Journal of the American Heart Association · 2026
ECG-Based Artificial Intelligence for Classifying Left Ventricular Dysfunction and Heart Failure With Preserved Ejection Fraction.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Karabayir Ibrahim, Gilbert Olivia, Valika Ali, Celik Turgay, Krishnan Arunkumar, Chinthala Lokesh, Pandey Ambarish, Soliman Elsayed Z, Jefferies John L, Kitzman Dalane, Herrington David, Davis Robert L, Akbilgic Oguz
Аннотация
BACKGROUND: Left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF) often present with early signs that are frequently overlooked or attributed to other conditions. This study proposes a novel, externally validated artificial intelligence (AI) tool using ECG data (ECG-AI) for the simultaneous detection of subtypes of LV dysfunction and HFpEF.
METHODS: Two ECG-AI models, using 12-lead or single-lead ECG, were developed using data from Atrium Health Wake Forest Baptist and University of Tennessee Health Science Center (UTHSC) to classify ECGs into 4 categories: reduced LV ejection fraction (rEF; EF<40), midrange EF (mEF; 40≤mEF<50), HFpEF, and controls (no LV dysfunction or HFpEF). Next, a boosting algorithm was used to incorporate clinical risk factors. Finally, the models were independently validated on pediatric data from UTHSC.
RESULTS: The Atrium Health Wake Forest Baptist cohort included 1 078 198 digital ECGs from 165 243 patients (5% rEF, 8% mEF, 2% HFpEF), and the UTHSC external validation cohort comprised 72 832 ECGs from 42 880 patients (1% rEF, 1% mEF, 1% HFpEF). For the 12-lead (Lead-I) ECG-AI model, areas under the curve for rEF/mEF/HFpEF were 0.90/0.81/0.80 (0.89/0.78/0.75) in the Atrium Health Wake Forest Baptist holdout data and 0.92/0.76/0.73 (0.90/0.75/0.74) in the UTHSC data. Clinical data-only machine-learning models lacked generalizability; clinical + ECG-AI models did not show any significant improvement compared with ECG-alone models. With 8418 ECGs (142 cases, 8276 controls) from UTHSC pediatric data, the 12-lead model achieved areas under the curve of 0.97/0.71/0.64 for rEF/mEF/HFpEF, and the Lead I model had areas under the curve of 0.94/0.77/0.67.
CONCLUSIONS: ECG-AI can accurately detect LV dysfunction and HFpEF even from single-lead ECG, enabling potential low-cost screening.
METHODS: Two ECG-AI models, using 12-lead or single-lead ECG, were developed using data from Atrium Health Wake Forest Baptist and University of Tennessee Health Science Center (UTHSC) to classify ECGs into 4 categories: reduced LV ejection fraction (rEF; EF<40), midrange EF (mEF; 40≤mEF<50), HFpEF, and controls (no LV dysfunction or HFpEF). Next, a boosting algorithm was used to incorporate clinical risk factors. Finally, the models were independently validated on pediatric data from UTHSC.
RESULTS: The Atrium Health Wake Forest Baptist cohort included 1 078 198 digital ECGs from 165 243 patients (5% rEF, 8% mEF, 2% HFpEF), and the UTHSC external validation cohort comprised 72 832 ECGs from 42 880 patients (1% rEF, 1% mEF, 1% HFpEF). For the 12-lead (Lead-I) ECG-AI model, areas under the curve for rEF/mEF/HFpEF were 0.90/0.81/0.80 (0.89/0.78/0.75) in the Atrium Health Wake Forest Baptist holdout data and 0.92/0.76/0.73 (0.90/0.75/0.74) in the UTHSC data. Clinical data-only machine-learning models lacked generalizability; clinical + ECG-AI models did not show any significant improvement compared with ECG-alone models. With 8418 ECGs (142 cases, 8276 controls) from UTHSC pediatric data, the 12-lead model achieved areas under the curve of 0.97/0.71/0.64 for rEF/mEF/HFpEF, and the Lead I model had areas under the curve of 0.94/0.77/0.67.
CONCLUSIONS: ECG-AI can accurately detect LV dysfunction and HFpEF even from single-lead ECG, enabling potential low-cost screening.