Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis

Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learn...
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Autor*in:	Haoyu Wu [verfasserIn] Lei Liang [verfasserIn] Fuyu Qiu [verfasserIn] Wenqi Han [verfasserIn] Zheng Yang [verfasserIn] Jie Qi [verfasserIn] Jizhao Deng [verfasserIn] Yida Tang [verfasserIn] Xiling Shou [verfasserIn] Haichao Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	coronary artery disease coronary lesion-specific ischemia fractional flow reserve (ffr) computed tomography angiography-derived ffr (ct-ffr) coronary computed tomographic angiography deep learning analysis

Übergeordnetes Werk:	In: Reviews in Cardiovascular Medicine - IMR Press, 2020, 25(2024), 1, p 20
Übergeordnetes Werk:	volume:25 ; year:2024 ; number:1, p 20

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.31083/j.rcm2501020

Katalog-ID:	DOAJ096119896

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520			\|a Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. Methods: In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. Results: With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR (p < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment.
650		4	\|a coronary artery disease
650		4	\|a coronary lesion-specific ischemia
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650		4	\|a coronary computed tomographic angiography
650		4	\|a deep learning analysis
653		0	\|a Diseases of the circulatory (Cardiovascular) system
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700	0		\|a Haichao Chen \|e verfasserin \|4 aut
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allfields	10.31083/j.rcm2501020 doi (DE-627)DOAJ096119896 (DE-599)DOAJb257bc87945d49b0aebe80a2dd1a5943 DE-627 ger DE-627 rakwb eng RC666-701 Haoyu Wu verfasserin aut Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. Methods: In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. Results: With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR (p < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment. coronary artery disease coronary lesion-specific ischemia fractional flow reserve (ffr) computed tomography angiography-derived ffr (ct-ffr) coronary computed tomographic angiography deep learning analysis Diseases of the circulatory (Cardiovascular) system Lei Liang verfasserin aut Fuyu Qiu verfasserin aut Wenqi Han verfasserin aut Zheng Yang verfasserin aut Jie Qi verfasserin aut Jizhao Deng verfasserin aut Yida Tang verfasserin aut Xiling Shou verfasserin aut Haichao Chen verfasserin aut In Reviews in Cardiovascular Medicine IMR Press, 2020 25(2024), 1, p 20 (DE-627)363773541 (DE-600)2108911-5 21538174 nnns volume:25 year:2024 number:1, p 20 https://doi.org/10.31083/j.rcm2501020 kostenfrei https://doaj.org/article/b257bc87945d49b0aebe80a2dd1a5943 kostenfrei https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501020 kostenfrei https://doaj.org/toc/1530-6550 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2024 1, p 20
spelling	10.31083/j.rcm2501020 doi (DE-627)DOAJ096119896 (DE-599)DOAJb257bc87945d49b0aebe80a2dd1a5943 DE-627 ger DE-627 rakwb eng RC666-701 Haoyu Wu verfasserin aut Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. Methods: In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. Results: With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR (p < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment. coronary artery disease coronary lesion-specific ischemia fractional flow reserve (ffr) computed tomography angiography-derived ffr (ct-ffr) coronary computed tomographic angiography deep learning analysis Diseases of the circulatory (Cardiovascular) system Lei Liang verfasserin aut Fuyu Qiu verfasserin aut Wenqi Han verfasserin aut Zheng Yang verfasserin aut Jie Qi verfasserin aut Jizhao Deng verfasserin aut Yida Tang verfasserin aut Xiling Shou verfasserin aut Haichao Chen verfasserin aut In Reviews in Cardiovascular Medicine IMR Press, 2020 25(2024), 1, p 20 (DE-627)363773541 (DE-600)2108911-5 21538174 nnns volume:25 year:2024 number:1, p 20 https://doi.org/10.31083/j.rcm2501020 kostenfrei https://doaj.org/article/b257bc87945d49b0aebe80a2dd1a5943 kostenfrei https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501020 kostenfrei https://doaj.org/toc/1530-6550 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2024 1, p 20
allfields_unstemmed	10.31083/j.rcm2501020 doi (DE-627)DOAJ096119896 (DE-599)DOAJb257bc87945d49b0aebe80a2dd1a5943 DE-627 ger DE-627 rakwb eng RC666-701 Haoyu Wu verfasserin aut Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. Methods: In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. Results: With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR (p < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment. coronary artery disease coronary lesion-specific ischemia fractional flow reserve (ffr) computed tomography angiography-derived ffr (ct-ffr) coronary computed tomographic angiography deep learning analysis Diseases of the circulatory (Cardiovascular) system Lei Liang verfasserin aut Fuyu Qiu verfasserin aut Wenqi Han verfasserin aut Zheng Yang verfasserin aut Jie Qi verfasserin aut Jizhao Deng verfasserin aut Yida Tang verfasserin aut Xiling Shou verfasserin aut Haichao Chen verfasserin aut In Reviews in Cardiovascular Medicine IMR Press, 2020 25(2024), 1, p 20 (DE-627)363773541 (DE-600)2108911-5 21538174 nnns volume:25 year:2024 number:1, p 20 https://doi.org/10.31083/j.rcm2501020 kostenfrei https://doaj.org/article/b257bc87945d49b0aebe80a2dd1a5943 kostenfrei https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501020 kostenfrei https://doaj.org/toc/1530-6550 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2024 1, p 20
allfieldsGer	10.31083/j.rcm2501020 doi (DE-627)DOAJ096119896 (DE-599)DOAJb257bc87945d49b0aebe80a2dd1a5943 DE-627 ger DE-627 rakwb eng RC666-701 Haoyu Wu verfasserin aut Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. Methods: In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. Results: With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR (p < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment. coronary artery disease coronary lesion-specific ischemia fractional flow reserve (ffr) computed tomography angiography-derived ffr (ct-ffr) coronary computed tomographic angiography deep learning analysis Diseases of the circulatory (Cardiovascular) system Lei Liang verfasserin aut Fuyu Qiu verfasserin aut Wenqi Han verfasserin aut Zheng Yang verfasserin aut Jie Qi verfasserin aut Jizhao Deng verfasserin aut Yida Tang verfasserin aut Xiling Shou verfasserin aut Haichao Chen verfasserin aut In Reviews in Cardiovascular Medicine IMR Press, 2020 25(2024), 1, p 20 (DE-627)363773541 (DE-600)2108911-5 21538174 nnns volume:25 year:2024 number:1, p 20 https://doi.org/10.31083/j.rcm2501020 kostenfrei https://doaj.org/article/b257bc87945d49b0aebe80a2dd1a5943 kostenfrei https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501020 kostenfrei https://doaj.org/toc/1530-6550 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2024 1, p 20
allfieldsSound	10.31083/j.rcm2501020 doi (DE-627)DOAJ096119896 (DE-599)DOAJb257bc87945d49b0aebe80a2dd1a5943 DE-627 ger DE-627 rakwb eng RC666-701 Haoyu Wu verfasserin aut Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. Methods: In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. Results: With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR (p < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment. coronary artery disease coronary lesion-specific ischemia fractional flow reserve (ffr) computed tomography angiography-derived ffr (ct-ffr) coronary computed tomographic angiography deep learning analysis Diseases of the circulatory (Cardiovascular) system Lei Liang verfasserin aut Fuyu Qiu verfasserin aut Wenqi Han verfasserin aut Zheng Yang verfasserin aut Jie Qi verfasserin aut Jizhao Deng verfasserin aut Yida Tang verfasserin aut Xiling Shou verfasserin aut Haichao Chen verfasserin aut In Reviews in Cardiovascular Medicine IMR Press, 2020 25(2024), 1, p 20 (DE-627)363773541 (DE-600)2108911-5 21538174 nnns volume:25 year:2024 number:1, p 20 https://doi.org/10.31083/j.rcm2501020 kostenfrei https://doaj.org/article/b257bc87945d49b0aebe80a2dd1a5943 kostenfrei https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501020 kostenfrei https://doaj.org/toc/1530-6550 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2024 1, p 20
language	English
source	In Reviews in Cardiovascular Medicine 25(2024), 1, p 20 volume:25 year:2024 number:1, p 20
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	coronary artery disease coronary lesion-specific ischemia fractional flow reserve (ffr) computed tomography angiography-derived ffr (ct-ffr) coronary computed tomographic angiography deep learning analysis Diseases of the circulatory (Cardiovascular) system
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authorswithroles_txt_mv	Haoyu Wu @@aut@@ Lei Liang @@aut@@ Fuyu Qiu @@aut@@ Wenqi Han @@aut@@ Zheng Yang @@aut@@ Jie Qi @@aut@@ Jizhao Deng @@aut@@ Yida Tang @@aut@@ Xiling Shou @@aut@@ Haichao Chen @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
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topic_title	RC666-701 Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis coronary artery disease coronary lesion-specific ischemia fractional flow reserve (ffr) computed tomography angiography-derived ffr (ct-ffr) coronary computed tomographic angiography deep learning analysis
topic	misc RC666-701 misc coronary artery disease misc coronary lesion-specific ischemia misc fractional flow reserve (ffr) misc computed tomography angiography-derived ffr (ct-ffr) misc coronary computed tomographic angiography misc deep learning analysis misc Diseases of the circulatory (Cardiovascular) system
topic_unstemmed	misc RC666-701 misc coronary artery disease misc coronary lesion-specific ischemia misc fractional flow reserve (ffr) misc computed tomography angiography-derived ffr (ct-ffr) misc coronary computed tomographic angiography misc deep learning analysis misc Diseases of the circulatory (Cardiovascular) system
topic_browse	misc RC666-701 misc coronary artery disease misc coronary lesion-specific ischemia misc fractional flow reserve (ffr) misc computed tomography angiography-derived ffr (ct-ffr) misc coronary computed tomographic angiography misc deep learning analysis misc Diseases of the circulatory (Cardiovascular) system
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title	Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis
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title_full	Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis
author_sort	Haoyu Wu
journal	Reviews in Cardiovascular Medicine
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author_browse	Haoyu Wu Lei Liang Fuyu Qiu Wenqi Han Zheng Yang Jie Qi Jizhao Deng Yida Tang Xiling Shou Haichao Chen
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author-letter	Haoyu Wu
doi_str_mv	10.31083/j.rcm2501020
author2-role	verfasserin
title_sort	diagnostic performance of noninvasive coronary computed tomography angiography-derived ffr for coronary lesion-specific ischemia based on deep learning analysis
callnumber	RC666-701
title_auth	Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis
abstract	Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. Methods: In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. Results: With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR (p < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment.
abstractGer	Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. Methods: In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. Results: With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR (p < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment.
abstract_unstemmed	Background: The noninvasive computed tomography angiography–derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. Methods: In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%–90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. Results: With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland–Altman analysis revealed a direct correlation between the CT-FFR and FFR (p < 0.001), without systematic differences (p = 0.085). Conclusions: The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment.
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container_issue	1, p 20
title_short	Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis
url	https://doi.org/10.31083/j.rcm2501020 https://doaj.org/article/b257bc87945d49b0aebe80a2dd1a5943 https://www.imrpress.com/journal/RCM/25/1/10.31083/j.rcm2501020 https://doaj.org/toc/1530-6550
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author2	Lei Liang Fuyu Qiu Wenqi Han Zheng Yang Jie Qi Jizhao Deng Yida Tang Xiling Shou Haichao Chen
author2Str	Lei Liang Fuyu Qiu Wenqi Han Zheng Yang Jie Qi Jizhao Deng Yida Tang Xiling Shou Haichao Chen
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up_date	2024-07-03T18:24:50.233Z
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Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis

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