A Strategy for Aided Diagnosis of Obstructive Sleep Apnea in Children Based on Graph Neural Network

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.