Deep Learning-Based Ultrasound Combined with Gastroscope for the Diagnosis and Nursing of Upper Gastrointestinal Submucous Lesions

Abstract

The study focused on the diagnostic value of deep learning-based ultrasound combined with gastroscope examination for upper gastrointestinal submucous lesions and nursing. A total of 104 patients with upper gastrointestinal submucous lesions diagnosed in hospital were selected as the research subjects. In this study, the feed forward denoising convulsive neural network (DnCNN) was improved, and the n-DnCNN model was designed and applied to ultrasonic image processing. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of Gaussian filtering, NL-means, and DnCNN were then compared with n-DnCNN. Subsequently, the distribution and types of submucosal lesions in different parts of the upper digestive tract were analyzed by ultrasound combined with gastroscope and gastroscope examination alone, and the diagnostic performance of this method was evaluated. The results showed that the average PSNR and SSIM of the n-DnCNN model were 33.01 dB and 0.87, respectively, which were significantly higher than GF, NL-means, and DnCNN algorithms, and the difference was statistically significant ( P < 0.05 ). Of the 116 lesions detected, 49 were located in the esophagus (42.24%), 52 in the stomach (44.83%), and 15 in the duodenum (12.93%). Of the 49 esophageal submucosal lesions, 6.12% were located in the upper esophagus, 55.1% in the middle esophagus, and 38.79% in the lower esophagus, and the difference was statistically significant ( P < 0.05 ). Of the gastric submucosal lesions, the lesions in the gastric cardia were significantly less than in other parts, and the difference was statistically significant ( P < 0.05 ). The accuracy of ultrasound combined with gastroscope in the diagnosis of upper gastrointestinal submucous episodes was 82.32%, higher than that of gastroscope examination, and the difference was statistically significant ( P < 0.05 ). In conclusion, the n-DnCNN model has a good noise reduction effect, and the obtained image is of high quality. Ultrasound combined with gastroscope examination can effectively improve the accuracy of diagnosis of upper gastrointestinal submucous lesions.

Outcome of Nursing Based on Health Belief United with Knowledge, Belief, and Practice Mode on Gastroscopy of Patients with Gastric Cancer

“…Gastroscopy plays an important role in gastric cancer screening, illness progress monitoring, and illness severity judgment [12][13][14][15]. Whereas, patients are often nervous and even afraid before the examination, resulting in low examination cooperation and inaccurate examination results, thus delaying the condition [16][17][18][19]. The causes of bad mood can be summarized as follows: (a) gastroscopy is an invasive examination, and patients who do not know much about it think that the operation will damage the throat and esophagus and are unwilling to cooperate with the examination; (b) in the process of examination, patients are prone to cough, nausea, and vomiting due to physical stimulation of gastroscope; (c) patients lack relevant knowledge about the monitoring of gastric cancer, the importance of gastroscopy, and the operation procedure of gastroscopy, especially for the first time.…”

Deep Learning Identifies HAT1 as a Morphological Regulator in Esophageal Squamous Carcinoma Cells through Controlling Cell Senescence

“…[2] .In clinical practice, accurate annotation of tumor grades and cancer subtypes can overcome the variations caused by clinician expertise or potential fatigue and adds significantly to the efficacy of the initial diagnosis and the following treatment, such as surgical excision, radiation, and chemotherapy, with the aid of the deep learning model. [3] The deep learning models have been applied to endoscopic images [4] and ultrasound images [5] for early esophageal disease diagnosis, PET images for treatment outcome prediction, [6] and computerized tomography for esophageal fistula prediction. [7] However, the identified properties or interpreted hypotheses by deep learning are seldom verified by experimental approaches to facilitate further scientific research.…”