Predicting the severity of valvular heart diseases using ai integrated with ECG and anthropometric data

Abstract Background AI has been used in studies to predict the severity of aortic stenosis (AS) from electrocardiography (ECG), but the application of AI to predict the severity of aortic regurgitation (AR), mitral regurgitation (MR) and tricuspid regurgitation (TR) has not been widely explored. This study aimed to determine whether deep learning can predict the severity of valvular heart disease (AS, AR, MR and TR) assessed by echocardiography using ECG data. Methods and Results We analyzed data from 31,787 adult patients who underwent ECG and echocardiography within three days of each other between 2014 and 2019 at two hospitals. Valvular heart diseases, including AS, AR, MR, and TR, were classified into categories; normal, mild, moderate, and severe; according to guidelines. ECGs, along with patient age, gender, height, and weight, were inputs to a LightGBM machine learning model. The model was trained on 80.6% of the data, with the remaining 19.4% used for validation. The sensitivity for predicting moderate or severe valvular heart disease was 85% for AS, 88% for AR, 85% for MR, and 85% for TR. The negative predictive values for AS, AR, MR, and TR were all 100%. Prediction accuracies for the severity of each disease were AS at 0.857, AR at 0.871, MR at 0.855, and TR at 0.890. Predictions based on AS diagnostic parameters; mean pressure gradient, valve area, and maximum blood flow velocity also showed favorable accuracies at 0.891, 0.821, and 0.877, respectively. Conclusion Integrating AI with standard clinical parameters, including ECG data, height, and weight, showed potential in accurately predicting the severity of various valvular heart diseases. Further researches across diverse populations and settings are necessary to validate the approach's effectiveness and explore its impact on patient care.