标题： 新生儿肺动脉高压预测：一种多视图变分自编码器方法 显示英文标题

标题： Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach

摘要： 新生儿肺动脉高压（PH）是一种危及生命的状况，其特征是肺动脉压力升高，导致右心室负担加重和心力衰竭。 虽然右心导管检查（RHC）是诊断的金标准，但超声心动图因其无创性、安全性和可及性而被优先选用。 然而，其准确性高度依赖于操作者，使得PH评估具有主观性。 虽然已经探索了自动检测方法，但大多数模型集中在成人身上，并依赖单视角超声心动图帧，限制了它们在诊断新生儿PH中的表现。 虽然多视角超声心动图在改善PH评估方面显示出潜力，但现有模型在泛化能力上仍存在困难。 在本工作中，我们采用多视角变分自编码器（VAE）来使用超声心动图视频进行PH预测。 通过利用VAE框架，我们的模型捕捉复杂的潜在表示，提高了特征提取和鲁棒性。 我们将其性能与单视角和监督学习方法进行了比较。 我们的结果表明，泛化能力和分类准确率有所提高，突显了多视角学习在新生儿中进行稳健PH评估的有效性。 显示英文摘要

摘要： Pulmonary hypertension (PH) in newborns is a critical condition characterized by elevated pressure in the pulmonary arteries, leading to right ventricular strain and heart failure. While right heart catheterization (RHC) is the diagnostic gold standard, echocardiography is preferred due to its non-invasive nature, safety, and accessibility. However, its accuracy highly depends on the operator, making PH assessment subjective. While automated detection methods have been explored, most models focus on adults and rely on single-view echocardiographic frames, limiting their performance in diagnosing PH in newborns. While multi-view echocardiography has shown promise in improving PH assessment, existing models struggle with generalizability. In this work, we employ a multi-view variational autoencoder (VAE) for PH prediction using echocardiographic videos. By leveraging the VAE framework, our model captures complex latent representations, improving feature extraction and robustness. We compare its performance against single-view and supervised learning approaches. Our results show improved generalization and classification accuracy, highlighting the effectiveness of multi-view learning for robust PH assessment in newborns.