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Background and Aims: To evaluate the risk of coronary artery disease (CAD), the traditional approach involves assessing the patient's symptoms, traditional cardiovascular risk factors (CVRFs), and a 12-lead electrocardiogram (ECG). However, currently, there are no established criteria for interpreting an ECG to diagnose CAD. Therefore, we sought to develop an artificial intelligence (AI)-enabled ECG model to assist in identifying patients with CAD., Methods: In this study, we included patients who underwent coronary angiography (CAG) at a single center between 2017 and 2019. Preprocedural 12-lead ECG performed within 24 h was obtained. Obstructive CAD was defined as ≥ 50% diameter stenosis. Using age, gender and ECG data, we developed stacking models using both deep learning and machine learning. Then we compared the performance of our models with CVRFs and with cardiologists' ECG interpretation. Additionally, we validated our model on an external cohort from a different hospital., Results: We included 4951 patients with a mean age of 65.5 ± 12.5 years, of whom 67.0% were men. Based on CAG, obstructive CAD was confirmed in 2637 patients (53.2%). Our best AI model demonstrated comparable performance to CVRFs in predicting CAD, with an AUC of 0.70 (95% CI: 0.66-0.75) compared to 0.71 (95% CI: 0.66-0.76). The sensitivity and specificity of the AI model were 0.75 and 0.54, respectively, while those of CVRFs were 0.67 and 0.63. Compared to cardiologists, the AI model showed better performance with an F1 score of 0.68 vs 0.41. The external validation showed generally consistent diagnostic findings, although there was a slightly lower level of agreement observed in the external cohort. Incorporating ECG and CVRFs improved the AUC to 0.72., Conclusions: Our study suggests that an AI-enabled ECG model can assist in identifying patients with obstructive CAD, with diagnostic performance similar to that of the traditional approach based on CVRFs. This model could serve as a useful clinical tool in an outpatient setting to identify patients who require further diagnostic tests., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Constrictive pericarditis (CP) is an uncommon but reversible cause of diastolic heart failure if appropriately identified and treated. However, its diagnosis remains a challenge for clinicians. Artificial intelligence may enhance the identification of CP. The authors proposed a deep learning approach based on transthoracic echocardiography to differentiate CP from restrictive cardiomyopathy. Patients with a confirmed diagnosis of CP and cardiac amyloidosis (CA) (as the representative disease of restrictive cardiomyopathy) at Mayo Clinic Rochester from January 2003 to December 2021 were identified to extract baseline demographics. The apical 4-chamber view from transthoracic echocardiography studies was used as input data. The patients were split into a 60:20:20 ratio for training, validation, and held-out test sets of the ResNet50 deep learning model. The model performance (differentiating CP and CA) was evaluated in the test set with the area under the curve. GradCAM was used for model interpretation. A total of 381 patients were identified, including 184 (48.3%) CP, and 197 (51.7%) CA cases. The mean age was 68.7 ± 11.4 years, and 72.8% were male. ResNet50 had a performance with an area under the curve of 0.97 to differentiate the 2-class classification task (CP vs CA). The GradCAM heatmap showed activation around the ventricular septal area. With a standard apical 4-chamber view, our artificial intelligence model provides a platform to facilitate the detection of CP, allowing for improved workflow efficiency and prompt referral for more advanced evaluation and intervention of CP. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Ruling out obstructive coronary artery disease (CAD) using coronary computed tomography angiography (CCTA) is time‐consuming and challenging. This study developed a deep learning (DL) model to assist in detecting obstructive CAD on CCTA to streamline workflows. Methods: In total, 2929 DICOM files and 7945 labels were extracted from curved planar reformatted CCTA images. A modified Inception V3 model was adopted. To validate the artificial intelligence (AI) model, two cardiologists labelled and adjudicated the classification of coronary stenosis on CCTA. The model was trained to differentiate the coronary artery into binary stenosis classifications <50% and ≥50% stenosis. Using the quantitative coronary angiography (QCA) consensus results as a reference standard, the performance of the AI model and CCTA radiology readers was compared by calculating Cohen's kappa coefficients at patient and vessel levels. The net reclassification index was used to evaluate the net benefit of the DL model. Results: The diagnostic accuracy of the AI model was 92.3% and 88.4% at the patient and vessel levels, respectively. Compared with CCTA radiology readers, the AI model had a better agreement for binary stenosis classification at both patient and vessel levels (Cohen kappa coefficient:.79 vs..39 and.77 vs..40, p <.0001). The AI model also exhibited significantly improved model discrimination and reclassification (Net reclassification index =.350; Z = 4.194; p <.001). Conclusions: The developed AI model identified obstructive CAD, and the model results correlated well with QCA results. Incorporating the model into the reporting system of CCTA may improve workflows. [ABSTRACT FROM AUTHOR]

This paper proposes an Adaptive Fuzzy Neural Agent (AFNA) with a Patch Learning Mechanism and IEEE 1855 Fuzzy Markup Language (FML) for human and machine co-learning. There are three phases of patch learning mechanism embedded in AFNA, including (1) training an initial global model, (2) training a patch model for each identified patch, and (3) updating the global model using the training data that do not fall into any patch. The AFNA can be applied to construct the student and robot co-learning regression model, as well as the regression model for the dataset retrieved from the game of Go. First, students generate human learning data through interactions with handheld devices or robots based on the AFNA in Taiwan and Japan. Then, the AFNA utilizes the student learning data collected in the classroom and the Go game data provided by both Google DeepMind and Facebook AI Research open-source OpenGo to train the Fuzzy Machine-Learning Model. In addition, the trained Fuzzy Machine-Learning Model of AFNA is deployed to the robots to make students and machines co-learn together based on IEEE 1855 FML. The experiments show that the AFNA with Patch Learning Mechanism and Fuzzy Machine-Learning Model can improve the performance of regression model based on the datasets of student learning and Go game. In the future, we hope to apply the AFNA with robots to the other domain areas, embed it with the Artificial Intelligence of Things devices, and introduce it to more teaching fields in various countries. [ABSTRACT FROM AUTHOR]