Deep Learning-Based CT Images in Pulmonary Function Assessment of Patients Who Underwent Laparoscopic Surgery under Guidance of Electrical Impedance Tomography

Abstract

This research aimed to study the application of CT images based on deep learning in pulmonary function assessment of patients who underwent laparoscopic surgery under the guidance of electrical impedance tomography (EIT). Sixty patients undergoing laparoscopic surgery were taken as the research subjects, who were randomly labelled as control group and experimental group. Based on deep learning, the empty convolution-combined fully convolutional neural network optimization algorithm (ECFCNN) was proposed, which was adopted to evaluate the pulmonary function of 60 patients and was compared with convolutional neural network (CNN) algorithm. The clarity of the edge contour of the image segmented by ECFCNN was better than that segmented by CNN. Average arterial pressure (MAP) and heart rate (HR) were recorded before induction (T1), 10 min before pneumoperitoneum (T2), 10 min after pneumoperitoneum (T3), 10 min before extubation (T4), and 10 min after extubation (T5), respectively. Oxygenation index (PaO2/FiO2), alveolary-arterial partial pressure of oxygen (A-ADO2), and respiratory index (RI) were recorded. The sharpness of the segmentation image edge contour of the algorithm model in this study was higher than that of the convolutional neural network. Compared with T1, T2-T4 MAP in 2 groups was decreased ( $P<0.05$ ). Compared with T1, T2-T5 HR was significantly decreased ( $P<0.05$ ). Compared with T2, T5 PaO2/FiO2 in control group was significantly decreased ( $P<0.05$ ). Compared with the control group, T5 A-aDO2 was decreased ( $P<0.05$ ). To sum up, EIT-guided lung protective ventilation can assess the pulmonary function of patients who underwent laparoscopic surgery, reduce the incidence of atelectasis, and improve postoperative lung oxygenation.