Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve

Coenen, Adriaan; Kim, Young-Hak Y.-H.; Kruk, Mariusz; Tesche, Christian; Geer, Jakob J. De; Kurata, Akira; Lubbers, Marisa; Daemen, Joost; Itu, Lucian; Rapaka, Saikiran; Sharma, Puneet; Schwemmer, Chris; Persson, Anders; Schoepf, Joseph; Kępka, Cezary; Yang, Dong Hyun; Nieman, Koen

doi:10.1161/circimaging.117.007217

Cited by 338 publications

(208 citation statements)

References 46 publications

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“…Further, ML has proven itself to be a vastly more powerful tool for prediction across several cardiovascular applications. [27][28][29][30][31] The addition of CACS to prediction models has been previously shown to improve performance; similarly, our findings show that the best prediction was achieved with the addition of CACS to the ML model. 11,15,32 For instance, the addition of the CACS to extended CAD consortium clinical score was found to significantly increase the C-statistic from 0.79 to 0.88 for the prediction of obstructive CAD on invasive coronary angiography.…”

Section: Discussionsupporting

confidence: 78%

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Al’Aref

Maliakal

Singh

et al. 2019

European Heart Journal

166

101

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Aims Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA). Methods and results The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features. Conclusion A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.

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Section: Discussionsupporting

confidence: 78%

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Al’Aref

Maliakal

Singh

et al. 2019

European Heart Journal

166

101

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“…The currently available work-in-progress on-site CT-FFR software works on a regular workstation using semi-automated 3D coronary artery modeling and 1 of the following algorithms: (1) the reduced-order CFD-based FFR calculations; 13,14 (2) CFD-based FFR calculations with an AI algorithm, 15 or (3) ML-based CT-FFR calculations. [17][18][19] The most recently developed on-site ML-based CT-FFR algorithm was developed using a deep learning model to integrate the complex relationship between various anatomic features and the CFD-based FFR results as the ground truth from a synthetically generated database of 12,000 coronary artery models. Abbreviations as in Table 2.…”

Section: Discussionmentioning

confidence: 99%

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

Kurata

Fukuyama

Hirai

et al. 2019

Circ J

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dial ischemia. 5,6 When lesion-specific ischemia is assessed by FFR during invasive coronary angiography (ICA), the findings have a great impact on the practical management of patients with CAD, by assisting decision-making and procedural planning for revascularization therapy. 7,8Coronary CTA-derived computational FFR (CT-FFR) determination is a new diagnostic method that uses information obtained from the resting coronary CTA dataset and computational flow dynamics (CFD). 9 Previous studies have shown that CT-FFR has good correlation with invasively measured FFR and improves the diagnostic differentiation between hemodynamically significant CAD and coronary CTA alone, using either a remote service 10-12 or on-site

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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 Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve

Abstract: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

Cited by 338 publications

References 46 publications

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

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

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

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Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve

Abstract: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

Cited by 338 publications

References 46 publications

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

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

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

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