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Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

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Artificial Intelligence in Coronary Artery Calcium Scoring.

Afolasayo A Aromiwura¹, Dinesh K Kalra²

¹Department of Medicine, University of Louisville, Louisville, KY 40202, USA.

Journal of Clinical Medicine

|June 27, 2024

Summary

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.

Keywords:

artificial intelligence cardiac computed tomography coronary artery calcium scoring

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Published on: August 9, 2024

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.