Artificial Intelligence–Enabled Model for Early Detection of Left Ventricular Hypertrophy and Mortality Prediction in Young to Middle-Aged Adults

Abstract

Background: Concealed left ventricular hypertrophy (LVH) is a prevalent condition that is correlated with a substantial risk of cardiovascular events and mortality, especially in young to middle-aged adults. Early identification of LVH is warranted. In this work, we aimed to develop an artificial intelligence (AI)–enabled model for early detection and risk stratification of LVH using 12-lead ECGs. Methods: By deep learning techniques on the ECG recordings from 28 745 patients (20–60 years old), the AI model was developed to detect verified LVH from transthoracic echocardiography and evaluated on an independent cohort. Two hundred twenty-five patients from Japan were externally validated. Cardiologists’ diagnosis of LVH was based on conventional ECG criteria. The area under the curve (AUC), sensitivity, and specificity were applied to evaluate the model performance. A Cox regression model estimated the independent effects of AI-predicted LVH on cardiovascular or all-cause death. Results: The AUC of the AI model in diagnosing LVH was 0.89 (sensitivity: 90.3%, specificity: 69.3%), which was significantly better than that of the cardiologists’ diagnosis (AUC, 0.64). In the second independent cohort, the diagnostic performance of the AI model was consistent (AUC, 0.86; sensitivity: 85.4%, specificity: 67.0%). After a follow-up of 6 years, AI-predicted LVH was independently associated with higher cardiovascular or all-cause mortality (hazard ratio, 1.91 [1.04–3.49] and 1.54 [1.20–1.97], respectively). The predictive power of the AI model for mortality was consistently valid among patients of different ages, sexes, and comorbidities, including hypertension, diabetes, stroke, heart failure, and myocardial infarction. Last, we also validated the model in the international independent cohort from Japan (AUC, 0.83). Conclusions: The AI model improved the detection of LVH and mortality prediction in the young to middle-aged population and represented an attractive tool for risk stratification. Early identification by the AI model gives every chance for timely treatment to reverse adverse outcomes.

Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis

“…The colors on the annual rings range from blue to red, with blue indicating 2013 and red indicating 2023. Cluster #0 was the largest one containing 122 keywords and the research topic is about using ML to predict risk factors for AF-related stroke [ 33 , 34 ]. Cluster # 7 was the earliest cluster with the mean year of 2014.…”

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

“…36–39 Analysis of ECG waveforms provides a rapid, easy-to-implement, and cost-effective application for artificial intelligence. Its use in adults has been wide-ranging, including prediction of ventricular dysfunction, 3–7 ventricular hypertrophy, 8–10 ventricular dilation, 9,11 atrial fibrillation and other arrhythmias, 17,26,40,41 and age, 42,43 sex, 42 and time to death. 8,43 Our findings provide proof-of-concept evidence that similar ECG applications can be explored in children and suggest that deep learning may also be applicable to other data streams (eg, wearable biosensor data) that could aid in predicting outcomes for children 44 similar to what has been performed in adults.…”

“…Its use in adults has been wide-ranging, including prediction of ventricular dysfunction, 3–7 ventricular hypertrophy, 8–10 ventricular dilation, 9,11 atrial fibrillation and other arrhythmias, 17,26,40,41 and age, 42,43 sex, 42 and time to death. 8,43 Our findings provide proof-of-concept evidence that similar ECG applications can be explored in children and suggest that deep learning may also be applicable to other data streams (eg, wearable biosensor data) that could aid in predicting outcomes for children 44 similar to what has been performed in adults. 45…”

“…This has historically motivated the development of computer-generated interpretations on the basis of predefined rules and feature recognition algorithms that may not capture subtleties of an ECG. 2 Recent work has demonstrated that deep learning-based artificial intelligence-enhanced ECG (AI-ECG) algorithms may result in greater diagnostic fidelity; studies of this approach in adult populations have reliably predicted a range of adult cardiovascular phenotypes, including ventricular dysfunction, [3][4][5][6][7] ventricular hypertrophy, [8][9][10] and ventricular dilation. 9,11 Progressive anatomic and physiological changes occurring from birth to adolescence lead to agedependent variations in pediatric ECGs.…”

Automated Estimation of Computed Tomography-Derived Left Ventricular Mass Using Sex-specific 12-Lead ECG-Based Temporal Convolutional Network

“…[14][15][16] Some studies take advantage of convolutional neural networks for heart disease (e.g., LVH) prediction. 17,18 The ecgAI model used a deep-learning model to automatically segment raw ECG signals into non-overlapping intervals and durations to generate ECG features. 14 This approach allowed for a more comprehensive analysis of the ECG signal, resulting in more accurate and reliable features.…”