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Purpose: The research aimed to verify the applicability of low computational complexity and high diagnosis accuracy deep convolutional neural network, using MobileNetV2 to identify the presence of chest catheters and tubes on chest X-ray images. Methods: The dataset of chest X-rays collected from a teaching hospital included the endotracheal tube (ETT), the central venous catheter (CVC), and the nasogastric tube (NGT) datasets. A new method of applying dynamic image size training procedures was implemented and compared with fixed image size training. The idea is to learn more features through dynamic image size training. Transfer learning with pre-trained MobileNetV2 on ImageNet was conducted to accelerate the training process and acquire higher accuracy. Class activation mapping (CAM) was also employed to visualize artificial intelligence (AI) predictions, making AI decisions more explainable. Results: The ETT datasets included 10464 X-ray images, while the CVC and NGT datasets contained 10274 and 9610 images, respectively. The accuracies for ETT, CVC, and NGT are 99.0%, 98.4%, and 96.2% in the validation dataset, while in the testing dataset are 98.8%, 98.6%, and 96.8%, respectively. The area under the receiver operating characteristics (AUROCs) were 0.992, 0.988, and 0.980 in the ETT, CVC, and NGT testing datasets. Conclusion: MobileNetV2 with the dynamic image size achieved dedicated performance in the application of chest catheters and tubes classifications. The similarity of the accuracy between the validation and testing data suggests the good generalization capability of the model.

In this paper, we consider an integrated error concealment system for lost color frames and lost depth frames in multiview videos with depths. We first proposed a pixel-based color error-concealment method with the use of depth information. Instead of assuming that the same moving object in consecutive frames has minimal depth difference, as is done in a state-of-the-art method, a more realistic situation in which the same moving object in consecutive frames can be in different depths is considered. In the derived motion vector candidate set, we consider all the candidate motion vectors in the set, and weight the reference pixels by the depth differences to obtain the final recovered pixel. Compared with the two state-of-the-art methods, the proposed method has average peak signal-to-noise ratio gains of up to 8.73 and 3.98 dB, respectively. Second, we proposed an iterative depth frame error-concealment method. The initial recovered depth frame is obtained by depth-image-based rendering from another available view. The holes in the recovered depth frame are then filled in the proposed priority order. Preprocessing methods (depth difference compensation and inconsistent pixel removal) are performed to improve the performance. Compared with a method that uses the available motion vector in a color frame to recover the lost depth pixels, the hybrid motion vector extrapolation method, the inpainting method and the proposed method have gains of up to 4.31, 10.29, and 6.04 dB, respectively. Finally, for the situation in which the color and the depth frames are lost at the same time, our two methods jointly perform better with a gain of up to 7.79 dB.

This paper proposes four logic-chain bridging fault models, which involve one net in the combinational logic and the other net in the scan chain. Test results of logic-chain bridging faults, unlike existing scan chain fault models, depend on the previous scan inputs as well as primary inputs. A bridging pair extraction algorithm is proposed to quickly extract bridging pairs from the layout. The paper proposed two sets of structural reduction techniques so that runtime is very short. Experimental results on ISCAS benchmark circuits show that, on the average, logic-chain bridging faults can be diagnosed within an accuracy of four bridging pairs. The techniques are still applicable when there are only 10 failing patterns due to limited ATE failure memory. This paper demonstrates the feasibility to diagnose logic-chain bridging faults by software.

Hypereosinophilic syndrome is a rare disease that can cause multiple-organ damage. Cardiac involvement, which presents as myocardial necrosis, infarction, or thrombus formation, is the leading cause of morbidity and death. Here, we present the case of a 17-year-old girl who had hypereosinophilic syndrome with cardiac involvement, which was evaluated by multidetector-row computed tomography. By means of arterial-phase and low-dose delayed-phase scanning, multidetector-row computed tomography clearly identified the mural thrombus and the infarcted and noninfarcted myocardium—which surpassed the performance of echocardiography in tissue characterization. Therefore, we propose that multidetector-row computed tomography could be a useful noninvasive tool for evaluating patients with cardiac involvement of hypereosinophilic syndrome.

The high accuracy of multidetector-row computed tomography (MDCT) in evaluating prosthetic valve disorders has been confirmed. This, we believe, is the 1st report of the use of MDCT to detect and evaluate left ventricular outflow tract (LVOT) pseudoaneurysms in patients who have undergone aortic valve replacement with prosthetic valves. We used MDCT to scan 21 such patients, 3 of whom had a small pseudoaneurysm in the LVOT. Each pseudoaneurysm projected away from the LVOT and had a narrow neck that was located just below the sewing ring of the prosthetic aortic valve. One pseudoaneurysm was not thrombotic, 1 was partially thrombotic, and 1 was completely thrombotic. One of these had gone undetected earlier on transthoracic echocardiography.We consider MDCT to be superior to echocardiography in the detection of LVOT pseudoaneurysms in patients who have undergone aortic valve replacement with prosthetic valves. We publicize our results in the hope that larger studies will be undertaken in order to investigate the prevalence and clinical implications of our findings.

In their article published online on 19 April 2005, Leschka et al. 1 shared the first experience of the new 64-slice MSCT. The adequate sample size and evaluation of performance without beta-blocker make the article of high reference value. However, we have several questions to the authors. First, the authors mentioned that the scanner was a 64-MSCT with only 32-detector rows. About the collimation, though the z-sharp technology is used, the total collimation will not be doubled.2 The actual coverage of the collimation should be 32×0.6 mm, but the authors chose to represent it as 64×0.6 mm2, which is …