一种基于深度学习的超声心动图图像区域性壁运动异常评估方法。
A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.
机构信息
Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.
Department of Radiology and Radiation Oncology, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan.
出版信息
JACC Cardiovasc Imaging. 2020 Feb;13(2 Pt 1):374-381. doi: 10.1016/j.jcmg.2019.02.024. Epub 2019 May 15.
OBJECTIVES
This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional 2-dimensional echocardiographic images compared with that of cardiologists, sonographers, and resident readers.
BACKGROUND
An effective intervention for reduction of misreading of RWMAs is needed. The hypothesis was that a DCNN trained using echocardiographic images would provide improved detection of RWMAs in the clinical setting.
METHODS
A total of 300 patients with a history of myocardial infarction were enrolled. From this cohort, 3 groups of 100 patients each had infarctions of the left anterior descending (LAD) artery, the left circumflex (LCX) branch, and the right coronary artery (RCA). A total of 100 age-matched control patients with normal wall motion were selected from a database. Each case contained cardiac ultrasonographs from short-axis views at end-diastolic, mid-systolic, and end-systolic phases. After the DCNN underwent 100 steps of training, diagnostic accuracies were calculated from the test set. Independently, 10 versions of the same model were trained, and ensemble predictions were performed using those versions.
RESULTS
For detection of the presence of WMAs, the area under the receiver-operating characteristic curve (AUC) produced by the deep learning algorithm was similar to that produced by the cardiologists and sonographer readers (0.99 vs. 0.98, respectively; p = 0.15) and significantly higher than the AUC result of the resident readers (0.99 vs. 0.90, respectively; p = 0.002). For detection of territories of WMAs, the AUC by the deep learning algorithm was similar to the AUC by the cardiologist and sonographer readers (0.97 vs. 0.95, respectively; p = 0.61) and significantly higher than the AUC by resident readers (0.97 vs. 0.83, respectively; p = 0.003). From a validation group at an independent site (n = 40), the AUC by the deep learning algorithm was 0.90.
CONCLUSIONS
The present results support the possibility of using DCNN for automated diagnosis of RWMAs in the field of echocardiography.
目的
本研究旨在探讨与心脏病专家、超声医师和住院医师读者相比，深度卷积神经网络（DCNN）是否能够从传统的二维超声心动图图像中更准确地检测区域性壁运动异常（RWMAs）并区分不同的冠状动脉梗死区域。
背景
需要一种有效的干预措施来减少 RWMAs 的误读。假设经过超声心动图图像训练的 DCNN 将提高临床环境下 RWMAs 的检测能力。
方法
共纳入 300 例有心肌梗死病史的患者。从该队列中，每组 100 例患者分别患有左前降支（LAD）、左旋支（LCX）和右冠状动脉（RCA）梗死。从数据库中选择 100 例年龄匹配的正常壁运动的对照组患者。每个病例均包含舒张末期、收缩中期和收缩末期的短轴超声心动图。在 DCNN 经过 100 个步骤的训练后，从测试集中计算诊断准确率。独立地，训练了 10 个相同模型的版本，并使用这些版本进行集成预测。
结果
对于 WMAs 存在的检测，深度学习算法产生的受试者工作特征曲线（ROC）下面积（AUC）与心脏病专家和超声医师读者产生的 AUC 相似（分别为 0.99 与 0.98；p=0.15），明显高于住院医师读者产生的 AUC（0.99 与 0.90；p=0.002）。对于 WMAs 区域的检测，深度学习算法的 AUC 与心脏病专家和超声医师读者的 AUC 相似（分别为 0.97 与 0.95；p=0.61），明显高于住院医师读者的 AUC（0.97 与 0.83；p=0.003）。在一个独立地点的验证组（n=40）中，深度学习算法的 AUC 为 0.90。
结论
本研究结果支持使用 DCNN 对超声心动图领域的 RWMAs 进行自动诊断的可能性。
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