1. Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images.
- Author
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Sangha, Veer, Nargesi, Arash, Dhingra, Lovedeep, Khunte, Akshay, Mortazavi, Bobak, Ribeiro, Antônio, Banina, Evgeniya, Adeola, Oluwaseun, Garg, Nadish, Brandt, Cynthia, Miller, Edward, Ribeiro, Antonio, Velazquez, Eric, Giatti, Luana, Barreto, Sandhi, Foppa, Murilo, Ouyang, David, Krumholz, Harlan, Khera, Rohan, and Yuan, Neal
- Subjects
artificial intelligence ,biomedical technology ,electrocardiography ,heart failure ,machine learning ,ventricular dysfunction ,left ,Adult ,Humans ,Prospective Studies ,Longitudinal Studies ,Electrocardiography ,Ventricular Dysfunction ,Left ,Ventricular Function ,Left - Abstract
BACKGROUND: Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction. METHODS: Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction 27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3-33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V2 and V3), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3-4.7]; median follow-up, 3.2 years). CONCLUSIONS: We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings.
- Published
- 2023