Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review

Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review

Background: The use of machine learning (ML) based coronary computed tomography angiography (CCTA) derived fractional flow reserve (ML-FFRCT), shortens the time of diagnosis of ischemia considerably and eliminates unnecessary invasive procedures, when compared to invasive coronary angiography with i...

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Bibliographic Details
Main Authors: Danny van Noort, Liang Guo, Shuang Leng, Luming Shi, Ru-San Tan, Lynette Teo, Min Sen Yew, Lohendran Baskaran, Ping Chai, Felix Keng, Mark Chan, Terrance Chua, Swee Yaw Tan, Liang Zhong
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:International Journal of Cardiology: Heart & Vasculature
Subjects:
Machine learning
Coronary computed tomography angiography (CCTA)
Fractional flow reserve (FFR)
Invasive coronary angiography (ICA)
Online Access:http://www.sciencedirect.com/science/article/pii/S2352906724001945
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