Predicting Intracardiac Thrombus Formation in the Left Atrial Appendage Using Machine Learning and CT Images.

This paper proposes a comprehensive method for estimating thrombus formation factors in the left atrial appendage (LAA). First, using 3D CT (Computer Tomography) image data as input, classification of thrombus presence/absence is learned using 3D ResNet. Besides, 3D Grad-CAM is applied to the prediction results to visualize regions of interest in thrombus formation. Second, features are extracted based on the visualization of regions of interest. Using the extracted features and numerical data obtained from the hospital as input, a regression analysis is performed to predict the presence/absence of thrombus using LightGBM. Visualization of regions of interest using 3D ResNet and 3D Grad-CAM shows that the right inferior pulmonary vein and the LAA were particularly correlated with thrombus formation. Estimation of important factors for thrombus formation using LightGBM shows that the LAA ostium area has the greatest influence on thrombus formation.Clinical Relevance-This paper shows the factors that contribute to thrombus formation in the LAA from the viewpoint of three-dimensional structure. In addition, the features considered important in thrombus formation were identified by comparing a variety of features.

Classification of Aortic Stenosis Using ECG by Deep Learning and its Analysis Using Grad-CAM.

This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using ECG (Electrocardiogram) images by the deep learning whose training ECG images are annotated by the diagnoses given by the medical doctor who observes the echocardiograms. Besides, it explores the relationship between the trained deep learning network and its determinations, using the Grad-CAM.In this study, one-beat ECG images for 12-leads and 4-leads are generated from ECG's and train CNN's (Convolutional neural network). By applying the Grad-CAM to the trained CNN's, feature areas are detected in the early time range of the one-beat ECG image. Also, by limiting the time range of the ECG image to that of the feature area, the CNN for the 4-lead achieves the best classification performance, which is close to expert medical doctors' diagnoses.Clinical Relevance-This paper achieves as high AS classification performance as medical doctors' diagnoses based on echocardiograms by proposing an automatic method for detecting AS only using ECG.