Evaluation of the diagnostic value of artificial intelligence-based CT-FFR and FAI for coronary artery stenosis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WU Weiqiang ,  ZHU Liangfei , DAI Zhuonan , LIN Yuhuan , CAI Lili

Department of Radiology, Guangdong Province Traditional Chinese and Western Medicine Hospital, Foshan, 528000, Guangdong, P. R. China;

Corresponding?author：

ZHU Liangfei, Email: zhuliang.fei@163.com

Keywords：

Artificial intelligence; cardiovascular diseases; radiation dose; computed tomography angiography; CT-fractional flow reserve; fat attenuation index; coronary artery stenosis

DOI：

10.7507/1007-4848.202512028

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To investigate the diagnostic value of CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI) based on artificial intelligence-assisted diagnostic software in coronary artery stenosis. Methods A retrospective analysis was conducted on patients clinically suspected of coronary artery syndrome who underwent coronary computed tomography angiography at Guangdong Province Traditional Chinese and Western Medicine Hospital between June 2021 and May 2025. Patients were divided into two groups according to scanning protocols: group A underwent conventional retrospective electrocardiography-gated scanning, while group B used Flash_ChestPlin mode. Invasive coronary angiography data served as the gold standard for diagnosing vascular stenosis (stenosis rate<50% defined as negative group, ≥50% with clinical symptoms as positive group). Radiation dose was compared between the two scanning protocols. The diagnostic efficacy of CT-FFR, pericoronary FAI, and transluminal attenuation gradient (TAG) based on artificial intelligence system for coronary stenosis was analyzed, including sensitivity, specificity, and area under the curve (AUC). Results A total of 567 vessels from 189 patients were analyzed, including 105 males, 84 females with a mean age of (62.5±12.3) years and a mean body mass index of (24.21±3.5) kg/m². There were 112 patients in the group A and 77 patients in the group B. The radiation dose in the group B was significantly lower than that in the group A [69.7 (58.1, 84.1) mGy·cm vs. 420.4 (338.6, 514.2) mGy·cm, P<0.001]. Significant differences in FAI and CT-FFR were observed between negative and positive groups under both scanning protocols (P<0.05), while no significant difference existed in TAG (P>0.05). In the group A, the AUC values for diagnosing stenosis were 0.925 for CT-FFR, 0.610 for FAI, and 0.516 for TAG. Corresponding values in the group B were 0.889, 0.677, and 0.548 respectively, with CT-FFR demonstrating optimal diagnostic performance. Conclusion Under both conventional scanning and Flash scanning, the artificial intelligence-based CT-FFR demonstrates good diagnostic performance for coronary artery stenosis, and the Flash protocol significantly lowers radiation dose, indicating substantial potential for clinical application.

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2.	World Heart Federation. World Heart Report 2023: confronting the world's number one killer. Geneva: World Heart Federation, 2023.
3.	Wang H, Zhang H, Zou Z. Changing profiles of cardiovascular disease and risk factors in China: a secondary analysis for the Global Burden of Disease Study 2019. Chin Med J (Engl), 2023, 136(20): 2431-2441.
4.	中華醫學會心血管病學分會, 中華心血管病雜志編輯委員會. 缺血性心肌病血運重建專家共識. 中華心血管病雜志, 2025, 53(4): 343-355.Chinese Society of Cardiology, Editorial Board of Chinese Journal of Cardiology. Expert consensus on revascularization for ischemic cardiomyopathy. Chin J Cardiol, 2025, 53(4): 343-355.
5.	Li Y, Qiu H, Hou Z, et al. Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes. Acta Radiol, 2022, 63(1): 133-140.
6.	Tesche C, De Cecco CN, Albrecht MH, et al. Coronary CT angiography-derived fractional flow reserve. Radiology, 2017, 285(1): 17-33.
7.	王點, 翟光耀. 無創血流儲備分數的歷史和現狀及發展趨勢. 中國醫藥, 2025, 20(1): 133-137.Wang D, Zhai GY. History, current status and development trend of noninvasive fractional flow reserve. China Med, 2025, 20(1): 133-137.
8.	鄧欣, 沈靂, 王瑞, 等. 基于冠狀動脈CT的血流儲備分數在心肌缺血中的診斷應用價值: 一項單中心前瞻性研究. 中國介入心臟病學雜志, 2021, 29(3): 138-142.Deng X, Shen L, Wang R, et al. Diagnostic value of CT-derived fractional flow reserve in myocardial ischemia: a single-center prospective study. Chin J Interv Cardiol, 2021, 29(3): 138-142.
9.	中華醫學會放射學分會質量控制與安全管理專業委員會, 江蘇省醫學會放射學分會智能影像與質量安全學組. 冠狀動脈CT血流儲備分數應用中國專家建議. 中華放射學雜志, 2020, 54(10): 925-933.Professional Committee of Quality Control and Safety Management, Chinese Society of Radiology; Intelligent Imaging and Quality Safety Group, Jiangsu Medical Association Radiology Society. Chinese expert recommendations on the application of coronary CT fractional flow reserve. Chin J Radiol, 2020, 54(10): 925-933.
10.	湯林夢, 劉峰, 董婷宇, 等. CT管腔內密度衰減梯度聯合無創血流儲備分數對心肌缺血性損傷的診斷價值. 臨床放射學雜志, 2023, 42(4): 585-590.Tang LM, Liu F, Dong TY, et al. Diagnostic value of CT transluminal attenuation gradient combined with noninvasive fractional flow reserve for myocardial ischemic injury. J Clin Radiol, 2023, 42(4): 585-590.
11.	丁熠璞, 單冬凱, 王璽, 等. 冠狀動脈周圍FAI對CT-FFR診斷重度鈣化患者冠脈血流動力學異常的增量價值. 解放軍醫學雜志, 2021, 46(7): 666-672.Ding YP, Shan DK, Wang X, et al. Incremental value of pericoronary FAI for CT-FFR in diagnosing coronary hemodynamic abnormalities in patients with severe calcification. Med J Chin PLA, 2021, 46(7): 666-672.
12.	宋瑤, 霍懷璧, 李晗, 等. 冠狀動脈CTA多參數AI特征對急性冠脈綜合征的診斷價值. 放射學實踐, 2023, 38(7): 873-878.Song Y, Huo HB, Li H, et al. Diagnostic value of multi-parameter AI features of coronary CTA for acute coronary syndrome. Radiol Pract, 2023, 38(7): 873-878.
13.	孫一, 劉博, 孫秀彬, 等. CCTA-AI聯合FFR-CT診斷冠狀動脈狹窄病變的價值. 醫學影像學雜志, 2022, 32(12): 2081-2085.Sun Y, Liu B, Sun XB, et al. Value of CCTA-AI combined with FFR-CT in diagnosing coronary artery stenosis. J Med Imaging, 2022, 32(12): 2081-2085.
14.	中國醫師協會心血管內科醫師分會超聲心動圖和影像學組, 《中國介入心臟病學雜志》編輯委員會. 冠狀動脈CT血流儲備分數應用臨床路徑中國專家共識. 中國介入心臟病學雜志, 2023, 31(4): 241-251.Echocardiography and Cardiovascular Imaging Group of Chinese College of Cardiovascular Physicians, Editorail Board of Chinese Journal of Interventional Cardiology. Chinese expert consensus on the clinical pathway of coronary computed tomography angiography-derived fractional flow reserve. Chin J Interv Cardiol, 2023, 31(4): 241-251.
15.	趙古月, 尚靳, 侯陽. 人工智能在冠狀動脈CT血管成像的應用進展. 山東大學學報(醫學版), 2023, 61(12): 30-35.Zhao GY, Shang J, Hou Y. Application progress of artificial intelligence in coronary CT angiography. J Shandong Univ (Health Sci), 2023, 61(12): 30-35.
16.	Guo B, Jiang M, Guo X, et al. Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD. Sci Bull, 2024, 69(10): 1472-1485.
17.	吳衛強, 朱亮飛, 沈麗榮, 等. 基于人工智能肺動脈CT血管造影及肺栓塞檢出應用的效能評估. 中國呼吸與危重監護雜志, 2024, 23(1): 24-29.Wu WQ, Zhu LF, Shen LR, et al. Efficacy evaluation of artificial intelligence-based pulmonary artery CTA in pulmonary embolism detection. Chin J Respir Crit Care Med, 2024, 23(1): 24-29.
18.	竇末未, 劉遠健, 陳凱, 等. 人工智能對外傷性腦出血的診斷及定量臨床應用研究. 醫學信息, 2023, 36(15): 57-63.Dou MW, Liu YJ, Chen K, et al. Clinical application of artificial intelligence in diagnosis and quantification of traumatic intracerebral hemorrhage. Med Inf, 2023, 36(15): 57-63.
19.	彭鯤. 冠狀動脈CTA腔內衰減梯度及其校正模式輔助評估血管狹窄程度的初步研究. 南京醫科大學, 2017.Peng K. Preliminary study on coronary CTA transluminal attenuation gradient and its correction mode in assessing vascular stenosis. Nanjing Medical University, 2017.
20.	滕雅琴, 姚亮, 于集紅, 等. Force CT大螺距Turbo Flash與回顧性心電門控掃描模式在冠狀動脈成像圖像質量及輻射劑量比較的研究. 中國CT和MRI雜志, 2024, 22(7): 96-98.Teng YQ, Yao L, Yu JH, et al. Comparison of image quality and radiation dose between Force CT large-pitch Turbo Flash and retrospective electrocardiographic gating in coronary angiography. Chin J CT MRI, 2024, 22(7): 96-98.
21.	Yu L, Chen X, Ling R, et al. Radiomics features of pericoronary adipose tissue improve CT-FFR performance in predicting hemodynamically significant coronary artery stenosis. Eur Radiol, 2022, 33(3): 2004-2014.
22.	Takagi H, Ihdayhid AR, Leipsic JA. Integration of fractional flow reserve derived from CT into clinical practice. J Cardiol, 2023, 81(6): 577-585.
23.	Yin S, Luo X, Yang Y, et al. Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study. Neuro-Oncology, 2022, 24(9): 1559-1570.
24.	Tang XC, Wang NY, Zhou F, et al. Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: a multi-center study and meta-analysis. Eur J Radiol, 2019, 116: 90-97.
25.	Ihdayhid AR, Norgaard BL, Gaur S, et al. Prognostic value and risk continuum of noninvasive fractional flow reserve derived from coronary CT angiography. Radiology, 2019, 292(2): 343-351.
26.	Tang XC, Liu YC, Lu JM, et al. CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm. JACC Cardiovasc Imaging, 2020, 13(4): 980-990.
27.	Douglas PS, Pontone G, Hlatky MA, et al. Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFRCT: outcome and resource impacts study. Eur Heart J, 2015, 36(47): 3359-3367.
28.	Kodama Y, Sakurai Y, Yamasaki K, et al. High false-negative rate of the anterior spinal artery by intercostobronchial trunk arteriography alone compared to CT during arteriography. Br J Radiol, 2021, 94(1123): 20210402.
29.	丁毅, 宋彬, 孫熙臨, 等. 第三代雙源CT大螺距掃描冠脈成像在低心率患者中的應用分析. 中國臨床醫學影像雜志, 2019, 30(11): 789-794.Ding Y, Song B, Sun XL, et al. Application of third-generation dual-source CT large-pitch coronary angiography in patients with low heart rate. J Clin Med Imaging, 2019, 30(11): 789-794.
30.	駱科純, 朱曉梅. 無心電門控自由呼吸Flash模式冠狀動脈CT血管成像的可行性及可靠性. 哈爾濱醫科大學學報, 2023, 57(1): 66-70, 76.Luo KC, Zhu XM. Feasibility and reliability of non-electrocardiographic gated free-breathing Flash mode coronary CTA. J Harbin Med Univ, 2023, 57(1): 66-70, 76.

1. 國家心血管病中心, 中國心血管健康與疾病報告編寫組, 胡盛壽. 中國心血管健康與疾病報告2024概要. 中國循環雜志, 2025, 40(6): 521-559.National Center for Cardiovascular Diseases, Editorial Board of China Cardiovascular Health and Disease Report, Hu SS. Summary of China cardiovascular health and disease report 2024. Chin Circ J, 2025, 40(6): 521-559.
2. World Heart Federation. World Heart Report 2023: confronting the world's number one killer. Geneva: World Heart Federation, 2023.
3. Wang H, Zhang H, Zou Z. Changing profiles of cardiovascular disease and risk factors in China: a secondary analysis for the Global Burden of Disease Study 2019. Chin Med J (Engl), 2023, 136(20): 2431-2441.
4. 中華醫學會心血管病學分會, 中華心血管病雜志編輯委員會. 缺血性心肌病血運重建專家共識. 中華心血管病雜志, 2025, 53(4): 343-355.Chinese Society of Cardiology, Editorial Board of Chinese Journal of Cardiology. Expert consensus on revascularization for ischemic cardiomyopathy. Chin J Cardiol, 2025, 53(4): 343-355.
5. Li Y, Qiu H, Hou Z, et al. Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes. Acta Radiol, 2022, 63(1): 133-140.
6. Tesche C, De Cecco CN, Albrecht MH, et al. Coronary CT angiography-derived fractional flow reserve. Radiology, 2017, 285(1): 17-33.
7. 王點, 翟光耀. 無創血流儲備分數的歷史和現狀及發展趨勢. 中國醫藥, 2025, 20(1): 133-137.Wang D, Zhai GY. History, current status and development trend of noninvasive fractional flow reserve. China Med, 2025, 20(1): 133-137.
8. 鄧欣, 沈靂, 王瑞, 等. 基于冠狀動脈CT的血流儲備分數在心肌缺血中的診斷應用價值: 一項單中心前瞻性研究. 中國介入心臟病學雜志, 2021, 29(3): 138-142.Deng X, Shen L, Wang R, et al. Diagnostic value of CT-derived fractional flow reserve in myocardial ischemia: a single-center prospective study. Chin J Interv Cardiol, 2021, 29(3): 138-142.
9. 中華醫學會放射學分會質量控制與安全管理專業委員會, 江蘇省醫學會放射學分會智能影像與質量安全學組. 冠狀動脈CT血流儲備分數應用中國專家建議. 中華放射學雜志, 2020, 54(10): 925-933.Professional Committee of Quality Control and Safety Management, Chinese Society of Radiology; Intelligent Imaging and Quality Safety Group, Jiangsu Medical Association Radiology Society. Chinese expert recommendations on the application of coronary CT fractional flow reserve. Chin J Radiol, 2020, 54(10): 925-933.
10. 湯林夢, 劉峰, 董婷宇, 等. CT管腔內密度衰減梯度聯合無創血流儲備分數對心肌缺血性損傷的診斷價值. 臨床放射學雜志, 2023, 42(4): 585-590.Tang LM, Liu F, Dong TY, et al. Diagnostic value of CT transluminal attenuation gradient combined with noninvasive fractional flow reserve for myocardial ischemic injury. J Clin Radiol, 2023, 42(4): 585-590.
11. 丁熠璞, 單冬凱, 王璽, 等. 冠狀動脈周圍FAI對CT-FFR診斷重度鈣化患者冠脈血流動力學異常的增量價值. 解放軍醫學雜志, 2021, 46(7): 666-672.Ding YP, Shan DK, Wang X, et al. Incremental value of pericoronary FAI for CT-FFR in diagnosing coronary hemodynamic abnormalities in patients with severe calcification. Med J Chin PLA, 2021, 46(7): 666-672.
12. 宋瑤, 霍懷璧, 李晗, 等. 冠狀動脈CTA多參數AI特征對急性冠脈綜合征的診斷價值. 放射學實踐, 2023, 38(7): 873-878.Song Y, Huo HB, Li H, et al. Diagnostic value of multi-parameter AI features of coronary CTA for acute coronary syndrome. Radiol Pract, 2023, 38(7): 873-878.
13. 孫一, 劉博, 孫秀彬, 等. CCTA-AI聯合FFR-CT診斷冠狀動脈狹窄病變的價值. 醫學影像學雜志, 2022, 32(12): 2081-2085.Sun Y, Liu B, Sun XB, et al. Value of CCTA-AI combined with FFR-CT in diagnosing coronary artery stenosis. J Med Imaging, 2022, 32(12): 2081-2085.
14. 中國醫師協會心血管內科醫師分會超聲心動圖和影像學組, 《中國介入心臟病學雜志》編輯委員會. 冠狀動脈CT血流儲備分數應用臨床路徑中國專家共識. 中國介入心臟病學雜志, 2023, 31(4): 241-251.Echocardiography and Cardiovascular Imaging Group of Chinese College of Cardiovascular Physicians, Editorail Board of Chinese Journal of Interventional Cardiology. Chinese expert consensus on the clinical pathway of coronary computed tomography angiography-derived fractional flow reserve. Chin J Interv Cardiol, 2023, 31(4): 241-251.
15. 趙古月, 尚靳, 侯陽. 人工智能在冠狀動脈CT血管成像的應用進展. 山東大學學報(醫學版), 2023, 61(12): 30-35.Zhao GY, Shang J, Hou Y. Application progress of artificial intelligence in coronary CT angiography. J Shandong Univ (Health Sci), 2023, 61(12): 30-35.
16. Guo B, Jiang M, Guo X, et al. Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD. Sci Bull, 2024, 69(10): 1472-1485.
17. 吳衛強, 朱亮飛, 沈麗榮, 等. 基于人工智能肺動脈CT血管造影及肺栓塞檢出應用的效能評估. 中國呼吸與危重監護雜志, 2024, 23(1): 24-29.Wu WQ, Zhu LF, Shen LR, et al. Efficacy evaluation of artificial intelligence-based pulmonary artery CTA in pulmonary embolism detection. Chin J Respir Crit Care Med, 2024, 23(1): 24-29.
18. 竇末未, 劉遠健, 陳凱, 等. 人工智能對外傷性腦出血的診斷及定量臨床應用研究. 醫學信息, 2023, 36(15): 57-63.Dou MW, Liu YJ, Chen K, et al. Clinical application of artificial intelligence in diagnosis and quantification of traumatic intracerebral hemorrhage. Med Inf, 2023, 36(15): 57-63.
19. 彭鯤. 冠狀動脈CTA腔內衰減梯度及其校正模式輔助評估血管狹窄程度的初步研究. 南京醫科大學, 2017.Peng K. Preliminary study on coronary CTA transluminal attenuation gradient and its correction mode in assessing vascular stenosis. Nanjing Medical University, 2017.
20. 滕雅琴, 姚亮, 于集紅, 等. Force CT大螺距Turbo Flash與回顧性心電門控掃描模式在冠狀動脈成像圖像質量及輻射劑量比較的研究. 中國CT和MRI雜志, 2024, 22(7): 96-98.Teng YQ, Yao L, Yu JH, et al. Comparison of image quality and radiation dose between Force CT large-pitch Turbo Flash and retrospective electrocardiographic gating in coronary angiography. Chin J CT MRI, 2024, 22(7): 96-98.
21. Yu L, Chen X, Ling R, et al. Radiomics features of pericoronary adipose tissue improve CT-FFR performance in predicting hemodynamically significant coronary artery stenosis. Eur Radiol, 2022, 33(3): 2004-2014.
22. Takagi H, Ihdayhid AR, Leipsic JA. Integration of fractional flow reserve derived from CT into clinical practice. J Cardiol, 2023, 81(6): 577-585.
23. Yin S, Luo X, Yang Y, et al. Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study. Neuro-Oncology, 2022, 24(9): 1559-1570.
24. Tang XC, Wang NY, Zhou F, et al. Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: a multi-center study and meta-analysis. Eur J Radiol, 2019, 116: 90-97.
25. Ihdayhid AR, Norgaard BL, Gaur S, et al. Prognostic value and risk continuum of noninvasive fractional flow reserve derived from coronary CT angiography. Radiology, 2019, 292(2): 343-351.
26. Tang XC, Liu YC, Lu JM, et al. CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm. JACC Cardiovasc Imaging, 2020, 13(4): 980-990.
27. Douglas PS, Pontone G, Hlatky MA, et al. Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFRCT: outcome and resource impacts study. Eur Heart J, 2015, 36(47): 3359-3367.
28. Kodama Y, Sakurai Y, Yamasaki K, et al. High false-negative rate of the anterior spinal artery by intercostobronchial trunk arteriography alone compared to CT during arteriography. Br J Radiol, 2021, 94(1123): 20210402.
29. 丁毅, 宋彬, 孫熙臨, 等. 第三代雙源CT大螺距掃描冠脈成像在低心率患者中的應用分析. 中國臨床醫學影像雜志, 2019, 30(11): 789-794.Ding Y, Song B, Sun XL, et al. Application of third-generation dual-source CT large-pitch coronary angiography in patients with low heart rate. J Clin Med Imaging, 2019, 30(11): 789-794.
30. 駱科純, 朱曉梅. 無心電門控自由呼吸Flash模式冠狀動脈CT血管成像的可行性及可靠性. 哈爾濱醫科大學學報, 2023, 57(1): 66-70, 76.Luo KC, Zhu XM. Feasibility and reliability of non-electrocardiographic gated free-breathing Flash mode coronary CTA. J Harbin Med Univ, 2023, 57(1): 66-70, 76.

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Latest ArticlesEvaluation of the diagnostic value of artificial intelligence-based CT-FFR and FAI for coronary artery stenosis

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