Coronary artery decision algorithm trained by two-step machine learning algorithm

Kim, Young Woo; Yu, Hee-Jin; Kim, Jung-Sun; Ha, Job; Choi, Jongeun; Lee, Joon Sang

doi:10.1039/c9ra08999c

Cited by 5 publications

(5 citation statements)

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“…This study demonstrated that various imaging tools have been used, including CCTA [ 24 – 40 ], OCT [ 41 – 43 ], XCA [ 44 ], and IVUS [ 45 ]. Several studies used a combination of CCTA with OCT [ 46 ], IVUS [ 47 ], and XCA [ 48 ], and a study combined IVUS and XCA [ 49 ] to estimate the FFR. Most of the studies used CCTA to estimate the FFR.…”

Section: Resultsmentioning

confidence: 99%

“…Most of the studies used CCTA to estimate the FFR. AI methods used include methods based on DL [ 25 , 27 – 29 , 34 , 40 , 41 , 48 ] and ML [ 26 , 42 , 30 , 32 , 33 , 37 , 39 , 44 , 46 , 49 ]. Some studies have used a combination of DL and ML techniques [ 24 , 32 , 37 , 38 , 45 , 47 ].…”

Section: Resultsmentioning

confidence: 99%

“…This study also shows that in several studies, image segmentation steps and feature extraction using DL methods have been done [ 27 , 29 , 32 , 34 , 35 , 37 , 38 , 40 , 41 , 43 ]. In addition, in some studies, the parameters in the images were extracted using manual methods and commercial software [ 25 , 28 , 30 , 42 , 46 , 30 , 26 , 45 , 33 , 44 , 49 , 39 ]. Due to the advantages of using automatic methods to extract the features of images, most studies have used automatic methods to extract features in the past year.…”

Section: Features and Feature Engineeringmentioning

confidence: 99%

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Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques

Farhad,

Reza,

Azamossadat

et al. 2023

BMC Cardiovasc Disord

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Background Fractional Flow Reserve (FFR) is the gold standard for the functional evaluation of coronary arteries, which is effective in selecting patients for revascularization, avoiding unnecessary procedures, and reducing treatment costs. However, its use is limited due to invasiveness, high cost, and complexity. Therefore, the non-invasive estimation of FFR using artificial intelligence (AI) methods is crucial. Objective This study aimed to identify the AI techniques used for FFR estimation and to explore the features of the studies that applied AI techniques in FFR estimation. Methods The present systematic review was conducted by searching five databases, PubMed, Scopus, Web of Science, IEEE, and Science Direct, based on the search strategy of each database. Results Five hundred seventy-three articles were extracted, and by applying the inclusion and exclusion criteria, twenty-five were finally selected for review. The findings revealed that AI methods, including Machine Learning (ML) and Deep Learning (DL), have been used to estimate the FFR. Conclusion This study shows that AI methods can be used non-invasively to estimate FFR, which can help physicians diagnose and treat coronary artery occlusion and provide significant clinical performance for patients.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Features and Feature Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques

Farhad,

Reza,

Azamossadat

et al. 2023

BMC Cardiovasc Disord

View full text Add to dashboard Cite

show abstract

“…CFD-based blood flow simulation of reconstructed three-dimensional models was performed using the lattice Boltzmann method, which has been widely used for biofluidics; it uses lattice grids instead of complicated meshes for complex three-dimensional models ( 19 , 20 ). Blood was modeled as an incompressible non-Newtonian fluid with a density of 1,060 kg/m 3 based on the Carreau–Yasuda model ( 21 ).…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, a no-slip boundary condition was used to calculate the interaction between the vessel wall and blood flow. Detailed equations and numerical methods used for estimating CFD-based computational FFR have been described previously ( 20 ). For each patient, Fusion-FFR, CT-FFR, and OCT-FFR were estimated.…”

Section: Methodsmentioning

confidence: 99%

Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography—Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis

Lee

Kim

et al. 2022

Front. Cardiovasc. Med.

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BackgroundCoronary 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.MethodsAmong 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.ResultsFusion-FFR was strongly correlated with FFR (r = 0.836, P < 0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR (r = 0.682, P < 0.001; z statistic, 5.42, P < 0.001) and between FFR and OCT-FFR (r = 0.705, P < 0.001; z statistic, 4.38, P < 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 < 0.001; 64.0%, P < 0.001) and OCT-FFR (75.7%, P = 0.021; 73.5%, P = 0.020; 69.9%, P = 0.012).ConclusionCFD-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].

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Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach

Ryu¹,

Kim²,

Yu³

et al. 2021

Computer Methods and Programs in Biomedicine

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Coronary artery decision algorithm trained by two-step machine learning algorithm

Abstract: A two-step machine learning (ML) algorithm for coronary artery decision making is introduced, to increase the data quality by providing flow characteristics and biometric features by aid of computational fluid dynamics (CFD).

Cited by 5 publications

References 34 publications

Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques

Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques

Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography—Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis

Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach

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