Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR

Tesche, Christian; Otani, Katharina; Cecco, Carlo N. De; Coenen, Adriaan; Geer, Jakob De; Kruk, Mariusz; Kim, Young‐Hak; Albrecht, Moritz H.; Baumann, Stefan; Renker, Matthias; Bayer, Richard R.; Duguay, Taylor M.; Litwin, Sheldon E.; Varga-Szemes, Ákos; Steinberg, Daniel; Yang, Dong Hyun; Kępka, Cezary; Persson, Anders; Nieman, Koen; Schoepf, U. Joseph

doi:10.1016/j.jcmg.2019.06.027

Cited by 73 publications

(52 citation statements)

References 30 publications

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“…A higher AUC of CT-FFR than CTA (per-patient and per-vessel) PLATFORM trial [123,124] The clinical, economic, and quality-of-life outcomes of using CT-FFR instead of usual care No differences between CTA + CT-FFR vs CTA + static CTP also negatively affect CT-FFR evaluation [135]. However, recent studies have shown that the performance of CT-FFR in patients with high coronary calcium score is superior to that of CTA alone [130,136]. The rates of rejection on CT-FFR analysis in the ADVANCE registry and in a large clinical cohort were 2.9% and 8.4%, respectively [137].…”

Section: Prospectivementioning

confidence: 99%

Computed tomographic evaluation of myocardial ischemia

et al. 2020

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Myocardial ischemia is caused by a mismatch between myocardial oxygen consumption and oxygen delivery in coronary artery disease (CAD). Stratification and decision-making based on ischemia improves the prognosis in patients with CAD. Non-invasive tests used to evaluate myocardial ischemia include stress electrocardiography, echocardiography, single-photon emission computed tomography, and magnetic resonance imaging. Invasive fractional flow reserve is considered the reference standard for assessment of the hemodynamic significance of CAD. Computed tomography (CT) angiography has emerged as a first-line imaging modality for evaluation of CAD, particularly in the population at low to intermediate risk, because of its high negative predictive value; however, CT angiography does not provide information on the hemodynamic significance of stenosis, which lowers its specificity. Emerging techniques, e.g., CT perfusion and CT-fractional flow reserve, help to address this limitation of CT, by determining the hemodynamic significance of coronary artery stenosis. CT perfusion involves acquisition during the first pass of contrast medium through the myocardium following pharmacological stress. CTfractional flow reserve uses computational fluid dynamics to model coronary flow, pressure, and resistance. In this article, we review these two functional CT techniques in the evaluation of myocardial ischemia, including their principles, technology, advantages, limitations, pitfalls, and the current evidence.

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Section: Prospectivementioning

confidence: 99%

Computed tomographic evaluation of myocardial ischemia

et al. 2020

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“…The ML application to CT-FFR in the MACHINE registry were later confirmed in multiple sub-studies that investigated their reproducibility in different clinical scenarios: in particular, ML CT-FFR proved its feasibility in the presence of heavy coronary calcifications ( 78 ); in patients with diabetes mellitus ( 76 ) and between genders ( 77 ).…”

Section: Ai For Functional Ischemia Assessment By Cardiac Ctmentioning

confidence: 87%

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Maragna

Giacari

Guglielmo

et al. 2021

Front. Cardiovasc. Med.

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Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.

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“…Nieman et al conducted a study to investigate the diagnostic performance of ML-based, coronary computed, tomography–angiography-derived fractional flow reserve (NCT02805621; [ 63 , 64 , 65 ]);…”

Section: Discussionmentioning

confidence: 99%

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov

Zippel

Bohnet-Joschko

2021

IJERPH

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Although advances in machine-learning healthcare applications promise great potential for innovative medical care, few data are available on the translational status of these new technologies. We aimed to provide a comprehensive characterization of the development and status quo of clinical studies in the field of machine learning. For this purpose, we performed a registry-based analysis of machine-learning-related studies that were published and first available in the ClinicalTrials.gov database until 2020, using the database’s study classification. In total, n = 358 eligible studies could be included in the analysis. Of these, 82% were initiated by academic institutions/university (hospitals) and 18% by industry sponsors. A total of 96% were national and 4% international. About half of the studies (47%) had at least one recruiting location in a country in North America, followed by Europe (37%) and Asia (15%). Most of the studies reported were initiated in the medical field of imaging (12%), followed by cardiology, psychiatry, anesthesia/intensive care medicine (all 11%) and neurology (10%). Although the majority of the clinical studies were still initiated in an academic research context, the first industry-financed projects on machine-learning-based algorithms are becoming visible. The number of clinical studies with machine-learning-related applications and the variety of medical challenges addressed serve to indicate their increasing importance in future clinical care. Finally, they also set a time frame for the adjustment of medical device-related regulation and governance.

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Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR

Cited by 73 publications

References 30 publications

Computed tomographic evaluation of myocardial ischemia

Computed tomographic evaluation of myocardial ischemia

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov

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