On-Site Computed Tomography-Derived Fractional Flow Reserve Using a Machine-Learning Algorithm　― Clinical Effectiveness in a Retrospective Multicenter Cohort ―

Kurata, Akira; Fukuyama, Naoto; Hirai, Keita; Kawaguchi, Naoto; Tanabe, Yuji; Okayama, Hideki; Shigemi, Susumu; Watanabe, Kentaro; Uetani, Teruyoshi; Ikeda, Shuntaro; Inaba, Shinji; Kido, Teruhiko; Itoh, Toshihide; Mochizuki, Takashi

doi:10.1253/circj.cj-19-0163

Cited by 26 publications

(17 citation statements)

References 33 publications

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“…Obtained results show that FFR < 0.8 CT-FFR had AUC of 0.907 where CTA stenosis > 50% had 0.595 and stenosis > 70% had 0.603. CT-FFR had an analysis time of 16.4 ± 7.5 min [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

Diagnostic AI and Cardiac Diseases

Ozsahin

Ozgocmen²,

Balcioglu³

et al. 2022

Diagnostics

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(1) Background: The purpose of this study is to review and highlight recent advances in diagnostic uses of artificial intelligence (AI) for cardiac diseases, in order to emphasize expected benefits to both patients and healthcare specialists; (2) Methods: We focused on four key search terms (Cardiac Disease, diagnosis, artificial intelligence, machine learning) across three different databases (Pubmed, European Heart Journal, Science Direct) between 2017–2022 in order to reach relatively more recent developments in the field. Our review was structured in order to clearly differentiate publications according to the disease they aim to diagnose (coronary artery disease, electrophysiological and structural heart diseases); (3) Results: Each study had different levels of success, where declared sensitivity, specificity, precision, accuracy, area under curve and F1 scores were reported for every article reviewed; (4) Conclusions: the number and quality of AI-assisted cardiac disease diagnosis publications will continue to increase through each year. We believe AI-based diagnosis should only be viewed as an additional tool assisting doctors’ own judgement, where the end goal is to provide better quality of healthcare and to make getting medical help more affordable and more accessible, for everyone, everywhere.

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“…Obtained results show that FFR < 0.8 CT-FFR had AUC of 0.907 where CTA stenosis > 50% had 0.595 and stenosis > 70% had 0.603. CT-FFR had an analysis time of 16.4 ± 7.5 min [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

Diagnostic AI and Cardiac Diseases

Ozsahin

Ozgocmen²,

Balcioglu³

et al. 2022

Diagnostics

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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%

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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“…Overall, the sensitivity and specificity of CT-FFR at the per-vessel level were 76% to 98% and 61% to 94%, respectively. [18][19][20]23,24,26,28,29,[53][54][55][56][57][58][68][69][70]90,91 The MLbased CT-FFR and CFD-based CT-FFR showed similar performance in detecting lesion-specific ischemia. 28,70 CT-FFR performed better than conventional CCTA alone (Fig.…”

Section: The Clinical Evidence Of Ct-ffr Diagnostic Performance Of Ct...mentioning

confidence: 99%

Coronary Computed Tomography Angiography–derived Fractional Flow Reserve

Zhang

Tang

et al. 2022

Journal of Thoracic Imaging

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Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the stateof-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

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On-Site Computed Tomography-Derived Fractional Flow Reserve Using a Machine-Learning Algorithm　― Clinical Effectiveness in a Retrospective Multicenter Cohort ―

Cited by 26 publications

References 33 publications

Diagnostic AI and Cardiac Diseases

Diagnostic AI and Cardiac Diseases

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Coronary Computed Tomography Angiography–derived Fractional Flow Reserve

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On-Site Computed Tomography-Derived Fractional Flow Reserve Using a Machine-Learning Algorithm ― Clinical Effectiveness in a Retrospective Multicenter Cohort ―

Cited by 26 publications

References 33 publications

Diagnostic AI and Cardiac Diseases

Diagnostic AI and Cardiac Diseases

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Coronary Computed Tomography Angiography–derived Fractional Flow Reserve

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Product

Resources

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On-Site Computed Tomography-Derived Fractional Flow Reserve Using a Machine-Learning Algorithm　― Clinical Effectiveness in a Retrospective Multicenter Cohort ―