Impact of the COVID-19 Pandemic Regulations on the Health Status and Medical Care of Children with Trisomy 21.

BACKGROUND: While children are considered at low risk for COVID-19, little is known about the impact of SARS-CoV-2 on paediatric risk patients like children with Trisomy 21 (T21). As these children often need regular therapy and various medical appointments, this study aimed to investigate the possible impact of the COVID-19 pandemic on children with T21. PATIENTS AND METHODS: Parents of children with T21 in the age of 0-12 years in Saxony-Anhalt were interviewed via phone in June 2021 regarding the health status and medical care of their children during the past 15 months of pandemic. RESULTS: 37 children with mean age of 6.1 years (min 0; max 12) were included in the study. The majority did not have any additional congenital anomalies. Surveyed parents hardly reported adverse changes of health status during the pandemic, but rather improvements, such as decreased number of respiratory infections and more time spend with their children. Outpatient appointments and therapy were cancelled or postponed at the onset of the pandemic, but parents reported low impact on their child's health and development. The main concern seemed to be lack of childcare during school and day-care closures and uncertainty concerning possible health impacts of an infection on their children. CONCLUSION: There was low impact of the COVID-19 pandemic on health and medical care of children with T21 in our study population. Further research is needed to help weigh the child's individual risk of infection against the need for medical treatment and therapy when dealing with paediatric risk patients.

Information needs of parents of children with congenital anomalies across Europe: a EUROlinkCAT survey.

BACKGROUND: Parents of children who have a congenital anomaly can experience significant worry about their child's health. Access to clear, helpful, and trustworthy information can provide a valuable source of support. In this study the aim was to explore the information needs of parents/carers of children with congenital anomalies across Europe. METHOD: A cross-sectional online survey was developed in nine languages to measure parents' information needs, including: (1) the 'helpfulness'/'trustworthiness' of information received from eight relevant sources, and (2) overall satisfaction with information received. Parents/carers of children (0-10 years) with cleft lip, spina bifida, congenital heart defect [CHD] requiring surgery, and/or Down syndrome were recruited online via relevant organisations in 10 European countries from March-July 2021. Quantitative analyses using multivariable logistic regressions were performed. RESULTS: One thousand seventy parents/carers of children with a cleft lip (n = 247), spina bifida (n = 118), CHD (n = 366), Down syndrome (n = 281), and Down syndrome with CHD (n = 58) were recruited in Poland (n = 476), the UK (n = 120), Germany (n = 97), the Netherlands/Belgium (n = 74), Croatia (n = 68), Italy (n = 59), other European countries (n = 92), and not specified/non-European countries (n = 84). Most participants were mothers (92%) and aged 31-40 years (71%). Participants were most likely to rate support groups (63%), patient organisations (60%), specialist doctors/nurses (58%), and social media (57%) as 'very helpful' information sources. 'Very trustworthy' ratings remained high for specialist doctors/nurses (61%), however, they declined for support groups (47%), patient organisations (48%), and social media (35%). Germany had the highest proportion of participants who were 'very satisfied' (44%, 95% CI = 34%-54%) with information, whereas this percentage was lowest in Croatia (11%, 95% CI = 3%-19%) and Poland (15%, 95% CI = 11%-18%). Parents of children with Down syndrome had significantly lower satisfaction ratings than parents of children with CHD; 13% (95% CI = 8%-18%) reported being 'very satisfied' compared to 28% (95% CI = 23%-33%) in the CHD group. CONCLUSIONS: Findings suggest that informal sources of information (e.g. support groups) are of value to parents, however, they are not deemed as trustworthy as specialist medical sources. Satisfaction ratings differed across countries and by anomaly, and were particularly low in Croatia and Poland, as well as for parents of children with Down syndrome, which warrants further investigation.

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management.

PURPOSE: Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of intracranial aneurysm rupture is simplified and might be unreliable, especially for patients with multiple aneurysms. Clinical research proposed more advanced analysis of intracranial aneurysm, but requires many complex preprocessing steps. Advanced tools for automatic aneurysm analysis are needed to transfer current research into clinical routine. METHODS: We propose a pipeline for intracranial aneurysm analysis using deep learning-based mesh segmentation, automatic centerline and outlet detection and automatic generation of a semantic vessel graph. We use the semantic vessel graph for morphological analysis and an automatic rupture state classification. RESULTS: The deep learning-based mesh segmentation can be successfully applied to aneurysm surface meshes. With the subsequent semantic graph extraction, additional morphological parameters can be extracted that take the whole vascular domain into account. The vessels near ruptured aneurysms had a slightly higher average torsion and curvature compared to vessels near unruptured aneurysms. The 3D surface models can be further employed for rupture state classification which achieves an accuracy of 83.3%. CONCLUSION: The presented pipeline addresses several aspects of current research and can be used for aneurysm analysis with minimal user effort. The semantic graph representation with automatic separation of the aneurysm from the parent vessel is advantageous for morphological and hemodynamical parameter extraction and has great potential for deep learning-based rupture state classification.

Multimodal exploration of the intracranial aneurysm wall.

PURPOSE: Intracranial aneurysms (IAs) are pathological changes of the intracranial vessel wall, although clinical image data can only show the vessel lumen. Histology can provide wall information but is typically restricted to ex vivo 2D slices where the shape of the tissue is altered. METHODS: We developed a visual exploration pipeline for a comprehensive view of an IA. We extract multimodal information (like stain classification and segmentation of histologic images) and combine them via 2D to 3D mapping and virtual inflation of deformed tissue. Histological data, including four stains, micro-CT data and segmented calcifications as well as hemodynamic information like wall shear stress (WSS), are combined with the 3D model of the resected aneurysm. RESULTS: Calcifications were mostly present in the tissue part with increased WSS. In the 3D model, an area of increased wall thickness was identified and correlated to histology, where the Oil red O (ORO) stained images showed a lipid accumulation and the alpha-smooth muscle actin (aSMA) stained images showed a slight loss of muscle cells. CONCLUSION: Our visual exploration pipeline combines multimodal information about the aneurysm wall to improve the understanding of wall changes and IA development. The user can identify regions and correlate how hemodynamic forces, e.g. WSS, are reflected by histological structures of the vessel wall, wall thickness and calcifications.

MedmeshCNN - Enabling meshcnn for medical surface models.

BACKGROUND AND OBJECTIVE: MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. It outperformed state-of-the-art methods in classification and segmentation tasks of popular benchmarking datasets. The medical domain provides a large amount of complex 3D surface models that may benefit from processing with MeshCNN. However, several limitations prevent outstanding performances on highly diverse medical surface models. Within this work, we propose MedMeshCNN as an expansion dedicated to complex, diverse, and fine-grained medical data. METHODS: MedMeshCNN follows the functionality of MeshCNN with a significantly increased memory efficiency that allows retaining patient-specific properties during processing. Furthermore, it enables the segmentation of pathological structures that often come with highly imbalanced class distributions. RESULTS: MedMeshCNN achieved an Intersection over Union of 63.24% on a highly complex part segmentation task of intracranial aneurysms and their surrounding vessel structures. Pathological aneurysms were segmented with an Intersection over Union of 71.4%. CONCLUSIONS: MedMeshCNN enables the application of MeshCNN on complex, fine-grained medical surface meshes. It considers imbalanced class distributions derived from pathological findings and retains patient-specific properties during processing.

Complex wall modeling for hemodynamic simulations of intracranial aneurysms based on histologic images.

PURPOSE: For the evaluation and rupture risk assessment of intracranial aneurysms, clinical, morphological and hemodynamic parameters are analyzed. The reliability of intracranial hemodynamic simulations strongly depends on the underlying models. Due to the missing information about the intracranial vessel wall, the patient-specific wall thickness is often neglected as well as the specific physiological and pathological properties of the vessel wall. METHODS: In this work, we present a model for structural simulations with patient-specific wall thickness including different tissue types based on postmortem histologic image data. Images of histologic 2D slices from intracranial aneurysms were manually segmented in nine tissue classes. After virtual inflation, they were combined into 3D models. This approach yields multiple 3D models of the inner and outer wall and different tissue parts as a prerequisite for subsequent simulations. RESULT: We presented a pipeline to generate 3D models of aneurysms with respect to the different tissue textures occurring in the wall. First experiments show that including the variance of the tissue in the structural simulation affect the simulation result. Especially at the interfaces between neighboring tissue classes, the larger influence of stiffer components on the stability equilibrium became obvious. CONCLUSION: The presented approach enables the creation of a geometric model with differentiated wall tissue. This information can be used for different applications, like hemodynamic simulations, to increase the modeling accuracy.

Interactive exploration of a 3D intracranial aneurysm wall model extracted from histologic slices.

PURPOSE: Currently no detailed in vivo imaging of the intracranial vessel wall exists. Ex vivo histologic images can provide information about the intracranial aneurysm (IA) wall composition that is useful for the understanding of IA development and rupture risk. For a 3D analysis, the 2D histologic slices must be incorporated in a 3D model which can be used for a spatial evaluation of the IA's morphology, including analysis of the IA neck. METHODS: In 2D images of histologic slices, different wall layers were manually segmented and a 3D model was generated. The nuclei were automatically detected and classified as round or elongated, and a neural network-based wall type classification was performed. The information was combined in a software prototype visualization providing a unique view of the wall characteristics of an IA and allowing interactive exploration. Furthermore, the heterogeneity (as variance of the wall thickness) of the wall was evaluated. RESULT: A 3D model correctly representing the histologic data was reconstructed. The visualization integrating wall information was perceived as useful by a medical expert. The classification produces a plausible result. CONCLUSION: The usage of histologic images allows to create a 3D model with new information about the aneurysm wall. The model provides information about the wall thickness, its heterogeneity and, when performed on cadaveric samples, includes information about the transition between IA neck and sac.

Semiautomatic neck curve reconstruction for intracranial aneurysm rupture risk assessment based on morphological parameters.

PURPOSE: Morphological parameters of intracranial aneurysms (IAs) are well established for rupture risk assessment. However, a manual measurement is error-prone, not reproducible and cumbersome. For an automatic extraction of morphological parameters, a 3D neck curve reconstruction approach to delineate the aneurysm from the parent vessel is required. METHODS: We present a 3D semiautomatic aneurysm neck curve reconstruction for the automatic extraction of morphological parameters which was developed and evaluated with an experienced neuroradiologist. We calculate common parameters from the literature and include two novel angle-based parameters: the characteristic dome point angle and the angle difference of base points. RESULTS: We applied our method to 100 IAs acquired with rotational angiography in clinical routine. For validation, we compared our approach to manual segmentations yielding highly significant correlations. We analyzed 95 of these datasets regarding rupture state. Statistically significant differences were found in ruptured and unruptured groups for maximum diameter, maximum height, aspect ratio and the characteristic dome point angle. These parameters were also found to statistically significantly correlate with each other. CONCLUSIONS: The new 3D neck curve reconstruction provides robust results for all datasets. The reproducibility depends on the vessel tree centerline and the user input for the initial dome point and parameters characterizing the aneurysm neck region. The characteristic dome point angle as a new metric regarding rupture risk assessment can be extracted. It requires less computational effort than the complete neck curve reconstruction.