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

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.

Keywords: Barlow’s disease; automated diagnosis; deep learning; echocardiography; fbroelastic deficiency; mitral valve prolapse.

Graphical Abstract

Figure 1

The image presents a sample echocardiographic image of the different types of MVP and healthy controls in A2C, A3C, and A4C views.

Figure 2

The sample quantities in the patient data set and view-specific data set.

Figure 3

The training and testing process of MVP-VS model.

Figure 4

The training and testing process of MVP-VA model.