Application of two-dimensional fractional-order convolution and bounding box pixel analysis for rapid screening of pleural effusion.

Pleural effusion is a pathologic symptom in which there is accumulation of body fluids around the lungs. A chest radiograph is a rapid examination technique and does not require complex setup for making a preliminary diagnosis of lung and heart diseases. In radiographic visualization, the symptom patterns appear as light or dark areas in the lung cavity. Computer-aided diagnosis is an automatic manner that can rapidly highlight the object region by preanalyzing medical images. It can improve the problems of manual inspection and allow diagnosis in remote medical facilities. Based on the ratios of lung anatomy, the automatic screening manner based on pattern recognition can be viewed as pixel value detection in the bilateral lung cavities. In this study, a fractional-order convolution (FOC) process is used to enhance the original image for an accurate extrapolation of the desired object in an image. The specific object image feature can be improved, and an accurate quantification of the pleural effusion region can be obtained using the suitable ranges of fractional-order parameters. Based on the boundaries of homogeneous regions, the pixel ratios of the lung anatomy between normal and abnormal conditions can be computed. The pleural effusion sizes and volumes can be rapidly estimated through the number of pixel changes. The experimental results reveal that the feature maps are similar and stable on image enhancement and segmentation with two fractional-order enhancement masks, as fractional-order v = 0.05 to 0.20 for mask 1# and v = 0.80 to 0.95 for mask 2#, respectively. The results also demonstrate the feasibility of the study on combining two-dimensional image FOC-process and bounding box pixel analysis to estimate the moderate and large effusion sizes from 500-2,000 mL.

Blood leakage detection during dialysis therapy based on fog computing with array photocell sensors and heteroassociative memory model.

Blood leakage and blood loss are serious life-threatening complications occurring during dialysis therapy. These events have been of concerns to both healthcare givers and patients. More than 40% of adult blood volume can be lost in just a few minutes, resulting in morbidities and mortality. The authors intend to propose the design of a warning tool for the detection of blood leakage/blood loss during dialysis therapy based on fog computing with an array of photocell sensors and heteroassociative memory (HAM) model. Photocell sensors are arranged in an array on a flexible substrate to detect blood leakage via the resistance changes with illumination in the visible spectrum of 500-700 nm. The HAM model is implemented to design a virtual alarm unit using electricity changes in an embedded system. The proposed warning tool can indicate the risk level in both end-sensing units and remote monitor devices via a wireless network and fog/cloud computing. The animal experimental results (pig blood) will demonstrate the feasibility.

Integrating Flexible Sensor and Virtual Self-Organizing DC Grid Model With Cloud Computing for Blood Leakage Detection During Hemodialysis.

Blood leakage and blood loss are serious complications during hemodialysis. From the hemodialysis survey reports, these life-threatening events occur to attract nephrology nurses and patients themselves. When the venous needle and blood line are disconnected, it takes only a few minutes for an adult patient to lose over 40% of his / her blood, which is a sufficient amount of blood loss to cause the patient to die. Therefore, we propose integrating a flexible sensor and self-organizing algorithm to design a cloud computing-based warning device for blood leakage detection. The flexible sensor is fabricated via a screen-printing technique using metallic materials on a soft substrate in an array configuration. The self-organizing algorithm constructs a virtual direct current grid-based alarm unit in an embedded system. This warning device is employed to identify blood leakage levels via a wireless network and cloud computing. It has been validated experimentally, and the experimental results suggest specifications for its commercial designs. The proposed model can also be implemented in an embedded system.

Assistive technology using integrated flexible sensor and virtual alarm unit for blood leakage detection during dialysis therapy.

Blood leakages and blood loss are both serious complications during dialysis therapies. According to dialysis survey reports, these events are life-threatening issues for nephrology nurses, medical staff, and patients. When venous needle dislodgement occurs, it takes only <2.5 min of reaction time for blood loss in an adult patient, resulting in mortality. As an early-warning design, a wireless assistive technology using an integrated flexible sensor and virtual alarm unit was developed to detect blood leakage during dialysis therapies. The flexible sensor was designed using a screen print technique with printing electronic circuits on a plastic substrate. A self-organising algorithm was used to design a virtual alarm unit, consisting of a virtual direct current grid and a virtual alarm driver. In other words, this warning device was employed to identify the blood leakage levels via wireless fidelity wireless network in cloud computing. The feasibility was verified, and commercialisation designs can also be implemented in an embedded system.