Strain rate dependence of internal pressure and external bulge in human intervertebral discs during axial compression

Intervertebral discs (IVDs) lie between vertebrae of the spine. An understanding of their material behavior is of interest for research studies associated with injuries sustained during vehicle accidents, airplane ejections, sports injuries, and under-body blasts, and is particularly important for the development of biofidelic finite element (FE) models of these injurious events. The accuracy of such FE models depends on appropriate characterization of the material properties. The IVD consists of the anulus fibrosus (AF), which surrounds the nucleus pulposus (NP) and is encapsulated, above and below, by cartilage endplates (CEPs). The AF consists of 15–25 concentric layers, and each layer consists of collagen fiber bundles that have an orientation of approximately ±30 degrees to the transverse plane. The boundaries between the AF, the NP and the CEP are often unclear, which makes separating components of the IVD for mechanical testing a challenging task. The authors have previously used an inverse FE approach to obtain material properties of these components in bovine specimens; this approach involves obtaining subject-specific geometry of the whole IVD and optimizing individual components to ensure a close match between the experimental data and the numerical results. The aim of this study was to characterize experimentally the response of four human IVDs across strain rates. The results will be used to obtain material properties of the constituent components using an inverse FE approach

Harmonization Strategies in Multicenter MRI-Based Radiomics

Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process

A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains

Virtual population generation is an emerging field in data science with numerous applications in healthcare towards the augmentation of clinical research databases with significant lack of population size. However, the impact of data augmentation on the development of AI (artificial intelligence) models to address clinical unmet needs has not yet been investigated. In this work, we assess whether the aggregation of real with virtual patient data can improve the performance of the existing risk stratification and disease classification models in two rare clinical domains, namely the primary Sjo & uml;gren’s Syndrome (pSS) and the hypertrophic cardiomyopathy (HCM), for the first time in the literature. To do so, multivariate approaches, such as, the multivariate normal distribution (MVND), and straightforward ones, such as, the Bayesian networks, the artificial neural networks (ANNs), and the tree ensembles are compared against their performance towards the generation of high-quality virtual data. Both boosting and bagging algorithms, such as, the Gradient boosting trees (XGBoost), the AdaBoost and the Random Forests (RFs) were trained on the augmented data to evaluate the performance improvement for lymphoma classification and HCM risk stratification. Our results revealed the favorable performance of the tree ensemble generators, in both domains, yielding virtual data with goodness-of-fit 0.021 and KL-divergence 0.029 in pSS and 0.029, 0.027 in HCM, respectively. The application of the XGBoost on the augmented data revealed an increase by 10.9% in accuracy, 10.7% in sensitivity, 11.5% in specificity for lymphoma classification and 16.1% in accuracy, 16.9% in sensitivity, 13.7% in specificity in HCM risk stratification