Objective To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs). Summary of background data Hip fracture is a leading worldwide health problem for the elderly. A missed diagnosis of hip fracture on radiography leads to a dismal prognosis. The application of a DCNN to PXRs can potentially improve the accuracy and efficiency of hip fracture diagnosis. Methods A DCNN was pretrained using 25,505 limb radiographs between January 2012 and December 2017. It was retrained using 3605 PXRs between August 2008 and December 2016. The accuracy, sensitivity, false-negative rate, and area under the receiver operating characteristic curve (AUC) were evaluated on 100 independent PXRs acquired during 2017. The authors also used the visualization algorithm gradient-weighted class activation mapping (Grad-CAM) to confirm the validity of the model. Results The algorithm achieved an accuracy of 91%, a sensitivity of 98%, a false-negative rate of 2%, and an AUC of 0.98 for identifying hip fractures. The visualization algorithm showed an accuracy of 95.9% for lesion identification. Conclusions A DCNN not only detected hip fractures on PXRs with a low false-negative rate but also had high accuracy for localizing fracture lesions. The DCNN might be an efficient and economical model to help clinicians make a diagnosis without interrupting the current clinical pathway. Key Points • Automated detection of hip fractures on frontal pelvic radiographs may facilitate emergent screening and evaluation efforts for primary physicians. • Good visualization of the fracture site by Grad-CAM enables the rapid integration of this tool into the current medical system. • The feasibility and efficiency of utilizing a deep neural network have been confirmed for the screening of hip fractures .

Continuous measurement of blood pressure is crucial to the assessment of many medical conditions. However, the current clinical gold standard involving an arterial catheter, occluding cuff, and other invasive procedures are performed in hospital settings while home-based devices can provide only intermittent measurement and are not as reliable. Therefore, there is a significant need for continuous noninvasive blood pressure (cNIBP) monitoring in the daily life. Pulse transit time (PTT)/pulse arrival time (PAT)-based blood pressure measurement has proven its potential to address this need. In this article, we present state-of-the-art devices and recent literature related to measurement technologies used in PTT/PAT-based methods for cNIBP monitoring. Various physiological signals which could be used to enable cNIBP in the home setting are categorized into two groups (i.e., proximal waveforms and distal waveforms) and are thoroughly discussed and compared. Given insightful analysis of these waveforms, we highlight their combinations to derive PTT/PAT values for BP measurement then discuss challenges presented from the cuffless and PTT/PAT-based nature of these devices. Finally, we conclude with future directions needed for home-based cNIBP adaptation and present societal broader impacts.

No abstract

No abstract

Noninvasive, and continuous physiological sensing enabled by novel wearable sensors is generating unprecedented diagnostic insights in many medical practices. However, the limited battery capacity of these wearable sensors poses a critical challenge in extending device lifetime in order to prevent omission of informative events. In this work, we exploit the inherent sparsity of physiological signals to intelligently enable selective transmission of these signals and thereby improve the energy efficiency of wearable sensors. We propose STINT, a selective transmission framework that generates a sparse representation of the raw signal based on domainspecific knowledge, and which can be integrated into a wide range of resource-constrained embedded sensing IoT platforms. STINT employs a neural network (NN) for selective transmission: the NN identifies, and transmits only the informative parts of the raw signal, thereby achieving low power operation. We validate STINT and establish its efficacy in the domain of IoT for energy-efficient physiological monitoring, by testing our framework on EcoBP-a novel miniaturized, and wireless continuous blood pressure sensor. Early experimental results on the EcoBP device demonstrate that the STINT-enabled EcoBP sensor outperforms the native platform by 14% of sensor energy consumption, with room for additional energy savings via complementary bluetooth and wireless optimizations.