Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review

Abstract

Screening for left ventricular systolic dysfunction (LVSD), defined as reduced left ventricular ejection fraction (LVEF), deserves renewed interest as the medical treatment for the prevention and progression of heart failure improves. We aimed to review the updated literature to outline the potential and caveats of using artificial intelligence-enabled electrocardiography (AIeECG) as an opportunistic screening tool for LVSD. We searched PubMed and Cochrane for variations of the terms "ECG," "Heart Failure," "systolic dysfunction," and "Artificial Intelligence" from January 2010 to April 2022 and selected studies that reported the diagnostic accuracy and confounders of using AIeECG to detect LVSD. Out of 40 articles, we identified 15 relevant studies; eleven retrospective cohorts, three prospective cohorts, and one case series. Although various LVEF thresholds were used, AIeECG detected LVSD with a median AUC of 0.90 (IQR from 0.85 to 0.95), a sensitivity of 83.3% (IQR from 73 to 86.9%) and a specificity of 87% (IQR from 84.5 to 90.9%). AIeECG algorithms succeeded across a wide range of sex, age, and comorbidity and seemed especially useful in non-cardiology settings and when combined with natriuretic peptide testing. Furthermore, a false-positive AIeECG indicated a future development of LVSD. No studies investigated the effect on treatment or patient outcomes. This systematic review corroborates the arrival of a new generic biomarker, AIeECG, to improve the detection of LVSD. AIeECG, in addition to natriuretic peptides and echocardiograms, will improve screening for LVSD, but prospective randomized implementation trials with added therapy are needed to show cost-effectiveness and clinical significance.

Artificial intelligence-based identification of left ventricular systolic dysfunction from 12-lead electrocardiograms: external validation and advanced application of an existing model

“… 5–9 However, independent external validation is not available for all of the published models. 10 , 11 A high variance of performance results for external validation studies reflects both the necessity and difficulty of such analyses, which are influenced by the setting of model application. 12 Validating an algorithm in an unrelated patient population by a different group of researchers is necessitated to prove its reliability and overcome possible systematic bias.…”

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study

“…While neural network-based models have previously been designed to detect prevalent systolic dysfunction or HF, [27][28][29][30][31][32][33][34] our study suggests the role of an AI-ECG model as a biomarker for new-onset HF. This AI-based approach can enable opportunistic HF screening for patients undergoing clinical ECGs and also facilitate population-based screening approaches for HF.…”

“…[11][12][13]21 Recently, artificial intelligence (AI)-enhanced interpretation of electrocardiograms (ECGs; AI-ECG) has been proposed to detect hidden cardiovascular disease signatures from 12-lead ECGs. [22][23][24][25][26][27][28][29][30] However, these deep learning models have focused on the cross-sectional detection of prevalent systolic dysfunction or HF, [27][28][29][30][31][32][33][34] with limited application in predicting incident HF. 27,29,35,36 Moreover, most current approaches use raw ECG voltage data as inputs, inaccessible to clinicians and patients at the point-of-care.…”

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment

“…Applications of deep learning for ECGs have demonstrated the ability to identify subtle signatures of structural heart disorders previously considered electrically silent, [40][41][42][43][44][45][46][47][48] with applications extending to detecting LVSD from single-lead tracings. 20,[49][50][51] Recently, the US Food and Drug Administration also granted clearance to an AI tool using electronic stethoscope-based single-lead ECGs for cross-sectional LVSD detection.…”