Machine learning and computational fluid dynamics derived FFRCT demonstrate comparable diagnostic performance in patients with coronary artery disease; A Systematic Review and Meta-Analysis.

• Tác giả: Mahboob Alam, Alireza Arzhangzadeh, Kaveh Hosseini, Seyed Kianoosh Hosseini, Omar Khalique, Nakisa Khansari, Toshiki Kuno, Mehrdad Mahalleh, Amr Mohsen, Hamid Mojibian, Mehdi Moradi, Roya Najafi-Vosough, Roozbeh Narimani-Javid, Salma Nozhat, Sasan Shafiei, Zahra Shaghaghi
• Ngôn ngữ: eng
• Ký hiệu phân loại: 726.59 Parts
• Thông tin xuất bản: United States : Journal of cardiovascular computed tomography , 2025
• Bộ sưu tập: NCBI
• ID: 644138

Tóm tắt

BACKGROUND: As a new noninvasive diagnostic technique, computed tomography-derived fraction flow reserve (FFRCT) has been used to identify hemodynamically significant coronary artery stenosis. FFRCT can be calculated using computational fluid dynamics (CFD) or machine learning (ML) approaches. It was hypothesized that ML-based FFRCT (FFRCT METHODS: We searched PubMed, Cochrane Library, EMBASE, WOS, and Scopus for articles published until March 2024. We analyzed the synthesized sensitivity, specificity, and diagnostic odds ratio (DOR) of FFRCT RESULTS: This meta-analysis included 23 studies reporting FFRCT CONCLUSION: The FFRCT