Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques
Abstract Background Fractional 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, h...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BMC Cardiovascular Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12872-023-03447-w |
| _version_ | 1797578313263218688 |
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| author | Arefinia Farhad Rabiei Reza Hosseini Azamossadat Ghaemian Ali Roshanpoor Arash Aria Mehrad Khorrami Zahra |
| author_facet | Arefinia Farhad Rabiei Reza Hosseini Azamossadat Ghaemian Ali Roshanpoor Arash Aria Mehrad Khorrami Zahra |
| author_sort | Arefinia Farhad |
| collection | DOAJ |
| description | Abstract Background Fractional 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. Objective This 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. Methods The 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. Results Five 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. Conclusion This 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. |
| first_indexed | 2024-03-10T22:20:09Z |
| format | Article |
| id | doaj.art-3a2cba9346e648b79404d8f38271a40f |
| institution | Directory Open Access Journal |
| issn | 1471-2261 |
| language | English |
| last_indexed | 2024-03-10T22:20:09Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cardiovascular Disorders |
| spelling | doaj.art-3a2cba9346e648b79404d8f38271a40f2023-11-19T12:18:21ZengBMCBMC Cardiovascular Disorders1471-22612023-08-0123111010.1186/s12872-023-03447-wArtificial intelligence in estimating fractional flow reserve: a systematic literature review of techniquesArefinia Farhad0Rabiei Reza1Hosseini Azamossadat2Ghaemian Ali3Roshanpoor Arash4Aria Mehrad5Khorrami Zahra6Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical SciencesDepartment of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical SciencesDepartment of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical SciencesCardiovascular Research Center, Mazandaran University of Medical SciencesDepartment of Computer Science, Sama Technical and Vocational Training College, Tehran Branch (Tehran), Islamic Azad University (IAU)Department of Information Technology and Computer Engineering and Ophthalmic Epidemiology Research Center, Azarbaijan Shahid Madani UniversityResearch Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical SciencesAbstract Background Fractional 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. Objective This 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. Methods The 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. Results Five 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. Conclusion This 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.https://doi.org/10.1186/s12872-023-03447-wMachine learningFractional Flow ReverseFunctional evaluation |
| spellingShingle | Arefinia Farhad Rabiei Reza Hosseini Azamossadat Ghaemian Ali Roshanpoor Arash Aria Mehrad Khorrami Zahra Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques BMC Cardiovascular Disorders Machine learning Fractional Flow Reverse Functional evaluation |
| title | Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques |
| title_full | Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques |
| title_fullStr | Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques |
| title_full_unstemmed | Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques |
| title_short | Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques |
| title_sort | artificial intelligence in estimating fractional flow reserve a systematic literature review of techniques |
| topic | Machine learning Fractional Flow Reverse Functional evaluation |
| url | https://doi.org/10.1186/s12872-023-03447-w |
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