Detection of Left Atrial Enlargement Using a Convolutional Neural Network-Enabled Electrocardiogram
| Autor: | Shulin Wu, Yumei Xue, Hongtao Liao, Jun-Rong Jiang, Hai Deng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Diseases of the circulatory (Cardiovascular) system
medicine.medical_specialty Heart disease 030204 cardiovascular system & hematology Cardiovascular Medicine artificial intelligence (AI) 03 medical and health sciences 0302 clinical medicine Internal medicine medicine Left atrial enlargement echocardiography Sinus rhythm left atrial enlargement (LAE) 030212 general & internal medicine Original Research Body surface area convolutional neural network (CNN) medicine.diagnostic_test business.industry valvular heart disease Area under the curve medicine.disease electrocardiogram (ECG) lcsh:RC666-701 Cardiology Population study Cardiology and Cardiovascular Medicine business Electrocardiography |
| Zdroj: | Frontiers in Cardiovascular Medicine Frontiers in Cardiovascular Medicine, Vol 7 (2020) |
| ISSN: | 2297-055X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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