Key result
Artificial intelligence applied to coronary CT angiography automates workflows, reduces radiation dose and image noise, and improves the accuracy of plaque identification and stenosis assessment.
Why the study?
Coronary CT angiography is the preferred choice for CHD screening and diagnosis but cannot meet clinical needs regarding examination quality, reporting accuracy, and prognosis analysis.
Artificial intelligence shows significant promise in enhancing coronary CT angiography by improving image quality, reducing radiation dose, and automating the quantification of calcium scores, plaque characteristics, and fractional flow reserve.
AI-assisted CCTA analysis merits exploration in CHD; leaves open need for prospective validation before practice change.
Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.
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Liao et al. (2022) conducted a review in Coronary heart disease. Artificial intelligence vs. Conventional methods was evaluated. Artificial intelligence applied to coronary CT angiography automates workflows, reduces radiation dose and image noise, and improves the accuracy of plaque identification and stenosis assessment.
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