Graphical abstract: Study design and main results: Two test-retest data sets were collected at 2 different centers (data set I and data set II). For both data sets, 2 different echocardiographers acquired separate recordings in immediate succession in each patient to create a test-retest pair of images (upper panel). For each patient, these test-retest recordings were analyzed by 4 readers who measured global longitudinal strain (GLS) using a commercially available semiautomatic method. This resulted in 12 interreader and 4 intrareader scenarios. In addition, GLS was measured using an artificial intelligence (AI) method based on deep learning. The test-retest measurement bias and minimal detectable change for all inter- and intrareader scenarios and the AI scenario are presented for both data sets (lower panels). Artificial intelligence–based measurements eliminated test-retest bias between readers and resulted in reduced test-retest variability.
ObjectiveTo validate an artificial intelligence–augmented electrocardiogram (AI-ECG) algorithm for the detection of preclinical left ventricular systolic dysfunction (LVSD) in a large community-based cohort.MethodsWe identified a randomly selected community-based cohort of 2041 subjects age 45 years or older in Olmsted County, Minnesota. All participants underwent a study echocardiogram and ECG. We first assessed the performance of the AI-ECG to identify LVSD (ejection fraction ≤40%). After excluding participants with clinical heart failure, we further assessed the AI-ECG to detect preclinical LVSD among all patients (n=1996) and in a high-risk subgroup (n=1348). Next we modelled an imputed screening program for preclinical LVSD detection where a positive AI-ECG triggered an echocardiogram. Finally, we assessed the ability of the AI-ECG to predict future LVSD. Participants were enrolled between January 1, 1997, and September 30, 2000; and LVSD surveillance was performed for 10 years after enrollment.ResultsFor detection of LVSD in the total population (prevalence, 2.0%), the area under the receiver operating curve for AI-ECG was 0.97 (sensitivity, 90%; specificity, 92%); in the high-risk subgroup (prevalence 2.7%), the area under the curve was 0.97 (sensitivity, 92%; specificity, 93%). In an imputed screening program, identification of one preclinical LSVD case would require 88.3 AI-ECGs and 8.7 echocardiograms in the total population and 65.7 AI-ECGs and 5.5 echocardiograms in the high-risk subgroup. The unadjusted hazard ratio for a positive AI-ECG for incident LVSD over 10 years was 2.31 (95% CI, 1.32 to 4.05; P=.004).ConclusionArtificial intelligence–augmented ECG can identify preclinical LVSD in the community and warrants further study as a screening tool for preclinical LVSD. 相似文献
