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
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.
Transcatheter pulmonary valve replacement (TPVR) is a technique for treating valvular heart diseases or dysfunctions without the need for open-heart surgery. Commercial valve stents, such as Epic TM valved stents or mechanical heart valves, can be used to solve the problems of narrowed or leaky pulmonary valves and also to improve regurgitation flow and heart pump efficiency (HPE). However, these prosthetic valves have limitations in terms of availability, durability, and stent size for children and for meeting the needs of special subjects. To be deemed fit for new clinical cases, handmade trileaflet-valved conduits (HTVCs) provide a promising strategy for designing stents using optimal design methods for customized specifications for child and adult patients. Before clinical applications, the functions of HTVCs need to verify hemodynamic statuses, such as the regurgitation fraction (RF), HPE, and pressure drop, under different heart rates (HR), blood flow volumes, and hypertension conditions. To reduce the number of experimental tests, the Taguchi method with signal-tonoise (S/N) ratios is used to validate the assigned optimal parameter designs, which can obtain good hemodynamic performance with the desired target goals of (1) RF ≤ 20% (minimizing the objective function) and (2) HPE ≥ 80% (maximizing the objective function). In contrast to commercial valve stents, such as Epic TM valved stent and mechanical heart valve, likelihood degree estimation is also employed to quantify the degree of valve performance and verify the quality of HTVCs.
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