The effect of coronary calcification on diagnostic performance of machine learning–based CT-FFR: a Chinese multicenter study

Jiang, Meng; Zhang, Xiao Lei; Liu, Hui; Tang, Chunbo; Li, Jian Hua; Wang, Yi Ning; Xu, Peng; Zhou, Chang Sheng; Zhou, Fan; Lu, Minghui; Zhang, Jia Yin; Yu, Mengchao; Hou, Yang; Zheng, Minjuan; Zhang, Bo; Zhang, Dai‐Min; Yi, Yan; Xu, Lei; Hu, Xinghao; Yang, Jian; Lu, Guang Ming; Niu, Qian; Zhang, Long Jiang

doi:10.1007/s00330-020-07261-2

Cited by 34 publications

(29 citation statements)

References 35 publications

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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%

“…The segmental arc calcification method estimates lesion-specific calcium burden by measuring the greatest circumferential extent of coronary calcium, grading as nil (noncalcified), mild (0–90°), moderate (90–180°), and severe (>180°) calcification [ 110 , 114 ]. Recent studies have evaluated the performance of CT-FFR ML with varying calcification burden as assessed by the parameters mentioned above [ 97 , 98 , 99 , 104 ]. Tesche et al did a retrospective analysis using 482 vessels in 314 patients to evaluate the impact of calcifications on the performance of CT-FFR ML [ 104 ].…”

Section: Artificial Intelligence To Predict Functionally Obstructive ...mentioning

confidence: 99%

“…Di Jiang et al [ 98 ] evaluated the impact of calcification arc and CRI on the performance of CT-FFR ML . No statistically significant difference was found in the discriminatory power of CT-FFR ML with increasing calcification burden.…”

Section: Artificial Intelligence To Predict Functionally Obstructive ...mentioning

confidence: 99%

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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%

Section: Artificial Intelligence To Predict Functionally Obstructive ...mentioning

confidence: 99%

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Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

et al. 2022

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“…It was found that ML-based FFR-CT had an accuracy of 0.90 in determining calcification lesions as compared to invasive FFR. Additionally, CT-FFR generally had higher accuracy in diagnosis and differentiating ischemia in blood vessels as compared to CCTA by itself [ 45 ].…”

Section: Ai: Cardiology Imaging Applicationsmentioning

confidence: 99%

Artificial Intelligence Advances in the World of Cardiovascular Imaging

Patel

Makaryus

2022

Healthcare

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The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.

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“…To this end, some prior studies explored the correlation of coronary calcification and discriminatory performance of CT-FFR (8)(9)(10)(11)(12)(13)(14)(15). Based on multicenter clinical trial data, CT-FFR showed remarkable improvement in diagnosing ischemia over CCTA and no statistically significant difference in CT-FFR performance was found across calcification severity categories (8,14,16). However, the previous studies focused more on the efficacy comparison between CT-FFR and CCTA with increased calcification.…”

Section: Introductionmentioning

confidence: 99%

Effect of Coronary Calcification Severity on Measurements and Diagnostic Performance of CT-FFR With Computational Fluid Dynamics: Results From CT-FFR CHINA Trial

Zhao

Gao

et al. 2022

Front. Cardiovasc. Med.

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Aims: To explore the effect of coronary calcification severity on the measurements and diagnostic performance of computed tomography-derived fractional flow reserve (FFR; CT-FFR).Methods: This study included 305 patients (348 target vessels) with evaluable coronary calcification (CAC) scores from CT-FFR CHINA clinical trial. The enrolled patients all received coronary CT angiography (CCTA), CT-FFR, and invasive FFR examinations within 7 days. On both per-patient and per-vessel levels, the measured values, accuracy, and diagnostic performance of CT-FFR in identifying hemodynamically significant lesions were analyzed in all CAC score groups (CAC = 0, > 0 to <100, ≥ 100 to <400, and ≥ 400), with FFR as reference standard.Results: In total, the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under receiver operating characteristics curve (AUC) of CT-FFR were 85.8, 88.7, 86.9, 87.8, 87.1%, 0.90 on a per-patient level and 88.3, 89.3, 89.5, 88.2, 88.9%, 0.88 on a per-vessel level, respectively. Absolute difference of CT-FFR and FFR values tended to elevate with increased CAC scores (CAC = 0: 0.09 ± 0.10; CAC > 0 to <100: 0.06 ± 0.06; CAC ≥ 100 to <400: 0.09 ± 0.10; CAC ≥ 400: 0.11 ± 0.13; p = 0.246). However, no statistically significant difference was found in patient-based and vessel-based diagnostic performance of CT-FFR among all CAC score groups.Conclusion: This prospective multicenter trial supported CT-FFR as a viable tool in assessing coronary calcified lesions. Although large deviation of CT-FFR has a tendency to correlate with severe calcification, coronary calcification has no significant influence on CT-FFR diagnostic performance using the widely-recognized cut-off value of 0.8.

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The effect of coronary calcification on diagnostic performance of machine learning–based CT-FFR: a Chinese multicenter study

Cited by 34 publications

References 35 publications

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Artificial Intelligence Advances in the World of Cardiovascular Imaging

Effect of Coronary Calcification Severity on Measurements and Diagnostic Performance of CT-FFR With Computational Fluid Dynamics: Results From CT-FFR CHINA Trial

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