Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects

Abstract

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.

Vascular persistence following precision micropuncture

“…Global microvascular analysis is fraught with difficulties such as sampling limitations, especially when only using thin‐section histology 60 . AI has recently been shown to be useful in the analysis of medical images, 61 including angiograms 62 . Likewise, it has also been applied to histopathology image analysis workflow 63 .…”

“…60 AI has recently been shown to be useful in the analysis of medical images, 61 including angiograms. 62 Likewise, it has also been applied to histopathology image analysis workflow. 63 Our whole mount "vessel painting" angiograms are a reliable and simple combination of histology and angiography to evaluate the microvasculature.…”

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

“…Coronary CT angiography (CCTA) is a useful diagnostic tool for screening and diagnosing coronary heart disease; however, it is limited by its examination quality, accuracy of reporting, and prognosis analysis. AI has demonstrated how it may optimize CCTA through radiation dose reduction without affecting image quality, reduce image noise and motion artifact, perform automatic segmentation, perform risk stratification through coronary artery calcium (CAC) assessment, and analyze coronary plaque and severity of stenosis [83], with all of these practical applications improving clinician efficiency and aiding with the diagnostic process. Wolterink et al first trained a supervised machine learning system in 2014 to distinguish between true coronary calcifications and other candidate calcifications [84].…”

Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

“…Within cardiovascular imaging, the main areas of AI application are: (1) image acquisition and reconstruction—which helps to reduce the scan time, (2) improving the imaging workflow and efficiency of time-expensive tasks such as segmentation, (3) improving the diagnosis-making process, (4) evaluation of disease progression and prognosis, (5) assessment of treatment effectiveness, and (6) generation of new knowledge. Examples illustrating key areas of AI applications from non-invasive cardiovascular imaging is summarized in Table 1 , further examples in can be found in dedicated publications ( 15 , 16 , 18 , 49 , 50 ).…”