With the rapid development of artificial intelligence, especially deep learning technology, various new technologies and applications based on face images have emerged. Obstructive sleep apnea (OSA) is the disease with the highest morbidity and the most serious long-term harm among childhood sleep breathing disorders, and it is increasingly receiving common attention from families and society. Children with the disease have a special facial appearance and require early identification and treatment to prevent it. However, the current diagnostic methods have problems such as being invasive, time-consuming, and expensive. The purpose of this article is to use graph neural network technology based on face images to establish an OSA auxiliary diagnosis strategy for children to achieve OSA screening and analysis. Therefore, this article first takes the facial landmarks as the analysis object, divides the face into six key areas, and selects important landmarks in these regions. On this basis, to better consider the relationship between important landmarks, a global collaborative recognition strategy is proposed. By extracting the implicit relationship between landmarks, face graph structure data is established. Finally, the OSA-GNN model is established to achieve OSA screening and auxiliary analysis in children. Compared with other related studies, this strategy not only has a stronger representation and generalisation ability but can also carry out clinical applications better, providing doctors with diagnostic suggestions.

Adverse drug reactions (ADR) have become a common and serious problem faced by drug users worldwide, posing a significant threat to human life and health safety. How to achieve automatic evaluation of the quality of ADR reports, and how to mine and evaluate the relevance between drugs and adverse drug reactions, has become an urgent problem that needs to be solved at present. In this study, a text classification technology based on deep learning were employed to establish an automated system to evaluate the information quality and relevance of ADR reports, using ADR reports from cooperative medical institutions and case studies in the literature as samples. The ERNIE+DCGNN model was used to train the ADR relevance evaluation model, and its effects were compared with other mainstream models. Comparative experimental results demonstrated that the ADR relevance evaluation model constructed in this paper had better experimental results.

Gastroscopy is an important step in the diagnosis of early gastric cancer. However, because the morphological manifestations of early gastric cancer are not obvious, endoscopists need long-term specialized training and experience accumulation to correctly identify early cancer through magnification gastroscopy. In this paper, the data set of gastroscopy image is collected and enhanced, and target detection method is combined with gastroscopy image. The Mask R-CNN+BiFPN model was proposed to enhance the feature fusion and improve the detection effect of early gastric cancer lesions. Compared with Mask R-CNN, the improved Mask R-CNN model has better performance, with the sensitivity and specificity of 91.67% and 88.95% in accurately labeled gastroscopic datasets, respectively, showing a good segmentation effect for surface swelling lesions.

As the medical aesthetic market is growing rapidly in China, orthodontic treatment is becoming very common among the adolescent population. However, there are countless doctor-patient disputes due to treatment results that do not meet patients' expectations, so there is an urgent need for a method to predict treatment results. With the development of artificial intelligence technology, generative adversarial network has provided us with a new way of thinking. The purpose of this paper is to accurately predict the face of patients after orthodontic treatment by using generative adversarial network. Therefore, we designed an evaluation index to reflect the difference between the algorithm predicted image and the patient's real image. After that, we designed a network based on Encoder-Decoder architecture to transform the vectors in StyleGAN latent space. Finally, we carried out experiments to verify the effectiveness of the evaluation index design and the advantages of the algorithm.