Machine Learning and Coronary Artery Calcium Scoring.
Heon LeeSimon MartinJeremy R BurtPooyan Sahbaee BagherzadehSaikiran RapakaHunter N GrayTyler J LeonardChris SchwemmerJoseph Uwe SchoepfPublished in: Current cardiology reports (2020)
Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.
Keyphrases
- coronary artery disease
- machine learning
- coronary artery
- artificial intelligence
- deep learning
- image quality
- dual energy
- computed tomography
- contrast enhanced
- low dose
- pulmonary artery
- positron emission tomography
- big data
- healthcare
- percutaneous coronary intervention
- magnetic resonance imaging
- cardiovascular events
- high dose
- magnetic resonance
- risk assessment
- mass spectrometry
- cardiovascular disease
- convolutional neural network
- type diabetes
- aortic stenosis
- climate change
- human health
- fluorescence imaging
- pulmonary arterial hypertension