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Published on: September 22, 2023
Artificial Intelligence in Coronary Artery Calcium Scoring
Afolasayo A Aromiwura1, Dinesh K Kalra2
1Department of Medicine, University of Louisville, Louisville, KY 40202, USA.
Insights
Artificial intelligence can improve coronary artery calcium scoring (CACS) using non-dedicated CT scans, reducing costs and radiation exposure. This review explores automated CACS methods and implementation barriers for better cardiovascular risk assessment.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Healthcare
- Cardiovascular Disease Research
Background:
- Coronary heart disease (CHD) is a leading cause of death in the US, necessitating effective risk assessment tools.
- Coronary artery calcium scoring (CACS) using computed tomography (CT) is crucial for estimating atherosclerotic cardiovascular disease (ASCVD) risk and guiding statin therapy.
- Current CACS protocols often require dedicated CT scans, leading to increased time, cost, and radiation exposure.
Purpose of the Study:
- To review current studies on automated CACS using various CT protocols, including non-dedicated ones.
- To explore the potential of artificial intelligence (AI) in enhancing CACS efficiency and utilizing non-dedicated CT scans.
- To discuss clinical application considerations and implementation barriers for AI-driven CACS.
Main Methods:
- Review of existing literature on automated CACS.
- Analysis of AI applications for improving CACS from both dedicated and non-dedicated CT protocols.
- Discussion of challenges and considerations for clinical integration.
Main Results:
- Non-dedicated CT protocols offer potential for reduced cost and radiation exposure in CACS.
- AI shows promise in overcoming limitations of non-dedicated CT, such as motion artifacts, and improving CACS accuracy.
- Automated CACS across diverse CT protocols is feasible and being actively researched.
Conclusions:
- AI-powered automated CACS holds significant potential to improve efficiency and accessibility of cardiovascular risk assessment.
- Repurposing non-dedicated CT scans with AI can lower healthcare costs and patient radiation exposure.
- Addressing implementation barriers is crucial for the widespread clinical adoption of AI-enhanced CACS.
Abstract:
Cardiovascular disease (CVD), particularly coronary heart disease (CHD), is the leading cause of death in the US, with a high economic impact. Coronary artery calcium (CAC) is a known marker for CHD and a useful tool for estimating the risk of atherosclerotic cardiovascular disease (ASCVD). Although CACS is recommended for informing the decision to initiate statin therapy, the current standard requires a dedicated CT protocol, which is time-intensive and contributes to radiation exposure. Non-dedicated CT protocols can be taken advantage of to visualize calcium and reduce overall cost and radiation exposure; however, they mainly provide visual estimates of coronary calcium and have disadvantages such as motion artifacts. Artificial intelligence is a growing field involving software that independently performs human-level tasks, and is well suited for improving CACS efficiency and repurposing non-dedicated CT for calcium scoring. We present a review of the current studies on automated CACS across various CT protocols and discuss consideration points in clinical application and some barriers to implementation.
