Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning - Cha, Jung-Joon; Nguyen, Ngoc-Luu; Tran, Cong; Shin, Won-Yong; Lee, Seul-Gee; Lee, Yong-Joon; Lee, Seung-Jun; Hong, Sung-Jin; Ahn, Chul-Min; Kim, Byeong-Keuk; Ko, Young-Guk; Choi, Donghoon; et al. (Frontiers in Cardiovascular Medicine, 2023)
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 (r=0.8782, P <0.001) with the wire-based FFR. The ML-FFR predicted wire-based FFR ≤ 0.80 in the test set with sensitivity of 98.3%, specificity of 61.5%, and overall accuracy of 91.7% (area under the curve: 0.948). External validation showed good correlation (r=0.7884, P<0.001) and accuracy of 83.2% (area under the curve: 0.912). Conclusions: 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.