Abstract: Coarctation of aorta (CoA) is a congenital malformation of the aortic arch with a poor natural prognosis, which requires early intervention and even emergency surgery. Meanwhile, postoperative aortic re-coarctation is still a possible problem. At present, the prediction of aortic re-coarctation is mainly carried out based on the risk factor analysis of doctors on the clinical characteristics of patients combining with echocardiography (Ultra Sound Cardiogram) data, which is easy to be misdiagnosed. In this paper, a multimodal data detection framework based on Swin-Unet network is proposed based on the images of the patient's heart from computed tomography (CT) combining with the patient's clinical data. The framework carries out multimodal feature fusion analysis by combining the Swin-Unet network and the machine learning models, aiming to perform early detection of aortic re-coarctation. The experimental results on the clinical dataset show that our proposed methodeffectively improves the prediction effect of aortic re-coarctation when compared with the traditional prediction methods using clinical data. Particularly, we verifie the risk factors related to re-coarctation, the results of which provides a reference for clinical medicine.

Key words: coarctation of aorta, multimodal feature fusion, image segmentation