A 2-year investigation of the impact of the computed tomography–derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management

Liu, Xin; Mo, Xukai; Zhang, Heye; Yang, Guang; Shi, Changzheng; Hau, William Kongto

doi:10.1007/s00330-021-07771-7

Cited by 27 publications

(38 citation statements)

References 29 publications

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“…CT-FFR ML has been shown to be a better predictor than plaque features derived from CCTA for the determination of the presence of lesion-specific ischemia, but whether CT-FFR ML influences the eventual treatment plan and outcomes (as guided by ICA-FFR) remains an active area of investigation [ 115 , 116 , 117 , 118 ]. Qiao et al demonstrated the added benefit of CT-FFR ML compared to relying on an anatomy-based strategy in patients with stable chest pain (reduction rate of ICA by 54.5% and 4.4% fewer revascularizations) [ 115 ].…”

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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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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“…Furthermore, ML may accurately identify patients who require coronary intervention. Liu et al enrolled 296 patients with symptomatic CAD and stenosis (> 50%) to create a training dataset in order to develop a DL tool which could automatically calculate fractional flow reserve [43]. It proved to be accurate, thus possibly reducing the need of invasive coronary intervention.…”

Section: Coronary Computed Tomography Angiographymentioning

confidence: 99%

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Spadarella

Perillo

Ugga

et al. 2022

Curr Cardiovasc Imaging Rep

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Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.

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“…with ⃗ u and p representing the velocity and pressure, and t the time. The blood is assumed to be an incompressible Newtonian fluid, with dynamic viscosity 𝜈 of 0.0035 Pa⋅s and density 𝜌 of 1050 kg/m 3 . In order to solve the above system of equations, the conditions of domain boundaries should be specified, which is the focus of this study.…”

Section: Cfd Simulation Of Navier-stokes Equations and Ffrct Computationmentioning

confidence: 99%

Physiologically personalized coronary blood flow model to improve the estimation of noninvasive fractional flow reserve

Liu

Rao

et al. 2021

Medical Physics

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Purpose Coronary outlet resistance is influenced by the quantification and distribution of resting coronary blood flow. It is crucial for a more physiologically accurate estimation of fractional flow reserve (FFR) derived from computed tomography angiography (CTA), referred to as FFRCT. This study presents a physiologically personalized (PP)‐based coronary blood flow model involving the outlet boundary condition (BC) and a standardized outlet truncation strategy to estimate the outlet resistance and FFRCT. Methods In this study, a total of 274 vessels were retrospectively collected from 221 patients who underwent coronary CTA and invasive FFR within 14 days. For FFRCT determination, we have employed a PP‐based outlet BC model involving personalized physiological parameters and left ventricular mass (LVM) to quantify resting coronary blood flow. We evaluated the improvement achieved in the diagnostic performance of FFRCT by using the PP‐based outlet BC model relative to the LVM‐based model, with respect to the invasive FFR. Additionally, in order to evaluate the impact of the outlet truncation strategy on FFRCT, 68 vessels were randomly selected and analyzed independently by two operators, by using two different outlet truncation strategies at 1‐month intervals. Results The per‐vessel diagnostic performance of the PP‐based outlet BC model was improved, based on invasive FFR as reference, compared to the LVM‐based model: (i) accuracy/sensitivity/specificity: 91.2%/90.4%/91.8% versus 86.5%/84.6%/87.6%, for the entire dataset of 274 vessels, (ii) accuracy/sensitivity/specificity: 88.7%/82.4%/90.4% versus 82.4%/ 76.5%/84.0%, for moderately stenosis lesions. The standardized outlet truncation strategy showed good repeatability with the Kappa coefficient of 0.908. Conclusions It has been shown that our PP‐based outlet BC model and standardized outlet truncation strategy can improve the diagnostic performance and repeatability of FFRCT.

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A 2-year investigation of the impact of the computed tomography–derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management

Cited by 27 publications

References 29 publications

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Physiologically personalized coronary blood flow model to improve the estimation of noninvasive fractional flow reserve

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