Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review
This systemic review demonstrated the favourable diagnostic performance of ML-FFR<sub>CT</sub> against standard iFFR, although heterogeneity exists, providing support for the use of ML-FFR<sub>CT</sub> as a triage tool for non-invasive screening of coronary ischemia in the clinical setting.
Authors
- Min Sen Yew (ORCID: https://orcid.org/0000-0002-1491-5864)
- Ru‐San Tan (ORCID: https://orcid.org/0000-0003-2086-6517)
- Danny van Noort (ORCID: https://orcid.org/0000-0002-0818-0443)
- Lohendran Baskaran (ORCID: https://orcid.org/0000-0003-0565-7151)
- Lynette Teo (ORCID: https://orcid.org/0000-0002-2817-9458)
- Swee Yaw Tan (ORCID: https://orcid.org/0000-0002-2780-3247)
- Liang Zhong (ORCID: https://orcid.org/0000-0003-3608-4173)
- Luming Shi (ORCID: https://orcid.org/0000-0001-6828-5856)
- Mark Y. Chan (ORCID: https://orcid.org/0000-0003-0389-7450)
- Ping Chai (ORCID: https://orcid.org/0000-0003-1134-6667)
- Felix Keng
- Shuang Leng
- Terrance Chua
- Liang Guo (ORCID: https://orcid.org/0000-0001-6520-6331)
Institutions
- Tan Tock Seng Hospital (SG)
- Duke-NUS Medical School (SG)
- National University Hospital (SG)
- National Heart Centre Singapore (SG)
- Singapore Clinical Research Institute (SG)
Publication Details
- Journal
- IJC Heart & Vasculature
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1016/j.ijcha.2024.101528
- Citations
- 4
- Primary Topic
- Coronary Interventions and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 2.29
Funders
- Medical Research Council
- National Medical Research Council