Department of Endoscope Center, The Affiliated Huaian No.1 People's Hospital, Nanjing Medical University, Huaian, 223300 Jiangsu, China.
Comput Math Methods Med. 2022 Apr 19;2022:1607099. doi: 10.1155/2022/1607099. eCollection 2022.
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 ( < 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 ( < 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 ( < 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 ( < 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.
本研究旨在探讨基于深度学习的超声联合胃镜检查对上消化道黏膜下病变的诊断价值及护理。选取医院收治的 104 例上消化道黏膜下病变患者作为研究对象。本研究对前馈去噪卷积神经网络(DnCNN)进行改进,设计并应用 n-DnCNN 模型进行超声图像处理,比较高斯滤波、NL-means 和 DnCNN 与 n-DnCNN 的峰值信噪比(PSNR)和结构相似性(SSIM)。然后,通过超声联合胃镜和单纯胃镜检查分析不同部位上消化道黏膜下病变的分布和类型,并评估该方法的诊断性能。结果显示,n-DnCNN 模型的平均 PSNR 和 SSIM 分别为 33.01dB 和 0.87,均显著高于 GF、NL-means 和 DnCNN 算法,差异具有统计学意义( < 0.05)。在检测到的 116 个病变中,49 个位于食管(42.24%),52 个位于胃(44.83%),15 个位于十二指肠(12.93%)。49 个食管黏膜下病变中,6.12%位于食管上段,55.1%位于食管中段,38.79%位于食管下段,差异有统计学意义( < 0.05)。胃黏膜下病变中,贲门部病变明显少于其他部位,差异有统计学意义( < 0.05)。超声联合胃镜检查对上消化道黏膜下病变的诊断准确率为 82.32%,高于胃镜检查,差异有统计学意义( < 0.05)。综上所述,n-DnCNN 模型具有良好的降噪效果,得到的图像质量较高。超声联合胃镜检查能有效提高上消化道黏膜下病变的诊断准确率。