Prediction Of Obstructive Sleep Apnea In Children Based On Magnetic Resonance Imaging

Obstructive sleep apnea(OSA)is a sleep disorder disease,which has a negative impact on the physical and mental health of patients.Especially for children in developmental stage,if they are not treated in time,OSA may cause irreversible damage to their brain structure.Polysomnography is the gold standard for diagnosing OSA,but its application is limited because of its high price and time-consuming detection.At present,with the increase of suspected OSA patients,more and more OSA patients can not get timely diagnosis.It is very important to develop an effective OSA screening method.Previous studies on OSA have shown that OSA leads to changes in brain structure.Magnetic resonance imaging(MRI)technology can quantitatively measure and record brain activity based on living body,and convert it into images and signals.Therefore,machine learning and deep learning methods can be used to analyze images and signals.Structural magnetic resonance imaging(s MRI)and diffusion tensor imaging(DTI)are often used to explore the effect of OSA on the brain structure of patients.Therefore,in order to solve the problems in the clinical prediction of OSA,this thesis has carried out three studies on the prediction of OSA in children based on magnetic resonance imaging,which is committed to providing an effective auxiliary diagnostic method for the clinical prediction of OSA in children.The specific research contents are as follows:(1)In clinical practice,there are different treatment schemes for OSA patients with different severity,so quantitative prediction of the severity of OSA children has important clinical significance.This paper constructs an elastic net prediction model based on DTI data to realize the quantitative prediction of the severity of OSA children,and provide guidance and help for the personalized treatment of OSA children in clinical practice.The experimental results show that the method used in this paper can not only better predict the severity of OSA children,but also deeply analyze the brain white matter regions of children most affected by OSA.(2)Given that children with moderate to severe OSA are the key screening objects of OSA and need to be treated first,it is necessary to develop a method to identify children with moderate to severe OSA.Since the scanning time of s MRI is shorter than that of DTI and it is more convenient to obtain,this thesis proposes a method for identifying moderate and severe OSA in children based on morphological and positional attention features using s MRI data.This method obtains the attention distribution of different morphological and positional features from the two dimensions of channel and space respectively,and aggregates the attention feature vectors from different dimensions to enhance the expression of discriminative features.The experimental results show that the proposed method can achieve better classification performance than other feature extraction methods.(3)Considering that the condition of children with mild OSA may develop further,it is important to develop an applicable method for diagnosing OSA and non OSA.Since multimodal data can provide potential information related to disease from different imaging perspectives,this thesis proposes a method for identifying OSA in children based on multimodal transformer feature fusion using DTI and s MRI data.Based on the attention mechanism,this method adopts the way of feature interaction to realize the cross fusion of different modal data.Then attention fusion strategy is applied to enhance the expression ability of fused features.Compared with other feature fusion methods,the proposed method for identifying OSA in children in this thesis can achieve better classification performance.