Feasibility validation of automatic diagnosis of mitral valve prolapse from multi-view echocardiographic sequences based on deep neural network.

European heart journal. Imaging methods and practice Pub Date : 2024-10-28 eCollection Date: 2024-10-01 DOI:10.1093/ehjimp/qyae086
Zijian Wu, Zhenyi Ge, Zhengdan Ge, Yumeng Xing, Weipeng Zhao, Lili Dong, Yongshi Wang, Dehong Kong, Chunqiang Hu, Yixiu Liang, Haiyan Chen, Wufeng Xue, Cuizhen Pan, Dong Ni, Xianhong Shu
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Abstract

Aims: To address the limitations of traditional diagnostic methods for mitral valve prolapse (MVP), specifically fibroelastic deficiency (FED) and Barlow's disease (BD), by introducing an automated diagnostic approach utilizing multi-view echocardiographic sequences and deep learning.

Methods and results: An echocardiographic data set, collected from Zhongshan Hospital, Fudan University, containing apical 2 chambers (A2C), apical 3 chambers (A3C), and apical 4 chambers (A4C) views, was employed to train the deep learning models. We separately trained view-specific and view-agnostic deep neural network models, which were denoted as MVP-VS and MVP view-agonistic (VA), for MVP diagnosis. Diagnostic accuracy, precision, sensitivity, F1-score, and specificity were evaluated for both BD and FED phenotypes. MVP-VS demonstrated an overall diagnostic accuracy of 0.94 for MVP. In the context of BD diagnosis, precision, sensitivity, F1-score, and specificity were 0.83, 1.00, 0.90, and 0.92, respectively. For FED diagnosis, the metrics were 1.00, 0.83, 0.91, and 1.00. MVP-VA exhibited an overall accuracy of 0.95, with BD-specific metrics of 0.85, 1.00, 0.92, and 0.94 and FED-specific metrics of 1.00, 0.83, 0.91, and 1.00. In particular, the MVP-VA model using mixed views for training demonstrated efficient diagnostic performance, eliminating the need for repeated development of MVP-VS models and improving the efficiency of the clinical pipeline by using arbitrary views in the deep learning model.

Conclusion: This study pioneers the integration of artificial intelligence into MVP diagnosis and demonstrates the effectiveness of deep neural networks in overcoming the challenges of traditional diagnostic methods. The efficiency and accuracy of the proposed automated approach suggest its potential for clinical applications in the diagnosis of valvular heart disease.

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基于深度神经网络从多视角超声心动图序列自动诊断二尖瓣脱垂的可行性验证。
目的:通过引入一种利用多视角超声心动图序列和深度学习的自动诊断方法,解决二尖瓣脱垂(MVP)传统诊断方法的局限性,特别是纤维弹性不足(FED)和巴洛氏病(BD):我们利用从复旦大学附属中山医院收集的超声心动图数据集来训练深度学习模型,该数据集包含心尖2腔(A2C)、心尖3腔(A3C)和心尖4腔(A4C)视图。我们分别训练了特定视图和视图拮抗深度神经网络模型,并将其命名为 MVP-VS 和 MVP 视图拮抗(VA),用于 MVP 诊断。对 BD 和 FED 表型的诊断准确度、精确度、灵敏度、F1-分数和特异性进行了评估。MVP-VS 对 MVP 的总体诊断准确率为 0.94。在 BD 诊断方面,精确度、灵敏度、F1-分数和特异性分别为 0.83、1.00、0.90 和 0.92。对于 FED 诊断,这些指标分别为 1.00、0.83、0.91 和 1.00。MVP-VA 的总体准确率为 0.95,BD 特定指标分别为 0.85、1.00、0.92 和 0.94,FED 特定指标分别为 1.00、0.83、0.91 和 1.00。特别是,使用混合视图训练的 MVP-VA 模型表现出了高效的诊断性能,无需重复开发 MVP-VS 模型,并通过在深度学习模型中使用任意视图提高了临床流水线的效率:本研究开创性地将人工智能整合到 MVP 诊断中,并证明了深度神经网络在克服传统诊断方法挑战方面的有效性。所提出的自动化方法的效率和准确性表明其在瓣膜性心脏病诊断中的临床应用潜力。
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