Pleural effusion is the pathologic accumulation of body fluids around the unilateral or bilateral lungs that is primarily caused by heart disease. A chest radiograph is a rapid examination technique used to provide a preliminary diagnosis of lung and heart diseases. Computer-aided diagnosis with the digitalized image is an automated approach that addresses the drawbacks of manual inspection. In this study, two corner detectors along with a two-dimensional convolution process are used to enhance the chest X-ray image for an accurate extrapolation of the bilateral lung cavities. Based on bounding box pixel analysis, the pixel ratios of the lung anatomy between normal and abnormal conditions can be estimated to identify the pleural effusion size. Next, a smart drainage monitoring system is developed to improve the current functions of the traditional drainage tool and confirm the drainage safety, including (a) drainage volume and required time detection, (b) unplanned removal warning, and (c) physiological status monitoring. The experimental result will be used to determine the feasibility of the proposed effusion volume estimation algorithm and the efficiency of the smart drainage monitoring prototyping tool. The proposed smart drainage monitoring system and the computer-aided method with digitalized images can be further applied in real clinical practice in the intensive care unit. INDEX TERMSPleural effusion, corner detector, bounding box pixel analysis, smart drainage monitoring system. PI-YUN CHEN received the Ph.D. degree from the
Clinically, arteriovenous shunt (AVS) stenosis results in turbulent and pulsatile flow because of high resistance and pressure within a narrowed space inside a stenotic access. Palpation and ultrasound methods are primarily used (first-line examination) to rapidly screen the risk of the degree of stenosis (DOS). Therefore, quantitative hemodynamic analysis involving Doppler ultrasound is performed in patients suffering from AVS stenosis and undergoing long-term hemodialysis. Doppler ultrasound with a center frequency of 7.5 MHz can provide substantial resolution and sensitivity to the measurement of blood flow velocity within a range of depth of 20.0-30.0 mm and a scan diameter of 10.0 mm. A hemodynamic method is used to analyze blood flow through a hemodialysis access in terms of dimensionless numbers. In this study, velocities were measured using Doppler ultrasound at three specific sites in vessels, namely, arterial anastomosis, loop, and venous anastomosis sites. Dimensionless numbers, such as supracritical Reynolds numbers, critical peak Reynolds numbers, and resistive indices, are determined in accordance with parallel conditional expressionbased rules to create decision trees for the rapid screening of the DOS at the abovementioned specific sites. For the enrolled subjects, results demonstrate that noninvasive hemodynamic analysis with Doppler ultrasound measurements and parallel decision trees has potential for the efficient screening of the DOS in patients suffering from AVS stenosis and undergoing long-term hemodialysis. Experimental results also indicate that the hit and true-positive rates of the proposed screening method in clinical indication are higher than those of the machine learning method.INDEX TERMS Doppler ultrasound, quantitative hemodynamic analysis, supracritical Reynolds number, resistive index, degree of stenosis.
In this paper, the Box–Cox transformation-based annealing robust fuzzy neural networks (ARFNNs) are proposed for identification of the nonlinear Magneto-rheological (MR) damper with outliers and skewness noises. Firstly, utilizing the Box-Cox transformation that its object is usually to make residuals more homogeneous in regression, or transform data to be normally distributed. Consequently, a support vector regression (SVR) method with Gaussian kernel function has the good performance to determine the number of rule in the simplified fuzzy inference systems and initial weights in the fuzzy neural networks. Finally, the annealing robust learning algorithm (ARLA) can be used effectively to adjust the parameters of the Box-Cox transformation-based ARFNNs. Simulation results show the superiority of the proposed method for the nonlinear MR damper systems with outliers and skewness noises.
Pleural effusion is the pathologic accumulation of body fluids in the chest cavity and can be classified as pulmonary edema and hemothorax. Pulmonary edema is usually caused by heart diseases, which account for a greater proportion. In the case of excess effusion volume (1000 -1500 mL), dyspnea occurs in patients, whereas purulent effusion may lead to infection. In general, pleural effusion drainage is performed via an inserted chest tube or a pigtail catheter under clinician suggestions. In clinical practice, current pleural effusion drainage has some concerns, such as (1) drainage volume estimation, (2) drainage volume and duration control, and (3) unplanned chest tube/catheter removal by the patients. Moreover, the rapid drainage of large pleural effusion volumes leads to reexpansion pulmonary edema (RPE), which can threaten the patient's life. Hence, the current drainage system needs to monitor the heart rate or respiration rate. In this study, we intend to establish a smart drainage monitoring system that could improve the traditional drainage system functions, including (1) drainage volume and speed estimation, removal warning, and heart rate monitoring, and (2) its applications to drainage monitoring in both the thoracic cavity and the abdominal cavity. We expect that we can improve the function of the drainage monitoring system in terms of drainage volume, physiological signals, and safety confirmation.
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