Ischemia and outcome prediction by cardiac CT based machine learning
Verena Brandt1,2, Tilman Emrich1,3,4, U Joseph Schoepf5
1Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Heart & Vascular Center, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Insights
Machine learning (ML) enhances cardiac CT scans like coronary artery calcium scoring (CACS) and coronary CT angiography (cCTA) for better coronary artery disease (CAD) assessment. These AI algorithms improve risk stratification and predict cardiovascular outcomes more objectively.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Machine Learning Applications
Background:
- Cardiac CT, including coronary artery calcium scoring (CACS) and coronary CT angiography (cCTA), is vital for evaluating coronary artery disease (CAD).
- It offers anatomical and morphological insights for risk stratification, treatment decisions, and outcome prediction.
- Artificial intelligence (AI) and machine learning (ML) are emerging to enhance objectivity, reproducibility, and decision-making in cardiovascular CT.
Purpose of the Study:
- To provide an overview of current ML-based algorithms in cardiac CT.
- To present scientific data on the clinical validation and implementation of these ML algorithms.
- To highlight the role of ML in predicting ischemia-specific CAD and cardiovascular outcomes.
Main Methods:
- Review of contemporary ML-based algorithms applied to cardiac CT.
- Analysis of scientific data regarding clinical validation and implementation.
- Focus on applications in CACS and cCTA for CAD assessment.
Main Results:
- ML algorithms show potential for improving imaging workflow in cardiac CT.
- AI-driven approaches offer more objective and reproducible risk stratification and outcome prediction.
- Validated ML tools are becoming available for clinical use in managing CAD.
Conclusions:
- ML holds significant promise for advancing cardiac CT interpretation and patient management.
- These algorithms can lead to more accurate prediction of ischemia-specific CAD and cardiovascular events.
- Further clinical validation and implementation are key to realizing the full potential of ML in cardiovascular CT.
Abstract:
Cardiac CT using non-enhanced coronary artery calcium scoring (CACS) and coronary CT angiography (cCTA) has been proven to provide excellent evaluation of coronary artery disease (CAD) combining anatomical and morphological assessment of CAD for cardiovascular risk stratification and therapeutic decision-making, in addition to providing prognostic value for the occurrence of adverse cardiac outcome. In recent years, artificial intelligence (AI) and, in particular, the application of machine learning (ML) algorithms, have been promoted in cardiovascular CT imaging for improved decision pathways, risk stratification, and outcome prediction in a more objective, reproducible, and rational manner. AI is based on computer science and mathematics that are based on big data, high performance computational infrastructure, and applied algorithms. The application of ML in daily routine clinical practice may hold potential to improve imaging workflow and to promote better outcome prediction and more effective decision-making in patient management. Moreover, CT represents a field wherein ML may be particularly useful, such as CACS and cCTA. Thus, the purpose of this review is to give a short overview about the contemporary state of ML based algorithms in cardiac CT, as well as to provide clinicians with currently available scientific data on clinical validation and implementation of these algorithms for the prediction of ischemia-specific CAD and cardiovascular outcome.
Related Concept Videos
01:28Imaging Studies for Cardiovascular System V: CT
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT