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

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          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.

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          Deep learning.

          Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)
          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

            Zachi Attia, Peter A. Noseworthy, Francisco López-Jiménez … (2019)
            Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning.
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              Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram

              Zachi Attia, Suraj Kapa, Francisco López-Jiménez … (2019)
              Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found1-4. An inexpensive, noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.
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                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Cardiovasc Med
                Journal ID (iso-abbrev): Front Cardiovasc Med
                Journal ID (publisher-id): Front. Cardiovasc. Med.
                Title: Frontiers in Cardiovascular Medicine
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 2297-055X
                Publication date (Electronic): 17 December 2020
                Publication date Collection: 2020
                Volume: 7
                Electronic Location Identifier: 609976
                Affiliations
                [1] 1School of Medicine, South China University of Technology , Guangzhou, China
                [2] 2Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences , Guangzhou, China
                Author notes

                Edited by: Chen Liu, First Affiliated Hospital of Sun Yat-sen University, China

                Reviewed by: Tong Liu, Tianjin Medical University, China; John David Horowitz, University of Adelaide, Australia

                *Correspondence: Shulin Wu doctor_wushulin@ 123456126.com

                This article was submitted to General Cardiovascular Medicine, a section of the journal Frontiers in Cardiovascular Medicine

                Article
                DOI: 10.3389/fcvm.2020.609976
                PMC ID: 7773668
                PubMed ID: 33392274
                SO-VID: a4abcf7a-7c5c-49c8-b220-8ce933b1d5aa
                Copyright © Copyright © 2020 Jiang, Deng, Xue, Liao and Wu.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 24 September 2020
                Date accepted : 16 November 2020
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 35, Pages: 8, Words: 5084
                Categories
                Subject: Cardiovascular Medicine
                Subject: Original Research

                Keywords: left atrial enlargement (lae),convolutional neural network (cnn),electrocardiogram (ecg),echocardiography,artificial intelligence (ai)
                Data availability:
                Keywords: left atrial enlargement (lae), convolutional neural network (cnn), electrocardiogram (ecg), echocardiography, artificial intelligence (ai)

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