Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction

Xixiang Lin; Feifei Yang; Yixin Chen; Xiaotian Chen; Wenjun Wang; Xu Chen; Qiushuang Wang; Liwei Zhang; Huayuan Guo; Bohan Liu; Liheng Yu; Haitao Pu; Peifang Zhang; Zhenzhou Wu; Xin Li; Daniel Burkhoff; Kunlun He

doi:10.3389/fcvm.2022.903660

Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction

Front Cardiovasc Med. 2022 Aug 22:9:903660. doi: 10.3389/fcvm.2022.903660. eCollection 2022.

Authors

Xixiang Lin^{1

2}, Feifei Yang¹, Yixin Chen³, Xiaotian Chen³, Wenjun Wang¹, Xu Chen^{1

2}, Qiushuang Wang⁴, Liwei Zhang⁴, Huayuan Guo¹, Bohan Liu¹, Liheng Yu¹, Haitao Pu³, Peifang Zhang³, Zhenzhou Wu³, Xin Li⁵, Daniel Burkhoff⁶, Kunlun He¹

Affiliations

¹ Medical Big Data Center, Chinese PLA General Hospital, Beijing, China.
² Medical School of Chinese PLA, Beijing, China.
³ BioMind Technology, Beijing, China.
⁴ Fourth Medical Center of PLA General Hospital, Beijing, China.
⁵ Sixth Medical Center of PLA General Hospital, Beijing, China.
⁶ Cardiovascular Research Foundation, New York, NY, United States.

Abstract

Objective: To compare the performance of a newly developed deep learning (DL) framework for automatic detection of regional wall motion abnormalities (RWMAs) for patients presenting with the suspicion of myocardial infarction from echocardiograms obtained with portable bedside equipment versus standard equipment.

Background: Bedside echocardiography is increasingly used by emergency department setting for rapid triage of patients presenting with chest pain. However, compared to images obtained with standard equipment, lower image quality from bedside equipment can lead to improper diagnosis. To overcome these limitations, we developed an automatic workflow to process echocardiograms, including view selection, segmentation, detection of RWMAs and quantification of cardiac function that was trained and validated on image obtained from bedside and standard equipment.

Methods: We collected 4,142 examinations from one hospital as training and internal testing dataset and 2,811 examinations from other hospital as the external test dataset. For data pre-processing, we adopted DL model to automatically recognize three apical views and segment the left ventricle. Detection of RWMAs was achieved with 3D convolutional neural networks (CNN). Finally, DL model automatically measured the size of cardiac chambers and left ventricular ejection fraction.

Results: The view selection model identified the three apical views with an average accuracy of 96%. The segmentation model provided good agreement with manual segmentation, achieving an average Dice of 0.89. In the internal test dataset, the model detected RWMAs with AUC of 0.91 and 0.88 respectively for standard and bedside ultrasound. In the external test dataset, the AUC were 0.90 and 0.85. The automatic cardiac function measurements agreed with echocardiographic report values (e. g., mean bias is 4% for left ventricular ejection fraction).

Conclusion: We present a fully automated echocardiography pipeline applicable to both standard and bedside ultrasound with various functions, including view selection, quality control, segmentation, detection of the region of wall motion abnormalities and quantification of cardiac function.

Keywords: artificial intelligence - AI; bedside ultrasound; deep learning; echocardiography; myocardial infarction.