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...

תיאור מלא

מידע ביבליוגרפי

Principais autores:	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
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Elsevier 2024-12-01
סדרה:	International Journal of Cardiology: Heart & Vasculature
נושאים:	Machine learning Coronary computed tomography angiography (CCTA) Fractional flow reserve (FFR) Invasive coronary angiography (ICA)
גישה מקוונת:	http://www.sciencedirect.com/science/article/pii/S2352906724001945
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!