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Background: Left atrial enlargement (LAE) can independently predict the development of a variety of cardiovascular diseases.Objectives: This study sought to develop an artificial intelligence approach for the detection of LAE based on 12-lead electrocardiography (ECG).Methods: The study population came from an epidemiological survey of heart disease in Guangzhou. Elderly people (3,391) over 65 years old who had both 10-s 12 lead ECG and echocardiography were enrolled in this study. The left atrial (LA) anteroposterior diameter >40 mm on echocardiography was diagnosed as LAE, and the LA anteroposterior diameter was indexed by body surface area (BSA) to classify LAE into different degrees. A convolutional neural network (CNN) was trained and validated to detect LAE from normal ECGs. The performance of the model was evaluated by calculating the area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score.Results: In this study, gender, obesity, hypertension, and valvular heart disease seemed to be related to left atrial enlargement. The AI-enabled ECG identified LAE with an AUC of 0.949 (95% CI: 0.911–0.987). The sensitivity, specificity, accuracy, precision, and F1 score were 84.0%, 92.0%, 88.0%, 91.3%, and 0.875, respectively. Physicians identified LAE with sensitivity, specificity, accuracy, precision, and F1 scores of 38.0%, 84.0%, 61.0%, 70.4%, and 0.494, respectively. In classifying LAE in different degrees, the AUCs of identifying normal, mild LAE, and moderate-severe LAE ECGs were 0.942 (95% CI: 0.903–0.981), 0.951 (95% CI: 0.917–0.987), and 0.998 (95% CI: 0.996–1.00), respectively. The sensitivity, specificity, accuracy, positive predictive value, and F1 scores of diagnosing mild LAE were 82.0%, 92.0%, 88.7%, 89.1%, and 0.854, while the sensitivity, specificity, accuracy, positive predictive value, and F1 scores of diagnosing moderate-severe LAE were 98.0%, 84.0%, 88.7%, 96.1%, and 0.969, respectively.Conclusions: An AI-enabled ECG acquired during sinus rhythm permits identification of individuals with a high likelihood of LAE. This model requires further refinement and external validation, but it may hold promise for LAE screening.

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Aim. We aimed to evaluate the effectiveness and safety between high-power short-duration (HPSD) radiofrequency ablation (RFA) and conventional RFA in patients with atrial fibrillation (AF). Methods. Studies comparing HPSD and traditional applications in patients undergoing initial catheter ablation for atrial fibrillation from inception through December 2021 were searched on Pubmed, Medline, Cochrane, and Clinicaltrials.gov. Results. The meta-analysis included seventeen studies with a total of 4934 patients. HPSD group decreased procedure duration (mean difference (MD) −38.28 min, P < 0.001 ), RF duration (MD −20.51 min, P < 0.001 ), fluoroscopy duration (MD −5.19 min, P < 0.001 ), and acute pulmonary vein reconnection (Odds ratio (OR) 0.40, P < 0.001 ), while improving the freedom from atrial arrhythmia at one year (OR 1.48, 95% confidence interval (CI) 1.12–1.94, P = 0.005 ) and rates of first-pass isolation (OR 8.92, P = 0.001 ). Compared with the conventional group, freedom from atrial arrhythmia at one-year follow-up was higher in the HPSD group without the guidance of AI/LSI (OR 1.66, P = 0.01 ) and studies with a power setting of 40–50 W (OR 1.93, P = 0.002 ). Nevertheless, the two groups had similar effectiveness with a power setting of 50 W in the HPSD RFA (OR 1.10, P = 0.52 ). There was no difference in complications between the two groups ( P = 0.71 ). Conclusion. HPSD RFA was associated with shorter procedure duration, higher freedom from atrial arrhythmia, and comparable safety compared to conventional RFA.