic conditions evade diagnosis and identification could assist with parental counselling, pregnancy management and neonatal care planning. We describe a methodology to build a shape model of the third trimester fetal face from 3D ultrasound and show how it can objectively describe morphological features and gestational-age related changes of normal fetal faces. 135 fetal face 3D ultrasound volumes (117 appropriately grown, 18 growth-restricted) of 24-34 weeks gestation were included. A 3D surface model of each face was obtained using a semi-automatic segmentation workflow. Size normalisation and rescaling was performed using a growth model giving the average size at every gestation. The model demonstrated a similar growth rate to standard head circumference reference charts. A landmark-free morphometry model was estimated to characterize shape differences using non-linear deformations of an idealized template face. Advancing gestation is associated with widening/fullness of the cheeks, contraction of the chin and deepening of the eyes. Fetal growth restriction is associated with a smaller average facial size but no morphological differences. This model may eventually be used as a reference to assist in the prenatal diagnosis of congenital anomalies with characteristic facial dysmorphisms.

Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86-0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60-15.30%) and 9.90% (IQR: 8.47-11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment.

Abstract Spring-assisted posterior vault expansion has been adopted at the London Great Ormond Street Hospital for Children to treat raised intracranial pressure in patients affected by syndromic craniosynostosis, a congenital calvarial anomaly causing the premature fusion of skull sutures. This procedure involves elastic distractors used to dynamically reshape the skull and increase the intracranial volume (ICV). In this study, we developed and validated a patient-specific model able to predict the ICV increase and carried out a parametric study to investigate the effect of surgical parameters on that final volume. Pre- and post-operative computed tomography data relative to 18 patients were processed to extract simplified patient-specific skull shape, replicate surgical cuts, and simulate spring expansion. A parametric study was performed to quantify each parameter’s impact on the surgical outcome: for each patient, the osteotomy location was varied in a pre-defined range; local sensitivity of the predicted ICV to each parameter was analysed and compared. Results showed that the finite element model performed well in terms of post-operative ICV prediction and allowed for parametric optimization of surgical cuts. The study indicates how to optimize the ICV increase according to the type of procedure and provides indication on the most robust surgical strategy.

Background and Objectives: Spring-assisted surgery is a popular option for the treatment of non-syndromic craniosynostosis. The main drawback of this procedure is the need for a second surgery for spring removal, which could be avoided if a distractor material could be metabolised over time. Iron–Manganese alloys (FeMn) have a good trade-off between degradation rate and strength; however, their biocompatibility is still debated. Materials and Methods: In this study, the neuro-compatibility of Fe-20Mn (wt.%) was assessed using standard assays. PC-12 cells were exposed to Fe-20Mn (wt.%) and stainless steel via indirect contact. To examine the cytotoxicity, a Cell Tox Green assay was carried out after 1, 2, and 3 days of incubation. Following differentiation, a neurite morphological examination after 1 and 7 days of incubation time was carried out. The degradation response in modified Hank’s solution at 1, 3, and 7 days was investigated, too. Results: The cytotoxicity assay showed a higher toxicity of Fe-20Mn than stainless steel at earlier time points; however, at the latest time point, no differences were found. Neurite morphology was similar for cells exposed to Fe-20Mn and stainless steel. Conclusions: In conclusion, the Fe-20Mn alloy shows promising neuro-compatibility. Future studies will focus on in vivo studies to confirm the cellular response to Fe-20Mn.

Abstract Background Computational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, typically obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. However, segmentation is time-consuming and requires expert input. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries for CFD studies. Methods 90 CHD patients were retrospectively selected for this study. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and pulmonary artery labels. These were used to train and optimize a U-Net model, using a 70-10-10 train-validation-test split. Segmentation performance was primarily evaluated using Dice score. CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Mean pressure and velocity fields across 99 planes along the vessel centrelines were extracted, and a mean average percentage error (MAPE) was calculated for each vessel pair (ML vs GT). A second observer (SO) segmented the test dataset for assessment of inter-observer variability. Friedman tests were used to compare ML vs GT, SO vs GT and ML vs SO metrics, and pressure/velocity field errors. Results The network’s Dice score (ML vs GT) was 0.945 (interquartile range: 0.929–0.955) for the aorta and 0.885 (0.851–0.899) for the pulmonary arteries. Differences with the inter-observer Dice score (SO vs GT) and ML vs SO Dice scores were not statistically significant for either aorta or pulmonary arteries (p = 0.741, p = 0.061). The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% (8.5–15.7%) and 4.1% (3.1–6.9%), respectively, and for the pulmonary arteries 14.6% (11.5–23.2%) and 6.3% (4.3–7.9%), respectively. Inter-observer (SO vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to ML vs GT (p > 0.2). Conclusions ML can successfully segment the great vessels for CFD, with errors similar to inter-observer variability. This fast, automatic method reduces the time and effort needed for CFD analysis, making it more attractive for routine clinical use.

ABSTRACT: Temporal indentations are the most impacting craniofacial complication after coronal flap dissection. It is mainly due to a temporal fat pad or temporalis muscle dissection.Because of the great improvements achieved recently in CAD-CAM-aided surgery and the possibility of performing accurate pre-surgical virtual planning, it is now possible to correct it with a customised virtual approach. Furthermore, advancements in material science have allowed surgeons to rely on biocompatible materials like PEEK (showing a low complication and recurrence rate) for the manufacturing of patient-specific implants.We hereby describe our experience on a case of secondary and corrective surgery after a fronto-orbital remodelling, in which we used PEEK implants designed by CAD and optimized by finite element modelling.

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy.

In silico medicine describes the application of computational modelling and simulation (CM&S) to the study, diagnosis, treatment or prevention of a disease. Tremendous research advances have been achieved to facilitate the use of CM&S in clinical applications. Nevertheless, the uptake of CM&S in clinical practice is not always timely and accurately reflected in the literature. A clear view on the current awareness, actual usage and opinions from the clinicians is needed to identify barriers and opportunities for the future of in silico medicine. The aim of this study was capturing the state of CM&S in clinics by means of a survey toward the clinical community. Responses were collected online using the Virtual Physiological Human institute communication channels, engagement with clinical societies, hospitals and individual contacts, between 2020 and 2021. Statistical analyses were done with R. Participants (n = 163) responded from all over the world. Clinicians were mostly aged between 35 and 64 years-old, with heterogeneous levels of experience and areas of expertise (i.e., 48% cardiology, 13% musculoskeletal, 8% general surgery, 5% paediatrics). The CM&S terms “Personalised medicine” and “Patient-specific modelling” were the most well-known within the respondents. “In silico clinical trials” and “Digital Twin” were the least known. The familiarity with different methods depended on the medical specialty. CM&S was used in clinics mostly to plan interventions. To date, the usage frequency is still scarce. A well-recognized benefit associated to CM&S is the increased trust in planning procedures. Overall, the recorded level of trust for CM&S is high and not proportional to awareness level. The main barriers appear to be access to computing resources, perception that CM&S is slow. Importantly, clinicians see a role for CM&S expertise in their team in the future. This survey offers a snapshot of the current situation of CM&S in clinics. Although the sample size and representativity could be increased, the results provide the community with actionable data to build a responsible strategy for accelerating a positive uptake of in silico medicine. New iterations and follow-up activities will track the evolution of responses over time and contribute to strengthen the engagement with the medical community.

Abstract 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.

BackgroundHaemodialysis is a life-saving treatment for children with kidney failure. The majority of children have haemodialysis through central venous lines (CVLs). The use of CVLs in pediatric patients is often associated to complications which can lead to their replacement. The aim of this study is to investigate haemodynamics of pediatric CVLs to highlight the criticalities of different line designs.MethodsFour models of CVLs for pediatric use were included in this study. The selected devices varied in terms of design and sizes (from 6.5 Fr to 14 Fr). Accurate 3D models of CVLs were reconstructed from high-resolution images including venous and arterial lumens, tips and side holes. Computational fluid dynamics (CFD) analyses were carried out to simulate pediatric working conditions of CVLs in ideal and anatomically relevant conditions.ResultsThe arterial lumens of all tested CVLs showed the most critical conditions with the majority of blood flowing through the side-holes. A zone of low flow was identified at the lines’ tip. The highest shear stresses distribution (>10 Pa) was found in the 8 Fr line while the highest platelet lysis index in the 10 Fr model. The analysis on the anatomical geometry showed an increase in wall shear stress measured in the 10 F model compared to the idealised configuration. Similarly, in anatomical models an increased disturbance and velocity of the flow was found inside the vein after line placement.ConclusionThis study provided a numerical characterization of fluid dynamics in pediatric CVLs highlighting performance criticalities (i.e. high shear stresses and areas of stagnation) associated to specific sizes (8 Fr and 10 Fr) and conditions (i.e. anatomical test).

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

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

The hemodynamic environment of the pulmonary bifurcation is of great importance for adult patients with repaired tetralogy of Fallot (rTOF) due to possible complications in the pulmonary valve and narrowing of the left pulmonary artery (LPA). The aim of this study was to computationally investigate the effect of geometrical variability and flow split on blood flow characteristics in the pulmonary trunk of patient-specific models. Data from a cohort of seven patients was used retrospectively and the pulmonary hemodynamics was investigated using averaged and MRI-derived patient-specific boundary conditions on the individualized models, as well as a statistical mean geometry. Geometrical analysis showed that curvature and tortuosity are higher in the LPA branch, compared to the right pulmonary artery (RPA), resulting in complex flow patterns in the LPA. The computational analysis also demonstrated high time-averaged wall shear stress (TAWSS) at the outer wall of the LPA and the wall of the RPA proximal to the junction. Similar TAWSS patterns were observed for averaged boundary conditions, except for a significantly modified flow split assigned at the outlets. Overall, this study enhances our understanding about the flow development in the pulmonary bifurcation of rTOF patients and associates some morphological characteristics with hemodynamic parameters, highlighting the importance of patient-specificity in the models. To confirm these findings, further studies are required with a bigger cohort of patients.

Abnormal aortic arches (AAAs) cover a spectrum of malformations, including abnormal laterality, branching patterns, and flow-limiting narrowing, which themselves vary from tubular hypoplasia, through discrete coarctation, to complete interruption of the arch. Neonatal surgery within the first days of life is necessary for most of these morphologies. Patch aortoplasty is widely used as it can offer a good haemodynamic result, being tailored to each combination of presenting pathologies. Our study hypothesis was that arch malformations are frequent in DORV and exhibit a plethora of phenotypes. We reviewed 54 post-mortem heart specimens from the UCL Cardiac Archive, analysing morphological features that would potentially influence the surgical repair, and taking relevant measurements of surgical importance. AAAs were found in half of the specimens, including 22.2% with aortic arch narrowing. In total, 70% and 30% of narrow arches had a subpulmonary and subaortic interventricular defect, respectively. Z-scores were significantly negative for all cases with tubular hypoplasia. We concluded that arch malformations are a common finding among hearts with DORV. Surgery on the neonatal aortic arch in DORV, performed in conjunction with other interventions that aim to balance pulmonary to systemic flow (Qp/Qs), should be anticipated and form an important part of multi-modal imaging.

Arhinia (congenital absence of the nose) is a congenital rare disease, which has been reported in less than 60 cases in the literature. It consists of the absence of external nose, nasal cavities and olfactory apparatus and is generally associated with midline defects, microphthalmia, blepharophimosis and hypotelorism. Aesthetic problems as well as associated functional anomalies can potentially impact on the development and interpersonal relationships of the child at a later stage in life.Arhinia requires extensive management in early life in order to ensure airway patency and protection by means of tracheostomy, and to allow adequate pharyngeal and feeding function to the child. Aesthetic issues are managed with reconstructive surgery or an external prosthesis. There is no previous description in Literature of internal prosthetic devices used to sequentially shape soft tissues in complex reconstruction.We present an example of design and manufacturing of a bespoke nose implant produced by means of 3D printing and directly assessed on-table by means of 3D surface scanning. Keywords: Congenital arhinia, 3D priting, 3D scanning, Patient specific implant

During Atrial Fibrillation (AF) more than 90% of the left atrial thrombi responsible for thromboembolic events originate in the left atrial appendage (LAA), a complex small sac protruding from the left atrium (LA). Current available treatments to prevent thromboembolic events are oral anticoagulation, surgical LAA exclusion, or percutaneous LAA occlusion. However, the mechanism behind thrombus formation in the LAA is poorly understood. The aim of this work is to analyse the hemodynamic behaviour in four typical LAA morphologies - “Chicken wing”, “Cactus”, “Windsock” and “Cauliflower” - to identify potential relationships between the different shapes and the risk of thrombotic events. Computerised tomography (CT) images from four patients with no LA pathology were segmented to derive the 3D anatomical shape of LAA and LA. Computational Fluid Dynamic (CFD) analyses based on the patient-specific anatomies were carried out imposing both healthy and AF flow conditions. Velocity and shear strain rate (SSR) were analysed for all cases. Residence time in the different LAA regions was estimated with a virtual contrast agent washing out. CFD results indicate that both velocity and SSR decrease along the LAA, from the ostium to the tip, at each instant in the cardiac cycle, thus making the LAA tip more prone to fluid stagnation, and therefore to thrombus formation. Velocity and SSR also decrease from normal to AF conditions. After four cardiac cycles, the lowest washout of contrast agent was observed for the Cauliflower morphology (3.27% of residual contrast in AF), and the highest for the Windsock (0.56% of residual contrast in AF). This suggests that the former is expected to be associated with a higher risk of thrombosis, in agreement with clinical reports in the literature. The presented computational models highlight the major role played by the LAA morphology on the hemodynamics, both in normal and AF conditions, revealing the potential support that numerical analyses can provide in the stratification of patients under risk of thrombus formation, towards personalised patient care.

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.

Background: Bicuspid aortic valve (BAV) is a congenital heart disease associated with aortic wall abnormalities and co-existing with other congenital defects (e.g. aortic coarctation). This study aimed to explore aortic shape features in a BAV population, identifying sub-groups with different aortic morphologies. Methods: Single-centre retrospective study. Patients with an MRI scan and native BAV diagnosis between 2011 and 16 were studied (n = 525); those with a 3D MRI dataset were included for shape analysis (n = 108, 64% males, 38 ± 16.5 years). MRI-derived 3D aortic reconstructions were analysed using a statistical shape modelling framework [1]. A mean aortic shape (‘template’) was computed and shape deformations were correlated with demographic, volumetric and functional data. Results: Aortic coarctation (n = 71) was significantly associated with a more gothic arch (p = 0.02), more tubular ascending aorta and descending aorta dilation (p < 0.001). Also, smaller aortic size in patients with coarctation was associated with the younger age of this group (33 ± 13 vs. 47 ± 19, p < 0.001), given the overall relationship between aortic size and age (p < 0.001). Aortic stenosis (n = 30) was also associated with gothic arch (p = 0.01), and dilated ascending aorta but with no aortic root dilation (p = 0.02). On multivariate regression analysis, gothic arch was indeed associated with coarctation and stenosis, and also with non-coronary valve fusion pattern (p = 0.03). Patients with aortic regurgitation tended to have larger aortas (p = 0.005). Conclusion: The presence of aortic coarctation and stenosis may influence the amount of dilation and the overall arch architecture in BAV patients. Patients with BAV present profoundly different morphological phenotypes depending on the presence/absence of aortic coarctation (Fig. 1). Figure 1Shape features of coarctation (CoA) vs no CoA in BAY population. A) The ‘template’ (or average shape) for patients with CoA on the left, and patients without CoA on the right. B) Patients with CoA have tubular ascending aortas (left), while patients without CoA tend to have increased ascending aortic dilation (right). C) Patients with CoA have more a gothic arch (left), whereas patients without CoA have a rounder arch (right).