Annals of biomedical engineering
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Comparative Study
Impact Performance Comparison of Advanced Bicycle Helmets with Dedicated Rotation-Damping Systems.
Bicycle helmets effectively mitigate skull fractures, but there is increasing concern on their effectiveness in mitigating traumatic brain injury (TBI) caused by rotational head acceleration. Bicycle falls typically involve oblique impacts that induce rotational head acceleration. Recently, bicycle helmet with dedicated rotation-damping systems have been introduced to mitigate rotational head acceleration. ⋯ Of the four rotation-damping systems, two systems significantly reduced rotational head acceleration, TBI risk, and brain strain compared to the standard bicycle helmet. One system had no significant effect on impact performance compared to control helmets, and one system significantly increase linear and rotational head acceleration by 62 and 61%, respectively. In conclusion, results revealed significant differences in the effectiveness between rotation-damping systems, whereby some rotation-damping systems significantly reduced rotational head acceleration and associated TBI risk.
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One major role of an accurate distribution of abdominal adipose tissue is to predict disease risk. This paper proposes a novel effective three-level convolutional neural network (CNN) approach to automate the selection of abdominal computed tomography (CT) images on large-scale CT scans and automatically quantify the visceral and subcutaneous adipose tissue. First, the proposed framework employs support vector machine (SVM) classifier with a configured parameter to cluster abdominal CT images from screening patients. ⋯ The mean accuracy of the configured SVM classifier yields promising performance of 99.83%, while DilaLabPlus achieves a remarkable performance improvement an with average of 98.08 ± 0.84% standard deviation and 0.7 ± 0.8% standard deviation false-positive rate. The performance of DilaLab yields average 97.82 ± 1.34% standard deviation and 1.23 ± 1.33% standard deviation false-positive rate. This study demonstrates considerable improvement in feasibility and reliability for the fully automated recognition of abdominal CT slices and segmentation of selected abdominal CT in subcutaneous and visceral adipose tissue, and it has a high agreement with a manually annotated biomarker.