Weaning from mechanical ventilation in the intensive care unit (ICU) is a complex clinical problem and relevant for future organ engineering. Prolonged mechanical ventilation (MV) leads to a range of medical complications that increases length of stay and costs as well as contributes to morbidity and even mortality and long-term quality of life. The need to reduce MV is both clinical and economical. Artificial intelligence or machine learning (ML) methods are promising opportunities to positively influence patient outcomes. ML methods have been proposed to enhance clinical decisions processes by using the large amount of digital information generated in the ICU setting. There is a particular interest in empirical methods (such as ML) to improve management of "difficult-to-wean" patients, due to the associated costs and adverse events associated with this population. A systematic literature search was performed using the OVID, IEEEXplore, PubMed, and Web of Science databases. All publications that included (1) the application of ML to weaning from MV in the ICU and (2) a clinical outcome measurement were reviewed. A checklist to assess the study quality of medical ML publications was modified to suit the critical assessment of ML in MV weaning literature. The systematic search identified nine studies that used ML for weaning management from MV in critical care. The weaning management application areas included (1) prediction of successful spontaneous breathing trials (SBTs), (2) prediction of successful extubation, (3) prediction of arterial blood gases, and (4) ventilator setting and oxygenation-adjustment advisory systems. Seven of the nine studies scored seven out of eight on the quality index. The remaining two of the nine studies scored one out of eight on the quality index. This scoring may, in part, be explained by the publications' focus on technical novelty, and therefore focusing on issues most important to a technical audience, instead of issues most important for a systematic medical review. This review showed that only a limited number of studies have started to assess the efficacy and effectiveness of ML for MV in the ICU. However, ML has the potential to be applied to the prediction of SBT failure, extubation failure, and blood gases, and also the adjustment of ventilator and oxygenation settings. The available databases for the development of ML in this clinical area may still be inadequate. None of the reviewed studies reported on the procedure, treatment, or sedation strategy undergone by patients. Such information is unlikely to be required in a technical publication but is potentially vital to the development ML techniques that are sufficiently robust to meet the needs of the "difficult-to-wean" patient population.
Capillary non-perfusion (CNP) in the retina is a characteristic feature used in the management of a wide range of retinal diseases. There is no well-established computation tool for assessing the extent of CNP. We propose a novel texture segmentation framework to address this problem. This framework comprises three major steps: pre-processing, unsupervised total variation texture segmentation, and supervised segmentation. It employs a state-of-the-art multiphase total variation texture segmentation model which is enhanced by new kernel based region terms. The model can be applied to texture and intensity-based multiphase problems. A supervised segmentation step allows the framework to take expert knowledge into account, an AdaBoost classifier with weighted cost coefficient is chosen to tackle imbalanced data classification problems. To demonstrate its effectiveness, we applied this framework to 48 images from malarial retinopathy and 10 images from ischemic diabetic maculopathy. The performance of segmentation is satisfactory when compared to a reference standard of manual delineations: accuracy, sensitivity and specificity are 89.0%, 73.0%, and 90.8% respectively for the malarial retinopathy dataset and 80.8%, 70.6%, and 82.1% respectively for the diabetic maculopathy dataset. In terms of region-wise analysis, this method achieved an accuracy of 76.3% (45 out of 59 regions) for the malarial retinopathy dataset and 73.9% (17 out of 26 regions) for the diabetic maculopathy dataset. This comprehensive segmentation framework can quantify capillary non-perfusion in retinopathy from two distinct etiologies, and has the potential to be adopted for wider applications.
The growing popularity of contact sports drives the requirement for better design of protective equipment, such as mouthguards. Smart mouthguards with embedded electronics provide a multitude of new ways to provide increased safety and protection to users. Characterisation of how electronic components embedded in typical mouthguard material, ethylene vinyl acetate (EVA), behave under typical sports impacts is crucial for future designs. A novel pendulum impact rig using a hockey ball disc impactor was developed to investigate impact forces and component failure. Two sets of dental models (aluminium and plastic padding chemical metal) were used to manufacture post-thermoformed mouthguards. Seven embedding conditions with varying thickness of EVA (1.5 and 3 mm) and locations of electrical components were tested. Component failures were observed in four out of seven test conditions, and the experimental failure forces at which the electrical component had a 50% chance of failure were reported for those cases. The experimental results showed that an EVA thickness of 3 mm surrounding the electrical component gives the most comprehensive protection even under extreme surface conformity. Computational models on surface conformity of EVA showed that a block of EVA with a minimum thickness of 1.5 mm was better at reducing stress concentration than a shell with an overall thickness of 1.5 mm. This study demonstrated that the thickness of a mouthguard is important when protecting electrical components from extreme dental surface conformity, furthermore the surface geometry should not be overlooked when considering electrical component safety for in-body wearables that are impact prone. Electronic supplementary material The online version of this article (10.1007/s10439-019-02267-4) contains supplementary material, which is available to authorized users.
Traumatic injuries to the central nervous system (brain and spinal cord) have recently been put under the spotlight because of their devastating socioeconomical cost. At the cellular scale, recent research efforts have focussed on primary injuries by making use of models aimed at simulating mechanical deformation induced axonal electrophysiological functional deficits. The overwhelming majority of these models only consider axonal stretching as a loading mode, while other modes of deformation such as crushing or mixed modes-highly relevant in spinal cord injury-are left unmodelled. To this end, we propose here a novel 3D finite element framework coupling mechanics and electrophysiology by considering the electrophysiological HodgkinHuxley and Cable Theory models as surface boundary conditions introduced directly in the weak form, hence eliminating the need to geometrically account for the membrane in its electrophysiological contribution. After validation against numerical and experimental results, the approach is leveraged to model an idealised axonal dislocation injury. The results show that the sole consideration of induced longitudinal stretch following transverse loading of a node of Ranvier is not necessarily enough to capture the extent of axonal electrophysiological deficit and that the non-axisymmetric loading of the node participates to a larger extent to the subsequent damage. On the contrary, * Corresponding authors Email addresses: man.kwong@eng.ox.ac.uk (Man Ting Kwong ), antoine.jerusalem@eng.ox.ac.uk (Antoine Jérusalem ) Preprint submitted to Computer Methods In Applied Mechanics And EngineeringJune 6, 2018 a similar transverse loading of internodal regions was not shown to significantly worsen with the additional consideration of the non-axisymmetric loading mode.
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