Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: A multi-center study and meta-analysis

Tang, Chunbo; Wang, Yi Ning; Zhou, Fan; Schoepf, U. Joseph; Assen, Marly van; Stroud, Robert E.; Li, Jian Hua; Zhang, Xiao Lei; Lu, Minghui; Zhou, Chang Sheng; Zhang, Dai‐Min; Yi, Yan; Yan, Jing; Lu, Guang Ming; Xu, Lei; Zhang, Long Jiang

doi:10.1016/j.ejrad.2019.04.011

Cited by 45 publications

(29 citation statements)

References 36 publications

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“…Tesche et al (67) investigated 314 patients (482 vessels in total) who first obtained the CACS, generated a patient-specific three-dimensional grid using the ML model, and calculated FFR-CT values throughout the coronary artery tree, using invasive FFR as a reference. It was reported that, with the increase in calcification score, the diagnostic accuracy of FFR-CT also decreased, but ML still had more prominent diagnostic advantages as compared to CCTA alone (69).…”

Section: Ct-derived Fractional Flow Reservementioning

confidence: 99%

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

Liao

Huang

et al. 2022

Front. Cardiovasc. Med.

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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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Section: Ct-derived Fractional Flow Reservementioning

confidence: 99%

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

Liao

Huang

et al. 2022

Front. Cardiovasc. Med.

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“…Since then, multiple retrospective studies have been performed to evaluate the diagnostic accuracy of CT-FFR ML , validated against the gold-standard invasive FFR. They have further demonstrated superior diagnostic performance of CT-FFR ML over CTA stenosis severity and quantitative atherosclerotic plaque features derived from CCTA [ 70 , 93 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 ].…”

Section: Artificial Intelligence To Predict Functionally Obstructive ...mentioning

confidence: 99%

Section: Artificial Intelligence To Predict Functionally Obstructive ...mentioning

confidence: 99%

“…To further highlight the incremental diagnostic value of CT-FFR ML over anatomic plaque features derived from CCTA in vessels with intermediate stenosis, several other studies have been performed [ 99 , 102 , 103 , 106 ]. Tang et al evaluated the diagnostic value of CT-FFR ML in predicting lesion-specific ischemia [ 103 ]. Based on a study sample of 122 vessels in 101 patients, CT-FFR ML performed better than anatomic CCTA parameters (AUC 0.96 for CT-FFR ML vs. 0.63 for CCTA on a per-vessel basis p < 0.05).…”

Section: Artificial Intelligence To Predict Functionally Obstructive ...mentioning

confidence: 99%

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Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

et al. 2022

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Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

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“…By only performing CFD simulations once in a training phase, the time required to perform FFR CT was reduced by two orders of magnitude. The diagnostic value of this method has been demonstrated thoroughly (64)(65)(66)(67)(68)(69)(70)(71)(72)(73)(74)(75)(76). Yu…”

Section: Functional Significancementioning

confidence: 99%

Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

Hampe

Wolterink

Velzen

et al. 2019

Front. Cardiovasc. Med.

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Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.

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Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: A multi-center study and meta-analysis

Cited by 45 publications

References 36 publications

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

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

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

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