Future Horizons: The Potential Role of Artificial Intelligence in Cardiology.
Octavian Stefan PatrascanuDana TutunaruCarmina Liana MusatOana Maria DragostinAna FulgaLuiza NechitaAlexandru Bogdan CiubarăAlin-Ionut PiraianuElena StamateDiana Gina PoalelungiIonut DragostinDoriana Cristea-Ene IancuAnamaria CiubaraIuliu FulgaPublished in: Journal of personalized medicine (2024)
Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.
Keyphrases
- artificial intelligence
- machine learning
- computed tomography
- left ventricular
- deep learning
- magnetic resonance imaging
- cardiovascular disease
- pulmonary embolism
- big data
- hypertrophic cardiomyopathy
- healthcare
- risk assessment
- contrast enhanced
- coronary artery disease
- positron emission tomography
- neural network
- cardiac surgery
- thoracic surgery
- escherichia coli
- multiple sclerosis
- newly diagnosed
- pulmonary hypertension
- ejection fraction
- human health
- heavy metals
- image quality
- heart rate variability
- congenital heart disease
- metabolic syndrome
- magnetic resonance
- high throughput
- patient reported
- percutaneous coronary intervention
- atrial fibrillation
- case control
- current status
- acute coronary syndrome
- health insurance
- transcatheter aortic valve replacement
- climate change
- acute kidney injury