A novel intelligent grade classification architecture for Patent Foramen Ovale by Contrast Transthoracic Echocardiography based on deep learning

Abstract

Patent foramen ovale (PFO) is one of the main causes of ischemic stroke. Due to the complex characteristics of contrast transthoracic echocardiography (cTTE), PFO classification is time-consuming and laborious in clinical practice. For this reason, a variety of PFO diagnostic models have been presented based on machine learning in recent years. However, existing models have lower diagnostic accuracy due to similar gray values of microbubbles and surrounding myocardial tissue in cTTE. Meanwhile, the greater volume of right-to-left shunt (RLS) volume leads to a higher incidence of migraine and stroke. Existing models do not quantify the severity of RLS, which affects the use of treatment methods in later clinical treatment. To solve these problems, we propose TVUNet++ for left ventricular segmentation and ULSAM-ResNet for PFO classification. More specifically, TVUNet++ can distinguish various local features in cTTE through learnable affinity maps and implicitly capture the semantic relationship between the left heart cavity and the background region. In addition, we provide a benchmark cTTE dataset to evaluate the performance of the proposed model through various experiments. Experimental results show that the average Dice Coefficient of the proposed model can reach 92.11%. Moreover, ULSAM-ResNet can realize multi-scale and multi-frequency feature learning through multiple subspaces and learn cross-channel information for accurate grade classification efficiently. The average recall of static cTTE can reach 84.27%. Furthermore, the proposed model outperforms state-of-the-art models in the grade classification of PFO.