A prospective multicenter validation study for a novel angiography-derived physiological assessment software: Rationale and design of the radiographic imaging validation and evaluation for Angio-iFR (ReVEAL iFR) study.
TL;DR: The ReVEAL iFR (Radiographic imaging Validation and EvALuation for Angio-iFR) trial is a multicenter, multicontinental, validation study which aims to validate the diagnostic accuracy of the AngioiFR medical software device (Philips, San Diego, US) in patients undergoing angiography for chronic Coronary Syndrome (CCS) as mentioned in this paper.
About: This article is published in American Heart Journal.The article was published on 2021-05-13 and is currently open access. It has received 4 citations till now. The article focuses on the topics: Cost effectiveness & Fractional flow reserve.
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TL;DR: In this paper, a review of angiography-derived fractional flow reserve (FFR) indices is presented, highlighting their differences, advantages, disadvantages and potential clinical implications.
2 citations
TL;DR: In this paper , a review of angiography-derived fractional flow reserve (FFR) indices is presented, highlighting their differences, advantages, disadvantages and potential clinical implications.
1 citations
TL;DR: Choi et al. as mentioned in this paper introduced two selective ensemble methods that integrated the weighted ensemble approach with per-image quality estimation, and the final output was determined by imposing different weights according to the ranking.
Abstract: AO_SCPLOWBSTRACTC_SCPLOWInvasive coronary angiography is a primary imaging modality that visualizes the lumen area of coronary arteries for the diagnosis of coronary artery diseases and guidance for interventional devices. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction; this limits their application in the catheterization room. For a more automated QCA, it is necessary to minimize operator intervention through robust segmentation methods with improved predictability. In this study, we introduced two selective ensemble methods that integrated the weighted ensemble approach with per-image quality estimation. In our selective ensemble methods, the segmentation outcomes from five base models with different loss functions were ranked by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranking. The ranking criteria based on mask morphology were determined empirically to avoid frequent types of segmentation errors, whereas the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner. In the assessment with 7,426 frames from 2,924 patients, the selective ensemble methods improved segmentation performance with DSCs of up to 93.11% and provided a better delineation of lumen boundaries near the coronary lesion with local DSCs of up to 94.04%, outperforming all individual models and hard voting ensembles. The probability of mask disconnection at the most narrowed region could be minimized to <1%. The robustness of the proposed methods was evident in the external validation. Inference time for major vessel segmentation was approximately one-third, indicating that our selective ensemble methods may allow the real-time application of QCA-based diagnostic methods in routine clinical settings.
1 citations
TL;DR: In this paper , a rank-based selective ensemble method was proposed to improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA.
Abstract: Background Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction, limiting their application in the catheterization room. Purpose This study aims to propose rank-based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA. Methods Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per-image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner (ESEN). Five-fold cross-validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients. Results The selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one-sixth of a second. Conclusion Proposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real-time QCA-based diagnostic methods in routine clinical settings.
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TL;DR: Authors/Task Force Members: Franz-Josef Neumann* (ESC Chairperson) (Germany), Miguel Sousa-Uva* (EACTS Chair person) (Portugal), Anders Ahlsson (Sweden), Fernando Alfonso (Spain), Adrian P. Banning (UK), Umberto Benedetto (UK).
4,342 citations
Turku University Hospital1, National University of Ireland, Galway2, University of Catania3, University of Naples Federico II4, University of Paris5, Bispebjerg Hospital6, University of Sheffield7, University of Cambridge8, Stavanger University Hospital9, Oslo University Hospital10, Hospital Clínico San Carlos11, Mayo Clinic12, University of Western Brittany13, Rabin Medical Center14, Slovak Medical University15, Saarland University16, University of Barcelona17, University of Brescia18, University of Bern19, University of Erlangen-Nuremberg20, Leiden University Medical Center21
TL;DR: In this article, the authors present guidelines for the management of patients with coronary artery disease (CAD), which is a pathological process characterized by atherosclerotic plaque accumulation in the epicardial arteries.
Abstract: Coronary artery disease (CAD) is a pathological process characterized by atherosclerotic plaque accumulation in the epicardial arteries, whether obstructive or non-obstructive. This process can be modified by lifestyle adjustments, pharmacological therapies, and invasive interventions designed to achieve disease stabilization or regression. The disease can have long, stable periods but can also become unstable at any time, typically due to an acute atherothrombotic event caused by plaque rupture or erosion. However, the disease is chronic, most often progressive, and hence serious, even in clinically apparently silent periods. The dynamic nature of the CAD process results in various clinical presentations, which can be conveniently categorized as either acute coronary syndromes (ACS) or chronic coronary syndromes (CCS). The Guidelines presented here refer to the management of patients with CCS. The natural history of CCS is illustrated in Figure 1.
3,448 citations
TL;DR: A Report of the American College of Cardiology Foundation/AmericanHeart Association Task Force on Practice Guidelines, and the AmericanCollege of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for CardiovascularAngiography and Interventions, and Society of ThorACic Surgeons
Abstract: Jeffrey L. Anderson, MD, FACC, FAHA, Chair
Jonathan L. Halperin, MD, FACC, FAHA, Chair-Elect
Alice K. Jacobs, MD, FACC, FAHA, Immediate Past Chair 2009–2011 [§§][1]
Sidney C. Smith, Jr, MD, FACC, FAHA, Past Chair 2006–2008 [§§][1]
Cynthia D. Adams, MSN, APRN-BC, FAHA[§§][1]
Nancy M
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TL;DR: In this paper, the diagnostic performance of noninvasive fractional flow reserve (FFR) derived from standard acquired coronary computed tomography angiography (CTA) datasets for the diagnosis of myocardial ischemia in patients with suspected stable coronary artery disease (CAD) was evaluated.
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