Kenya Kusunose1, Takashi Abe2, Akihiro Haga3, Daiju Fukuda4, Hirotsugu Yamada4, Masafumi Harada2, Masataka Sata4. 1. Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan. Electronic address: kusunosek@tokushima-u.ac.jp. 2. Department of Radiology and Radiation Oncology, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan. 3. Department of Medical Image Informatics, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan. 4. Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.
Abstract
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.
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.
Authors: Akhil Narang; Richard Bae; Ha Hong; Yngvil Thomas; Samuel Surette; Charles Cadieu; Ali Chaudhry; Randolph P Martin; Patrick M McCarthy; David S Rubenson; Steven Goldstein; Stephen H Little; Roberto M Lang; Neil J Weissman; James D Thomas Journal: JAMA Cardiol Date: 2021-06-01 Impact factor: 14.676
Authors: Carlos Martin-Isla; Victor M Campello; Cristian Izquierdo; Zahra Raisi-Estabragh; Bettina Baeßler; Steffen E Petersen; Karim Lekadir Journal: Front Cardiovasc Med Date: 2020-01-24