1. AI derived ECG global longitudinal strain compared to echocardiographic measurements
- Author
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Hong-Mi Choi, Joonghee Kim, Jiesuck Park, Jun-Bean Park, Hyung-Kwan Kim, Hye Jung Choi, Yeonyee E. Yoon, Goo-Yeong Cho, Youngjin Cho, and In-Chang Hwang
- Subjects
Artificial intelligence ,Electrocardiography ,Global longitudinal strain ,Heart failure ,Prognosis ,Medicine ,Science - Abstract
Abstract Left ventricular (LV) global longitudinal strain (LVGLS) is versatile; however, it is difficult to obtain. We evaluated the potential of an artificial intelligence (AI)-generated electrocardiography score for LVGLS estimation (ECG-GLS score) to diagnose LV systolic dysfunction and predict prognosis of patients with heart failure (HF). A convolutional neural network-based deep-learning algorithm was trained to estimate the echocardiography-derived GLS (LVGLS). ECG-GLS score performance was evaluated using data from an acute HF registry at another tertiary hospital (n = 1186). In the validation cohort, the ECG-GLS score could identify patients with impaired LVGLS (≤ 12%) (area under the receiver-operating characteristic curve [AUROC], 0.82; sensitivity, 85%; specificity, 59%). The performance of ECG-GLS in identifying patients with an LV ejection fraction (LVEF)
- Published
- 2024
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