Deep Learning-Based CT Imaging in Perioperative Period and Nursing of Esophageal Carcinoma Patients

• Main Authors: Qiaoli Wang, Jinfu Zhu
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: Scientific Programming
• Online Access: http://dx.doi.org/10.1155/2021/4453317

The study focused on the performance of Convolutional Neural Network- (CNN-) based lymph node recognition model as well as the effects of different rehabilitation nursing methods on patients with esophageal cancer. Specifically, the activation function and loss function were optimized by CNN, to establish a U-Net lymph node recognition model. It was compared with Mean Shift and Fuzzy C-means (FCM) algorithm for the loss value, the mean pixel accuracy (mPA), and intersection over union (IOU). 158 patients with esophageal cancer undergoing radical resection were selected as research subjects. With pathological diagnosis results as the gold standard, the role of CT imaging was evaluated in the diagnosis of esophageal cancer lymph nodes. All subjects were divided into control group (routine nursing) and intervention group (routine nursing + rehabilitation nursing) according to different nursing methods, with 79 cases in each. The two groups were compared in terms of the time in bed, hospital stay, indwelling chest tube time, and VAS scores. It was found that the loss value of the U-Net model was close to 0 when it was stable, and its IOU value and mPA value were significantly higher than those of the Mean Shift and FCM algorithms. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the U-Net model were 84.37%, 80.74%, 88.65%, 85.02%, and 87.16%, respectively. When it came to lymph node metastasis number of 1-2, there were notable differences between CT results and postoperative pathology results, and the difference was statistically significant (P<0.05). As for their identifying lymph node metastasis area, there was no statistically significant difference (P>0.05). The intervention group exhibited lower postoperative VAS score, shorter time in bed, and shorter hospital stay and indwelling chest tube time versus the control group (P<0.01). It suggested that the U-Net model optimized by CNN has high diagnostic efficiency for lymph nodes, and the rehabilitation nursing intervention significantly mitigates postoperative pain and accelerates postoperative recovery.