ArticleFrontiers in cardiovascular medicine2023
Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling.Cardiovascular engineering and technology · 2026Article
- Review of Optical Imaging in Coronary Artery Disease Diagnosis.Journal of cardiovascular development and disease · 2025Review
- Artificial Intelligence based fractional flow reserve.Cardiology journal · 2025Review
- Diagnostic performance of the quantitative flow ratio and CT-FFR for coronary lesion-specific ischemia.Scientific reports · 2024Article
- Diagnostic Performance of Intracoronary Optical Coherence Tomography-Modulated Quantitative Flow Ratio for Assessing Coronary Stenosis.Journal of the Society for Cardiovascular Angiography & InterventionsArticle
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aimed to evaluate and compare the diagnostic accuracy of machine learning (ML)- fractional flow reserve (FFR) based on optical coherence tomography (OCT) with wire-based FFR irrespective of the coronary territory. Background: ML techniques for assessing hemodynamics features including FFR in coronary artery disease have been developed based on various imaging modalities. However, there is no study using OCT-based ML models for all coronary artery territories. Methods: OCT and FFR data were obtained for 356 individual coronary lesions in 130 patients. The training and testing groups were divided in a ratio of 4:1. The ML-FFR was derived for the testing group and compared with the wire-based FFR in terms of the diagnosis of ischemia (FFR ≤ 0.80). Results: The mean age of the subjects was 62.6 years. The numbers of the left anterior descending, left circumflex, and right coronary arteries were 130 (36.5%), 110 (30.9%), and 116 (32.6%), respectively. Using seven major features, the ML-FFR showed strong correlation ( Conclusion: OCT-based ML-FFR showed good diagnostic performance in predicting FFR irrespective of the coronary territory. Because the study was a small-size study, the results should be warranted the performance in further large-scale research.
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