Detection of Left Atrial Enlargement Using a Convolutional Neural Network-Enabled Electrocardiogram

Abstract

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.

Artificial Intelligence-Enabled Electrocardiogram Estimates Left Atrium Enlargement as a Predictor of Future Cardiovascular Disease

“…Likewise, our ECG-LAE with high NPVs gave us the opportunity to exclude patients in clinical practice. Although a previous study suggested a DLM to diagnose LAE with an AUC of 0.95 in a test dataset of 50 ECGs [27], our larger validation datasets provide a more realistic result in the real world. Moreover, we demonstrated the continuous predictions of the LA diameter with MAEs of 5.87/5.74 in the internal/external validation set based on our larger development set.…”

“…A previous study applied a DLM to segment an ECG and trained it with a traditional machine learning model for detecting LAE, which had a poor performance with an AUC of 0.62 [26]. Using DLM technology, a prior study showed an AUC of 0.95 for detecting LAE in a test dataset of 50 ECGs [27]. Although the superiority of DLMs has been demonstrated in LAE detection via an ECG, there has been no large-scale study to validate its performance in clinical practice.…”

Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study

“…The study was the first report to show a better performance of machine learning to predict echocardiographic LAE compared to the traditional ECG criterion of P wave duration in young male adults who had a healthy status and without multiple comorbidities. Prior studies (29)(30)(31) have revealed that machine learning for ECG features could detect most of the LAE cases from hospitalized patients, probably due to those patients with LAE who were likely to have other cardiac comorbidities, such as heart failure, that were easily reflected by ECG features; thus, the results might not be appropriate for healthy individuals. Some studies have shown that, in young adults, particularly physically fit people, an enlarged cardiac chamber is likely, and the typical ECG features for LAE might not be the same as those in middle-aged individuals and elderly individuals who had several cardiovascular comorbidities, i.e., hypertension.…”

“…The P wave duration was also a predictor of atrial fibrillation, cardiovascular death, and early vascular aging (27,28). Over the past 5 years, there were only some hospital-based studies utilizing machine learning for ECG features to detect the presence of LAE, in which the area under the curve (AUC) of the receiver operating characteristic curve (ROC) varied much from 0.62 to 0.98 (29)(30)(31). However, there were no previous reports performed in the general population.…”

Development and Validation of a Deep-Learning Model to Detect CRP Level from the Electrocardiogram

“…The sample dimension was 12 × 5,000 (12 leads by 10-s duration sampled at 500 Hz). In order to remove the baseline drift ( Figure 2A ) and noise ( Figure 2B ), following our previous approach ( Jiang et al, 2020 ), the raw data was first filtered by using a low-pass filter to get the baseline, then the baseline was flattened by zeroing the mean ( Figure 2C ), and denoising was achieved by filtering out the high frequency signal ( Figure 2D ). Since any linear function of the leads could be learned by the models and more artifacts were contained within the first and last 1-s periods, to optimize performance, only 8-s of eight independent leads (leads I, II, and V1–6) were selected.…”