2018
DOI: 10.1007/s00330-018-5822-3
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Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis

Abstract: Objectives To evaluate the added value of deep learning (DL) analysis of the left ventricular myocardium (LVM) in resting coronary CT angiography (CCTA) over determination of coronary degree of stenosis (DS), for identification of patients with functionally significant coronary artery stenosis. Methods Patients who underwent CCTA prior to an invasive fractional flow reserve (FFR) measurement were retrospectively selected. Highest DS from CCTA was used to classify patien… Show more

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Cited by 80 publications
(64 citation statements)
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“…The growing amount of data generated by new CT acquisition techniques (eg, SDCT and CTP) makes DL analysis a technique of high interest. A recent study showed that patients with functionally significant coronary artery stenosis can be identified using DL analysis on CCTA 11 12. By combining these new technologies (SDCT, CTP, FFR CT and DL), we hypothesise more accurate information will be available about the coronary anatomy, degree of stenosis, FFR CT and myocardial perfusion, thereby contributing to a higher specificity of CT for identification of functionally significant coronary artery stenosis.…”
Section: Introductionmentioning
confidence: 99%
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“…The growing amount of data generated by new CT acquisition techniques (eg, SDCT and CTP) makes DL analysis a technique of high interest. A recent study showed that patients with functionally significant coronary artery stenosis can be identified using DL analysis on CCTA 11 12. By combining these new technologies (SDCT, CTP, FFR CT and DL), we hypothesise more accurate information will be available about the coronary anatomy, degree of stenosis, FFR CT and myocardial perfusion, thereby contributing to a higher specificity of CT for identification of functionally significant coronary artery stenosis.…”
Section: Introductionmentioning
confidence: 99%
“…Until these results are published, cardiac CT is the single modality able to simultaneously evaluate coronary artery anatomy and functional information in one investigation non-invasively. Recent developments in CT acquisition and image analysis techniques, such as dual-energy CT (DECT), static stress CT perfusion imaging (CTP), FFR derived from CCTA images (FFR CT ) and deep learning (DL) based image analysis, have shown the potential to improve the specificity of CCTA by combining both anatomical and functional information in one investigation 5–12…”
Section: Introductionmentioning
confidence: 99%
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“…For example, calcium scoring in low-dose chest computed tomography (CT), identification of functionally significant stenosis in CT angiography, and diagnosis of chronic myocardial infarction on cine magnetic resonance image (MRI) have been developed. [3][4][5] Compared with CT and MRI, in echocardiography there is an issue of high observer variation in the interpretation of images. Thus, AI might be help to improve observer variation and provide accurate diagnosis in echocardiography.…”
mentioning
confidence: 99%
“…For the same purpose, Zreik et al(53) trained an SVM based on features from myocardial regions extracted from CCTA. Clinical evaluation of this method yielded improved diagnostic accuracy of FFR CT over visual evaluation of stenosis(79). Hae et al(51) increased accuracy of FFR-prediction by including the tissue volume subtended to a stenotic lesion in analysis.…”
mentioning
confidence: 99%