Automatic quantitative analysis of atherosclerotic aortic plaques in patients with embolic cerebral infarction using deep learning
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Background/Aims: Transesophageal echocardiography (TEE) is a commonly used imaging modality for assessing embolic stroke of undetermined source (ESUS) in clinical practice. We aimed to develop an automatic plaque segmentation model based on U-net and evaluate its clinical usefulness in patients with ESUS. Methods: We used two aorta image sets. TEE aortic images of 711 patients visiting two cardiovascular centers for various causes were randomly divided into training, validation, and test sets to automatically segment plaques and estimate the aortic plaque area (APA) and aortic plaque ratio (APR) using U-net. The model was tested in a clinical data set of patients with ESUS who attended three cardiovascular centers to determine whether it could predict a composite cardiovascular event in those patients. Results: The mean intersection of over union to assess the accuracy of the U-net model was 0.997 ± 0.002 and 0.997 ± 0.001 for the model development and clinical application data sets, respectively. When using the U-net–based model, the APA and APR significantly differed between complex and simple aortic plaques (p < 0.001). However, unlike complex aortic plaques measured in clinical practice, APA or APR estimated by U-net models or manual segmentation did not show additional value in predicting major adverse cardiovascular and cerebrovascular events. Conclusions: The estimation of APA and APR by the U-net model could be helpful in predicting complex aortic plaques. Additional comprehensive quantitative image analysis of plaque characteristics using artificial intelligence, such as movability and morphology, may be needed to predict prognosis.
Abstract
Background/Aims: Transesophageal echocardiography (TEE) is a commonly used imaging modality for assessing embolic stroke of undetermined source (ESUS) in clinical practice. We aimed to develop an automatic plaque segmentation model based on U-net and evaluate its clinical usefulness in patients with ESUS. Methods: We used two aorta image sets. TEE aortic images of 711 patients visiting two cardiovascular centers for various causes were randomly divided into training, validation, and test sets to automatically segment plaques and estimate the aortic plaque area (APA) and aortic plaque ratio (APR) using U-net. The model was tested in a clinical data set of patients with ESUS who attended three cardiovascular centers to determine whether it could predict a composite cardiovascular event in those patients. Results: The mean intersection of over union to assess the accuracy of the U-net model was 0.997 ± 0.002 and 0.997 ± 0.001 for the model development and clinical application data sets, respectively. When using the U-net–based model, the APA and APR significantly differed between complex and simple aortic plaques (p < 0.001). However, unlike complex aortic plaques measured in clinical practice, APA or APR estimated by U-net models or manual segmentation did not show additional value in predicting major adverse cardiovascular and cerebrovascular events. Conclusions: The estimation of APA and APR by the U-net model could be helpful in predicting complex aortic plaques. Additional comprehensive quantitative image analysis of plaque characteristics using artificial intelligence, such as movability and morphology, may be needed to predict prognosis.