Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques

BMC Cardiovascular Disorders

Research

Ghaemian Ali¹  Roshanpoor Arash²  Arefinia Farhad³  Hosseini Azamossadat³  Rabiei Reza³  Aria Mehrad⁴  Khorrami Zahra⁵ 

[1] Cardiovascular Research Center, Mazandaran University of Medical Sciences, Sari, Iran;Department of Computer Science, Sama Technical and Vocational Training College, Tehran Branch (Tehran), Islamic Azad University (IAU), Tehran, Iran;Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran;Department of Information Technology and Computer Engineering and Ophthalmic Epidemiology Research Center, Azarbaijan Shahid Madani University, Tabriz, Iran;Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tabriz, Iran;

关键词: Machine learning;    Fractional Flow Reverse;    Functional evaluation;   

DOI  :  10.1186/s12872-023-03447-w

 received in 2023-05-27, accepted in 2023-08-12,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundFractional Flow Reserve (FFR) is the gold standard for the functional evaluation of coronary arteries, which is effective in selecting patients for revascularization, avoiding unnecessary procedures, and reducing treatment costs. However, its use is limited due to invasiveness, high cost, and complexity. Therefore, the non-invasive estimation of FFR using artificial intelligence (AI) methods is crucial.ObjectiveThis study aimed to identify the AI techniques used for FFR estimation and to explore the features of the studies that applied AI techniques in FFR estimation.MethodsThe present systematic review was conducted by searching five databases, PubMed, Scopus, Web of Science, IEEE, and Science Direct, based on the search strategy of each database.ResultsFive hundred seventy-three articles were extracted, and by applying the inclusion and exclusion criteria, twenty-five were finally selected for review. The findings revealed that AI methods, including Machine Learning (ML) and Deep Learning (DL), have been used to estimate the FFR.ConclusionThis study shows that AI methods can be used non-invasively to estimate FFR, which can help physicians diagnose and treat coronary artery occlusion and provide significant clinical performance for patients.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

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