An Automated Heart Shunt Recognition Pipeline Using Deep Neural Networks

Weidong Wang; Hongme Zhang; Yizhen Li; Yi Wang; Qingfeng Zhang; Geqi Ding; Lixue Yin; Jinshan Tang; Bo Peng

doi:10.1007/s10278-024-01047-4

An Automated Heart Shunt Recognition Pipeline Using Deep Neural Networks

J Imaging Inform Med. 2024 Feb 22. doi: 10.1007/s10278-024-01047-4. Online ahead of print.

Authors

Weidong Wang¹, Hongme Zhang², Yizhen Li³, Yi Wang³, Qingfeng Zhang³, Geqi Ding³, Lixue Yin³, Jinshan Tang⁴, Bo Peng^{5

6}

Affiliations

¹ School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, Sichuan, China.
² Department of Cardiovascular Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. oiczhm@163.com.
³ Department of Cardiovascular Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
⁴ Department of Health Administration and Policy, College of Public Health, George Mason University, Fairfax, USA.
⁵ School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, Sichuan, China. bopeng@swpu.edu.cn.
⁶ Department of Cardiovascular Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. bopeng@swpu.edu.cn.

PMID: 38388868
DOI: 10.1007/s10278-024-01047-4

Abstract

Automated recognition of heart shunts using saline contrast transthoracic echocardiography (SC-TTE) has the potential to transform clinical practice, enabling non-experts to assess heart shunt lesions. This study aims to develop a fully automated and scalable analysis pipeline for distinguishing heart shunts, utilizing a deep neural network-based framework. The pipeline consists of three steps: (1) chamber segmentation, (2) ultrasound microbubble localization, and (3) disease classification model establishment. The study's normal control group included 91 patients with intracardiac shunts, 61 patients with extracardiac shunts, and 84 asymptomatic individuals. Participants' SC-TTE images were segmented using the U-Net model to obtain cardiac chambers. The segmentation results were combined with ultrasound microbubble localization to generate multivariate time series data on microbubble counts in each chamber. A classification model was then trained using this data to distinguish between intracardiac and extracardiac shunts. The proposed framework accurately segmented heart chambers (dice coefficient = 0.92 ± 0.1) and localized microbubbles. The disease classification model achieved high accuracy, sensitivity, specificity, F1 score, kappa value, and AUC value for both intracardiac and extracardiac shunts. For intracardiac shunts, accuracy was 0.875 ± 0.008, sensitivity was 0.891 ± 0.002, specificity was 0.865 ± 0.012, F1 score was 0.836 ± 0.011, kappa value was 0.735 ± 0.017, and AUC value was 0.942 ± 0.014. For extracardiac shunts, accuracy was 0.902 ± 0.007, sensitivity was 0.763 ± 0.014, specificity was 0.966 ± 0.008, F1 score was 0.830 ± 0.012, kappa value was 0.762 ± 0.017, and AUC value was 0.916 ± 0.006. The proposed framework utilizing deep neural networks offers a fast, convenient, and accurate method for identifying intracardiac and extracardiac shunts. It aids in shunt recognition and generates valuable quantitative indices, assisting clinicians in diagnosing these conditions.

Keywords: Deep learning; Disease classification; Heart shunts; Saline contrast transthoracic echocardiography.

Abstract

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