Radiofrequency (RF) ablation is a minimally invasive method for treatment of primary and metastatic liver tumors. One of the currently commercially available devices employs an internally cooled 17-gauge needle probe. Within the probe, cool water is circulated during ablation, which cools tissue close to the probe resulting in larger lesions. We evaluated the effect of different cooling water temperatures on lesion size. We created a finite-element method model, simulated 12 min impedance-controlled ablation and determined temperature distribution for three water temperatures. Lesion diameters in the model were 33.8, 33.4, and 32.8 mm for water temperatures of 5 degrees C, 15 degrees C, and 25 degrees C, respectively. We solved a simplified model geometry analytically and present dependence of lesion diameter on cooling temperature. We further performed ex vivo experiments in fresh bovine liver. We created four lesions for each water temperature, with the same water temperatures as used in the finite-element method (FEM) model. Average lesion diameters were 28.3, 30, and 29.5 mm for water temperatures of 5 degrees C, 15 degrees C, and 25 degrees C, respectively. Water temperature did not have a significant effect on lesion size in the ex vivo experiments (p = 0.76), the FEM model, and the analytical solution.
This study proposes a Raspberry Pi-based system for the diagnosis of heart valve diseases as a primary tool to improve the diagnostic accuracy of physicians. The proposed system is able to detect and classify nine common valvular heart cases encompassing eight types of heart valve diseases as well as the normal case of valves. The design and development of the proposed system are mainly divided into two phases, namely development of a disease classification approach, and design and implementation of the diagnostic hardware system. The developed disease classification approach is comprised of five stages, namely obtaining phonocardiogram (PCG) signals, preprocessing, segmentation using a proposed automatic algorithm, feature extraction in three domains (time, frequency, and wavelet decomposition domains) and classification using a backpropagation neural network. The hardware of the diagnostic system consists of a PCG signal acquisition module connected to a processing and displaying unit, which is represented by a Raspberry Pi connected to a touch screen. Where the developed disease classification approach is implemented in
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