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  • Kim, Byeong-Keuk  (3)
  • Unknown  (3)
  • 1
    In: Frontiers in Cardiovascular Medicine, Frontiers Media SA, Vol. 9 ( 2022-6-13)
    Abstract: Coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) provide additional functional information beyond the anatomy by applying computational fluid dynamics (CFD). This study sought to evaluate a novel approach for estimating computational fractional flow reserve (FFR) from coronary CTA-OCT fusion images. Methods Among patients who underwent coronary CTA, 148 patients who underwent both pressure wire-based FFR measurement and OCT during angiography to evaluate intermediate stenosis in the left anterior descending artery were included from the prospective registry. Coronary CTA-OCT fusion images were created, and CFD was applied to estimate computational FFR. Based on pressure wire-based FFR as a reference, the diagnostic performance of Fusion-FFR was compared with that of CT-FFR and OCT-FFR. Results Fusion-FFR was strongly correlated with FFR ( r = 0.836, P & lt; 0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR ( r = 0.682, P & lt; 0.001; z statistic, 5.42, P & lt; 0.001) and between FFR and OCT-FFR ( r = 0.705, P & lt; 0.001; z statistic, 4.38, P & lt; 0.001). Area under the receiver operating characteristics curve to assess functionally significant stenosis was higher for Fusion-FFR than for CT-FFR (0.90 vs. 0.83, P = 0.024) and OCT-FFR (0.90 vs. 0.83, P = 0.043). Fusion-FFR exhibited 84.5% accuracy, 84.6% sensitivity, 84.3% specificity, 80.9% positive predictive value, and 87.5% negative predictive value. Especially accuracy, specificity, and positive predictive value were superior for Fusion-FFR than for CT-FFR (73.0%, P = 0.007; 61.4%, P & lt; 0.001; 64.0%, P & lt; 0.001) and OCT-FFR (75.7%, P = 0.021; 73.5%, P = 0.020; 69.9%, P = 0.012). Conclusion CFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone. Clinical Trial Registration [ www.ClinicalTrials.gov ], identifier [NCT03298282] .
    Type of Medium: Online Resource
    ISSN: 2297-055X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2781496-8
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  • 2
    In: Frontiers in Cardiovascular Medicine, Frontiers Media SA, Vol. 10 ( 2023-1-25)
    Abstract: This study aimed to evaluate and compare the diagnostic accuracy of machine learning (ML)- fractional flow reserve (FFR) based on optical coherence tomography (OCT) with wire-based FFR irrespective of the coronary territory. Background ML techniques for assessing hemodynamics features including FFR in coronary artery disease have been developed based on various imaging modalities. However, there is no study using OCT-based ML models for all coronary artery territories. Methods OCT and FFR data were obtained for 356 individual coronary lesions in 130 patients. The training and testing groups were divided in a ratio of 4:1. The ML-FFR was derived for the testing group and compared with the wire-based FFR in terms of the diagnosis of ischemia (FFR ≤ 0.80). Results The mean age of the subjects was 62.6 years. The numbers of the left anterior descending, left circumflex, and right coronary arteries were 130 (36.5%), 110 (30.9%), and 116 (32.6%), respectively. Using seven major features, the ML-FFR showed strong correlation ( r = 0.8782, P & lt; 0.001) with the wire-based FFR. The ML-FFR predicted wire-based FFR ≤ 0.80 in the test set with sensitivity of 98.3%, specificity of 61.5%, and overall accuracy of 91.7% (area under the curve: 0.948). External validation showed good correlation ( r = 0.7884, P & lt; 0.001) and accuracy of 83.2% (area under the curve: 0.912). Conclusion OCT-based ML-FFR showed good diagnostic performance in predicting FFR irrespective of the coronary territory. Because the study was a small-size study, the results should be warranted the performance in further large-scale research.
    Type of Medium: Online Resource
    ISSN: 2297-055X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2781496-8
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  • 3
    In: EuroIntervention, Europa Digital & Publishing, Vol. 9, No. 12 ( 2014-4), p. 1389-1397
    Type of Medium: Online Resource
    ISSN: 1774-024X
    Language: Unknown
    Publisher: Europa Digital & Publishing
    Publication Date: 2014
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