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Background: Transcatheter aortic valves (TAVs) are susceptible to leaflet thrombosis which may lead to thromboembolic events, and early detection and intervention are believed to be the key to avoiding such adverse outcomes. An embedded sensor system installed on the valve stent, coupled with an appropriate machine learning-based continuous monitoring algorithm can facilitate early detection to predict severity of reduced leaflet motion (RLM) and avoid adverse outcomes. Methods: We present a data-driven, in silico, proof-of-concept analysis of a pressure microsensor based system for quantifying RLM in TAVs. We generate a dataset of 21 high-fidelity transvalvular flow simulations with healthy and mildly stenotic TAVs to train a logistic regression model to correlate individual leaflet mobility in each simulation with principal components of corresponding hemodynamic pressure recorded at strategic locations of the TAV stent. A separate test dataset of 7 simulations is also generated for prospective assessment of model performance. Results: An array of 6 sensors embedded on the TAV stent, with two sensors tracking individual leaflet, successfully correlates leaflet mobility with recorded pressure. The sensors are placed along leaflet centerlines, one in the sinus, and the other at the sino-tubular junction. The regression model is tuned using cross-validation to achieve high accuracy on both training (R2 = 0.93) and test (R2 = 0.77) sets. Conclusion: Discrete blood pressure recordings on TAV stents can be successfully correlated with individual leaflet mobility. Further development of this technology can enable longitudinal monitoring of TAVs and early detection of valve failure. [ABSTRACT FROM AUTHOR]

Purpose: Patients receiving transcatheter aortic valve replacement (TAVR) can benefit from continuous, longitudinal monitoring of valve prosthesis to prevent leaflet thrombosis-related complications. We present a computational proof-of-concept study of a novel, non-invasive and non-toxic valve monitoring technique for TAVs which uses pressure measurements from microsensors embedded on the valve stent. We perform a data-driven analysis to determine the signal processing and machine learning required to detect reduced mobility in individual leaflets. Methods: We use direct numerical simulations to describe hemodynamic differences in transvalvular flow in ascending aorta models with healthy and stenotic valves. A Cartesian-grid flow solver and a reduced-order valve model simulate the complex dynamics of blood flow and leaflet motion, respectively. The two-way fluid-structure interaction coupling is achieved using a sharp interface immersed boundary method. Results: From a dataset of 21 simulations, we show leaflets with reduced mobility result in large, asymmetric pressure fluctuations in their vicinity, particularly in the region extending from the aortic sinus to the sino-tubular junction (STJ). We train a linear classifier algorithm by correlating sinus and STJ pressure measurements on the stent surface to individual leaflet status. The algorithm was shown to have >90% accuracy for prospective detection of individual leaflet dysfunction. Conclusions: We demonstrate that using only two discrete pressure measurements, per leaflet, on the TAV stent, individual leaflet status can be accurately predicted. Such a sensorized TAV system could enable safe and inexpensive detection of prosthetic valve dysfunction. [ABSTRACT FROM AUTHOR]

Ocular trauma is one of the most common types of combat injuries resulting from the interaction of military personnel with improvised explosive devices. Ocular blast injury mechanisms are complex, and trauma may occur through various injury mechanisms. However, primary blast injuries (PBI) are an important cause of ocular trauma that may go unnoticed and result in significant damage to internal ocular tissues and visual impairment. Further, the effectiveness of commonly employed eye armor, designed for ballistic and laser protection, in lessening the severity of adverse blast overpressures (BOP) is unknown. In this paper, we employed a three-dimensional (3D) fluid-structure interaction computational model for assessing effectiveness of the eye armor during blast loading on human eyes and validated results against free field blast measurements by Bentz and Grimm (). Numerical simulations show that the blast waves focused on the ocular region because of reflections from surrounding facial features and resulted in considerable increase in BOP. We evaluated the effectiveness of spectacles and goggles in mitigating the pressure loading using the computational model. Our results corroborate experimental measurements showing that the goggles were more effective than spectacles in mitigating BOP loading on the eye. Numerical results confirmed that the goggles significantly reduced blast wave penetration in the space between the armor and the eyes and provided larger clearance space for blast wave expansion after penetration than the spectacles. The spectacles as well as the goggles were more effective in reducing reflected BOP at higher charge mass because of the larger decrease in dynamic pressures after the impact. The goggles provided greater benefit of reducing the peak pressure than the spectacles for lower charge mass. However, the goggles resulted in moderate, sustained elevated pressure loading on the eye, that became 50-100 % larger than the pressure loading experienced by the unprotected eye after 0.2 ms of impact of blast wave, for lower as well as higher charge mass. The present model provides fundamental insights of flow and pressure fields in the ocular region, which helps to explain the effectiveness of the eye armor. Since the measurements of these fields are not trivial, the computational model aids in better understanding of development of PBI. [ABSTRACT FROM AUTHOR]