The influence of image quality on diagnostic performance of a machine learning–based fractional flow reserve derived from coronary CT angiography

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

doi:10.1007/s00330-019-06571-4

Cited by 32 publications

(22 citation statements)

References 36 publications

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“…The lack of patient preparation with nitroglycerine and beta blockers may degrade image quality and diagnostic accuracy of cCTA and CT-FFR [ 35 , 36 ]. However, we found no association between recategorization to false positive ratings and quantitatively or qualitatively assessed image quality in our study ( Table 3 and Figure 2 ).…”

Section: Discussionmentioning

confidence: 99%

“…It is well known that the lack of the administration of nitrates or beta blockers may degrade image quality and consequently diagnostic accuracy of both cCTA and CT-FFR [ 35 , 36 ]. However, as only exams without morphological signs of obstructive CAD were included, no exams with insufficient image quality for the delineation of the coronary lumina were included.…”

Section: Discussionmentioning

confidence: 99%

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Combined Coronary CT-Angiography and TAVI Planning: Utility of CT-FFR in Patients with Morphologically Ruled-Out Obstructive Coronary Artery Disease

Gohmann

Seitz

Pawelka

et al. 2022

JCM

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Background: Coronary artery disease (CAD) is a frequent comorbidity in patients undergoing transcatheter aortic valve implantation (TAVI). If significant CAD can be excluded on coronary CT-angiography (cCTA), invasive coronary angiography (ICA) may be avoided. However, a high plaque burden may make the exclusion of CAD challenging, particularly for less experienced readers. The objective was to analyze the ability of machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) to correctly categorize cCTA studies without obstructive CAD acquired during pre-TAVI evaluation and to correlate recategorization to image quality and coronary artery calcium score (CAC). Methods: In total, 116 patients without significant stenosis (≥50% diameter) on cCTA as part of pre-TAVI CT were included. Patients were examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the torso. Patients were re-evaluated with ML-based CT-FFR (threshold = 0.80). The standard of reference was ICA. Image quality was assessed quantitatively and qualitatively. Results: ML-based CT-FFR was successfully performed in 94.0% (109/116) of patients, including 436 vessels. With CT-FFR, 76/109 patients and 126/436 vessels were falsely categorized as having significant CAD. With CT-FFR 2/2 patients but no vessels initially falsely classified by cCTA were correctly recategorized as having significant CAD. Reclassification occurred predominantly in distal segments. Virtually no correlation was found between image quality or CAC. Conclusions: Unselectively applied, CT-FFR may vastly increase the number of false positive ratings of CAD compared to morphological scoring. Recategorization was virtually independently from image quality or CAC and occurred predominantly in distal segments. It is unclear whether or not the reduced CT-FFR represent true pressure ratios and potentially signifies pathophysiology in patients with severe aortic stenosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Combined Coronary CT-Angiography and TAVI Planning: Utility of CT-FFR in Patients with Morphologically Ruled-Out Obstructive Coronary Artery Disease

Gohmann

Seitz

Pawelka

et al. 2022

JCM

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“…Indeed, performance was substantially decreased in low-quality images vs. high-quality images, subjectively determined by expert readers (AUC: 0.80 vs. 0.93, respectively). 8 Moreover, in a multicentre study by Tesche et al . 14 , performance was also impacted by the CAC burden.…”

Section: Applications Of Machine Learning In Cardiac Computed Tomographymentioning

confidence: 99%

“… 5 Moreover, the study by Xu et al . 8 demonstrated the effect of poor image quality and tachycardia on the performance of the algorithm. Indeed, performance was substantially decreased in low-quality images vs. high-quality images, subjectively determined by expert readers (AUC: 0.80 vs. 0.93, respectively).…”

Section: Applications Of Machine Learning In Cardiac Computed Tomographymentioning

confidence: 99%

“…Using numerous iterations of CNN algorithms, CT-FFR has consistently been demonstrated to be superior to CCTA in assessing flow-limiting lesions with an average area under the curve (AUC) of 0.89 ( Table 2 ). 6 , 8 , 11–15 Early work demonstrated that this technique can reduce processing durations by 80-fold compared with physics-based computations, 5 in addition to being less computationally demanding. 6 Nevertheless, Itu et al .…”

Section: Applications Of Machine Learning In Cardiac Computed Tomographymentioning

confidence: 99%

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Machine learning applications in cardiac computed tomography: a composite systematic review

Bray

Hanif

Alradhawi

et al. 2022

European Heart Journal Open

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Introduction Artificial intelligence (AI) and machine learning (ML) models are rapidly being applied to analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Methods Six searches were performed in Medline, Embase and the Cochrane Library up to November 2021 for i) Computed Tomography-Fractional Flow Reserve (CT-FFR), ii) atrial fibrillation, iii) aortic stenosis, iv) plaque characterisation, v) fat quantification and vi) coronary artery calcium score. Results We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-fractional flow reserve (FFR) can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification (CAC) and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue (EAT) can also be automatically, accurately and rapidly quantified. Effective ML algorithms have been developed to streamline and optimise the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement (TAVR) valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of atrial fibrillation. Conclusion In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.

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Artificial Intelligence Integration into the Computed Tomography System

Sühling

Großkopf

Gutjahr

et al. 2022

Artificial Intelligence in Cardiothoracic Imaging

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The influence of image quality on diagnostic performance of a machine learning–based fractional flow reserve derived from coronary CT angiography

Cited by 32 publications

References 36 publications

Combined Coronary CT-Angiography and TAVI Planning: Utility of CT-FFR in Patients with Morphologically Ruled-Out Obstructive Coronary Artery Disease

Combined Coronary CT-Angiography and TAVI Planning: Utility of CT-FFR in Patients with Morphologically Ruled-Out Obstructive Coronary Artery Disease

Machine learning applications in cardiac computed tomography: a composite systematic review

Artificial Intelligence Integration into the Computed Tomography System

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