Advancements and Applications of Convolutional Neural Network Models in Cardiovascular Disease: A Comprehensive Review

Abstract

In recent years, artificial intelligence (AI) has demonstrated remarkable advancements in the field of cardiovascular disease (CVD), particularly in the analysis of electrocardiograms (ECGs). Due to its widespread use, low cost, and high efficiency, the ECG has long been regarded as a cornerstone of cardiological examinations and remains the most widely utilized diagnostic tool in cardiology. The integration of AI, especially deep learning (DL) technologies based on convolutional neural networks (CNNs), into ECG analysis, has shown immense potential across several cardiological subfields. Deep learning methods have provided robust support for the rapid interpretation of ECGs, enabling the fine-grained analysis of ECG waveform changes with diagnostic accuracy comparable to that of expert cardiologists. Additionally, CNN-based models have proven capable of capturing subtle ECG changes that are often undetectable by traditional methods, accurately predicting complex conditions such as atrial fibrillation, left and right ventricular dysfunction, hypertrophic cardiomyopathy, acute coronary syndrome, and aortic stenosis. This highlights the broad application potential of AI in the diagnosis of cardiovascular diseases. However, despite their extensive applications, CNN models also face significant limitations, primarily related to the reliability of the acquired data, the opacity of the “black box” processes, and the associated medical, legal, and ethical challenges. Addressing these limitations and seeking viable solutions remain critical challenges in modern medicine.