Fluid-Structure Interaction in Biological Media / FSI
shahrokh shojaei; Shahrokh Shojaei
Volume 16, Issue 4 , March 2023, , Pages 41-50
Abstract
The pathological effects of the tumor on the respiratory airway have always been the focus of researchers. So, these effects will lead to the suffocation of the patient in acute cases. This study presents a computational model to investigate the effect of a tumor on the airflow in the larynx area with ...
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The pathological effects of the tumor on the respiratory airway have always been the focus of researchers. So, these effects will lead to the suffocation of the patient in acute cases. This study presents a computational model to investigate the effect of a tumor on the airflow in the larynx area with the help of Ansys software. The presented model is able to numerically calculate the effect of tumor presence on airspeed and pressure in the upper air system. This study considered the simulation of steady airflow for exhalation in three respiratory flow rates of 15 L/min, 26 L/min, and 30 L/min. The maximum speed limit in the respiratory flow of L/min 15, L/min 26, and L/min 30, respectively, 6.26 m/s, 10.58 m/s, and 12.14 m/s, appears in the larynx. Also, the highest pressure occurs in the trachea, so the maximum pressure in the respiratory rate is 15 L/min, 26 L/min, and 30 L/min, respectively, equal 19.6 Pa, 51.01 Pa, and 65.8 Pa. On the other hand, most deformation occurs in the area of narrowing of the respiratory tract. With the increase in the flow rate, the amount of deformation also increases. The maximum deformation on the wall at the respiratory flow rate of 15 L/min, 26 L/min, and 30 L/min is equal to 0.07mm, 0.2mm, and 0.27mm, respectively. Due to the presence of a tumor in this respiratory model, velocity and WSS reach their maximum in the larynx region. The presence of a tumor can gradually lead to airway obstruction. Moreover, the risk of airway obstruction increases even in a slight reduction in respiratory capacity. Providing a numerical model for the respiratory system can effectively lead to a better treatment approach.
Neural Network / Biological & Artificial Neural Network / BNN & ANN
Hamed Abbasi; Shahrokh Shojaei; Nasim Naderi
Volume 13, Issue 2 , August 2019, , Pages 105-115
Abstract
Today, in order to decide on many cardiac surgeries, and whether the patient is able to get under surgery or the time of surgery is passed, it is necessary to measure pulmonary vascular resistance and if the resistance is above a threshold, the patient is considered to be non-surgery; and sometimes, ...
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Today, in order to decide on many cardiac surgeries, and whether the patient is able to get under surgery or the time of surgery is passed, it is necessary to measure pulmonary vascular resistance and if the resistance is above a threshold, the patient is considered to be non-surgery; and sometimes, some therapies are used to reduce the resistance of the pulmonary arteries to the initial disease of the arteries, in which, in order to track down the resistance of the pulmonary vascular, a re-measurement of this parameter is required. Currently, the golden standard of this measure is the use of catheterization procedures, which are aggressive and associated with complications. The purpose of this study is to replace a non-invasive method, rather than an invasive method of cardiac catheterization, by predicting pulmonary vascular resistance based on echocardiographic data by artificial neural networks. Research was performed on 591 patients. Echocardiography was recorded for all subjects, and the echocardiographic data (mPAP, dPAP, sPAP, PCWP, CO) as the neural network input and pulmonary vascular resistance of all patients who were subjected to previous catheterization was evaluated as the output of the neural network and thus, it was obtained, the relationship between echocardiography data and PVRcath. The proposed neural network was typically learned with 75% of the data, and was tested with 25% of the data, and these ratios were modified to better learn the neural network. As a result of implementation, the mean squared error, respectively, for the learning and testing data for the proposed neural network, was 0.37 and 0.27 for the first model, 14.67 and 10.76 for the second model, and 15.82 and 9.58 for the third model.