[Advances in the research of artificial intelligence technology assisting the diagnosis of burn depth]

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

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.

Analysis of the Curative Effect and Influencing Factors of Collagen Sponge Combined with Autologous Skin Graft in the Treatment of Deep Burn Patients

Burn is one of the common traumatic diseases in clinics. After deep burn, the complicated changes of the condition are caused by the burn wound, which ends with the repair of the wound. For patients with deep burns, whether the wound can be repaired as soon as possible is the key to the success of clinical treatment. For patients with deep burns, due to the lack of an autologous skin source, scar hyperplasia at donor site, skin graft repair at donor site, postoperative flap necrosis, and other problems in traditional surgical procedures, the method of improving function only by an autologous skin source has been unable to perform the later function reconstruction in patients with deep burns. In this study, collagen sponge combined with autologous skin graft was used to treat patients with deep burn, and the clinical efficacy of the patients was observed, and the related factors affecting the efficacy of the patients were analyzed. The results showed that collagen sponge combined with autologous skin graft was effective in the treatment of deep burn patients, and it was worth popularizing. Deep III-IV degree burns, wound infection, and hospital stay >3 months are all risk factors affecting the postoperative curative effect of patients. Therefore, in the clinical work, we should focus on patients with deep III-IV degree burns, perform surgery as soon as possible, and actively deal with wounds to prevent infection, which is beneficial to improve the curative effect.