Author + information
- Received December 17, 2018
- Revision received January 23, 2019
- Accepted February 14, 2019
- Published online May 15, 2019.
- Kenya Kusunose, MD, PhDa,∗ (, )
- Takashi Abe, MD, PhDb,
- Akihiro Haga, PhDc,
- Daiju Fukuda, MD, PhDa,
- Hirotsugu Yamada, MD, PhDa,
- Masafumi Harada, MD, PhDb and
- Masataka Sata, MD, PhDa
- aDepartment of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan
- bDepartment of Radiology and Radiation Oncology, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
- cDepartment of Medical Image Informatics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
- ↵∗Address for correspondence:
Dr. Kenya Kusunose, Department of Cardiovascular Medicine, Tokushima University Hospital, 2-50-1 Kuramoto, Tokushima, Japan.
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). One-hundred 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.
Partially supported by Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI) grant 17K09506. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received December 17, 2018.
- Revision received January 23, 2019.
- Accepted February 14, 2019.
- 2019 American College of Cardiology Foundation
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