AI analysis for ejection fraction estimation from 12-lead ECG

Heart failure (HF) remains a leading global cause of cardiovascular deaths, with its prevalence expected to rise in the upcoming decade. Measuring the heart ejection fraction (EF) is crucial for diagnosing and monitoring HF. Although echocardiography is the gold standard for EF measurement, it is often inaccessible in remote areas due to its cost and complexity. In contrast, electrocardiography (ECG) is more readily available and affordable, and emerging research suggests a possible link between ECG signals and EF. In this work, we explore the potential of 12-lead ECG signals to estimate EF using various machine learning (ML) and deep learning (DL) models. While recent studies have considered the use of ML or DL for estimating EF, these algorithms are often trained and tested on urban-based populations. However, demographics like those in rural Appalachia, where disease prevalence is extremely high, have been overlooked, potentially due to the unavailability of large volumes of data. Moreover, there have been concerning reports regarding the fairness of AI predictions across different populations, making it crucial to understand the performance of AI models across diverse demographics before their widespread application. To address this, our study focuses on analyzing AI models for EF estimation in the rural Appalachian population. We utilized a 12-lead ECG dataset of 55,500 patients from WVU Medicine hospitals in West Virginia and employed a wide array of AI algorithms, ranging from Random Forest to modern deep learning-based methods like Transformers, to estimate EF. We also considered different thresholds for analyzing these AI algorithms and examined the impact of single and multi-lead ECG signals, and conducted model interpretability analysis. Overall, our comprehensive analysis demonstrated that deep learning-based algorithms achieved the highest performance, with an AUROC of around 0.86 for EF estimation from 12-lead ECG signals. Additionally, we found that while individual ECG leads were insufficient for accurate EF estimation, specific lead combinations significantly improved classification performance.