{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T02:16:00Z","timestamp":1790388960998,"version":"4.1.0"},"reference-count":36,"publisher":"Japanese Circulation Society","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Circ J"],"published-print":{"date-parts":[[2026,7,24]]},"DOI":"10.1253\/circj.cj-26-0020","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T22:09:23Z","timestamp":1779919763000},"page":"1025-1032","source":"Crossref","is-referenced-by-count":1,"title":["Lesion-Specific Prediction of Segmental Fractional Flow Reserve Using Deep Learning and Optical Coherence Tomography-Derived Features"],"prefix":"10.1253","volume":"90","author":[{"given":"Juyeol","family":"Eom","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong Oh","family":"Kang","sequence":"first","affiliation":[{"name":"Multimodal Imaging and Theranostic Lab, Cardiovascular Center, Korea University Guro Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyeong Soo","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woojin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Won","family":"Kim","sequence":"additional","affiliation":[{"name":"Multimodal Imaging and Theranostic Lab, Cardiovascular Center, Korea University Guro Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongki","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"330","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"1. Vrints C, Andreotti F, Koskinas KC, Rossello X, Adamo M, Ainslie J, et al. 2024 ESC Guidelines for the management of chronic coronary syndromes: Developed by the task force for the management of chronic coronary syndromes of the European Society of Cardiology (ESC) endorsed by the European Association for Cardio-Thoracic Surgery (EACTS). <i>Eur Heart J<\/i> 2024; 45: 3415\u20133537.","DOI":"10.1093\/eurheartj\/ehae177"},{"key":"2","doi-asserted-by":"crossref","unstructured":"2. Lawton JS, Tamis-Holland JE, Bangalore S, Bates ER, Beckie TM, Bischoff JM, et al. 2021 ACC\/AHA\/SCAI Guideline for coronary artery revascularization: A report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines. <i>Circulation<\/i> 2022; 145: e18\u2013e114.","DOI":"10.1161\/CIR.0000000000001060"},{"key":"3","doi-asserted-by":"crossref","unstructured":"3. Scarsini R, Fezzi S, Leone AM, De Maria GL, Pighi M, Marcoli M, et al. Functional patterns of coronary disease: Diffuse, focal, and serial lesions. <i>JACC Cardiovasc Interv<\/i> 2022; 15: 2174\u20132191.","DOI":"10.1016\/j.jcin.2022.07.015"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Lee JM, Lee SH, Shin D, Choi KH, van de Hoef TP, Kim HK, et al. Physiology-based revascularization: A new approach to plan and optimize percutaneous coronary intervention. <i>JACC Asia<\/i> 2021; 1: 14\u201336.","DOI":"10.1016\/j.jacasi.2021.03.002"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5. Shin D, Dai N, Lee SH, Choi KH, Lefieux A, Molony D, et al. Physiological distribution and local severity of coronary artery disease and outcomes after percutaneous coronary intervention. <i>JACC Cardiovasc Interv<\/i> 2021; 14: 1771\u20131785.","DOI":"10.1016\/j.jcin.2021.06.013"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. Lee SH, Shin D, Lee JM, Lefieux A, Molony D, Choi KH, et al. Automated algorithm using pre-intervention fractional flow reserve pullback curve to predict post-intervention physiological results. <i>JACC Cardiovasc Interv<\/i> 2020; 13: 2670\u20132684.","DOI":"10.1016\/j.jcin.2020.06.062"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7. Toth GG, Toth B, Johnson NP, De Vroey F, Di Serafino L, Pyxaras S, et al. Revascularization decisions in patients with stable angina and intermediate lesions: Results of the international survey on interventional strategy. <i>Circ Cardiovasc Interv<\/i> 2014; 7: 751\u2013759.","DOI":"10.1161\/CIRCINTERVENTIONS.114.001608"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8. Kogame N, Ono M, Kawashima H, Tomaniak M, Hara H, Leipsic J, et al. The impact of coronary physiology on contemporary clinical decision making. <i>JACC Cardiovasc Interv<\/i> 2020; 13: 1617\u20131638.","DOI":"10.1016\/j.jcin.2020.04.040"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Scoccia A, Tomaniak M, Neleman T, Groenland FTW, Plantes A, Daemen J. Angiography-based fractional flow reserve: State of the art. <i>Curr Cardiol Rep<\/i> 2022; 24: 667\u2013678.","DOI":"10.1007\/s11886-022-01687-4"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: Scientific basis. <i>J Am Coll Cardiol<\/i> 2013; 61: 2233\u20132241.","DOI":"10.1016\/j.jacc.2012.11.083"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Zreik M, Van Hamersvelt RW, Khalili N, Wolterink JM, Voskuil M, Viergever MA, et al. Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography. <i>IEEE Trans Med Imaging<\/i> 2019; 39: 1545\u20131557.","DOI":"10.1109\/TMI.2019.2953054"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12. Okubo M, Kawasaki M, Yagami H, Tanigaki T, Kawase Y, Matsuo H, et al. Prediction of fractional flow reserve using intravascular ultrasound. <i>EuroIntervention<\/i> 2024; 20: e1237\u2013e1247.","DOI":"10.4244\/EIJ-D-24-00010"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13. Cha JJ, Son TD, Ha J, Kim JS, Hong SJ, Ahn CM, et al. Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: A feasibility study. <i>Sci Rep<\/i> 2020; 10: 20421.","DOI":"10.1038\/s41598-020-77507-y"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14. Cha JJ, Nguyen NL, Tran C, Shin WY, Lee SG, Lee YJ, et al. Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning. <i>Front Cardiovasc Med<\/i> 2023; 10: 1082214.","DOI":"10.3389\/fcvm.2023.1082214"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Arefinia F, Aria M, Rabiei R, Hosseini A, Ghaemian A, Roshanpoor A. Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images. <i>Sci Rep<\/i> 2024; 14: 1818.","DOI":"10.1038\/s41598-024-52360-5"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Collet C, Onuma Y, Sonck J, Asano T, Vandeloo B, Kornowski R, et al. Diagnostic performance of angiography-derived fractional flow reserve: A systematic review and Bayesian meta-analysis. <i>Eur Heart J<\/i> 2018; 39: 3314\u20133321.","DOI":"10.1093\/eurheartj\/ehy445"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Sonck J, Nagumo S, Norgaard BL, Otake H, Ko B, Zhang J, et al. Clinical validation of a virtual planner for coronary interventions based on coronary CT angiography. <i>JACC Cardiovasc Imaging<\/i> 2022; 15: 1242\u20131255.","DOI":"10.1016\/j.jcmg.2022.02.003"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Jeremias A, Maehara A, Matsumura M, Shlofmitz RA, Maksoud A, Akasaka T, et al. Optical coherence tomography-based functional stenosis assessment: FUSION \u2013 a prospective multicenter trial. <i>Circ Cardiovasc Interv<\/i> 2024; 17: e013702.","DOI":"10.1161\/CIRCINTERVENTIONS.123.013702"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Hu X, Zhang J, Yang S, Jiang J, Peng X, Lu D, et al. Angiography-derived fractional flow reserve versus intravascular ultrasound to guide percutaneous coronary intervention in patients with coronary artery disease (FLAVOUR II): A multicentre, randomised, non-inferiority trial. <i>Lancet<\/i> 2025; 405: 1491\u20131504.","DOI":"10.1016\/S0140-6736(25)00504-5"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20. Song L, Xu B, Tu S, Guan C, Jin Z, Yu B, et al. 2-year outcomes of angiographic quantitative flow ratio-guided coronary interventions. <i>J Am Coll Cardiol<\/i> 2022; 80: 2089\u20132101.","DOI":"10.1016\/j.jacc.2022.09.007"},{"key":"21","doi-asserted-by":"crossref","unstructured":"21. Cho H, Lee JG, Kang SJ, Kim WJ, Choi SY, Ko J, et al. Angiography-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions. <i>J Am Heart Assoc<\/i> 2019; 8: e011685.","DOI":"10.1161\/JAHA.118.011685"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22. Song X, Adachi T, Kawase Y, Kimura T, Saito N. Efficacy of the Wolverine cutting balloon on a circumferential calcified coronary lesion: Bench test using a three-dimensional printer and computer simulation with the finite element method. <i>Cardiovasc Interv Ther<\/i> 2022; 37: 78\u201388.","DOI":"10.1007\/s12928-020-00739-2"},{"key":"23","unstructured":"23. Zamir M. The physics of coronary blood flow. Springer; 2005."},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Demer LL, Tintut Y. Vascular calcification: Pathobiology of a multifaceted disease. <i>Circulation<\/i> 2008; 117: 2938\u20132948.","DOI":"10.1161\/CIRCULATIONAHA.107.743161"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Beverly J, Budoff MJ. Use of coronary computed tomography for calcium screening of atherosclerosis. <i>Heart Int<\/i> 2020; 14: 76.","DOI":"10.17925\/HI.2020.14.2.76"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. RLearning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv: 14061078. 2014.","DOI":"10.3115\/v1\/D14-1179"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27. Cortese B, Burzotta F, Alfonso F, Pellegrini D, Trani C, Aurigemma C, et al. Role of optical coherence tomography for distal left main stem angioplasty. <i>Catheter Cardiovasc Interv<\/i> 2020; 96: 755\u2013761.","DOI":"10.1002\/ccd.28547"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28. Toutouzas K, Karanasos A, Tousoulis D. Optical coherence tomography for the detection of the vulnerable plaque. <i>Eur Cardiol Rev<\/i> 2016; 11: 90.","DOI":"10.15420\/ecr.2016:29:2"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29. Sasaki W, Ishida M, Taguchi Y, Tosaka K, Koeda Y, Kimura T, et al. Discrepancy between optical coherence tomography-based and intravascular ultrasound-based calcium scoring for heavily calcified coronary lesions. <i>Circ J<\/i> 2025; 89: 992\u2013996.","DOI":"10.1253\/circj.CJ-25-0068"},{"key":"30","doi-asserted-by":"crossref","unstructured":"30. Matsukawa N, Yamamoto MH, Mizukami T, Kondo S, Kobayashi N, Takano M, et al. Clinical impact of optical coherence tomography-guided primary percutaneous coronary intervention in ST-elevation myocardial infarction: A substudy of the TACTICS registry. <i>Circ J<\/i> 2026; 90: 284\u2013292.","DOI":"10.1253\/circj.CJ-25-0154"},{"key":"31","doi-asserted-by":"crossref","unstructured":"31. Blanco PJ, Bulant CA, M\u00fcller LO, Talou GM, Bezerra CG, Lemos PA, et al. Comparison of 1D and 3D models for the estimation of fractional flow reserve. <i>Sci Rep<\/i> 2018; 8: 17275.","DOI":"10.1038\/s41598-018-35344-0"},{"key":"32","doi-asserted-by":"crossref","unstructured":"32. Koo BK, Lee JM, Hwang D, Park S, Shiono Y, Yonetsu T, et al. Practical application of coronary physiologic assessment: Asia-pacific expert consensus document: Part 1. <i>JACC Asia<\/i> 2023; 3: 689\u2013706.","DOI":"10.1016\/j.jacasi.2023.07.003"},{"key":"33","doi-asserted-by":"crossref","unstructured":"33. Williams B, Menon M, Satran D, Hayward D, Hodges JS, Burke MN, et al. Patients with coronary artery disease not amenable to traditional revascularization: Prevalence and 3-year mortality. <i>Catheter Cardiovasc Interv<\/i> 2010; 75: 886\u2013891.","DOI":"10.1002\/ccd.22431"},{"key":"34","doi-asserted-by":"crossref","unstructured":"34. Prati F, Di Vito L, Biondi-Zoccai G, Occhipinti M, La Manna A, Tamburino C, et al. Angiography alone versus angiography plus optical coherence tomography to guide decision-making during percutaneous coronary intervention: The Centro per la Lotta contro l\u2019Infarto-Optimisation of Percutaneous Coronary Intervention (CLI-OPCI) study. <i>EuroIntervention<\/i> 2012; 8: 823\u2013829.","DOI":"10.4244\/EIJV8I7A125"},{"key":"35","doi-asserted-by":"crossref","unstructured":"35. Prati F, Romagnoli E, Burzotta F, Limbruno U, Gatto L, La Manna A, et al. Clinical impact of OCT findings during PCI: The CLI-OPCI II study. <i>JACC Cardiovasc Imaging<\/i> 2015; 8: 1297\u20131305.","DOI":"10.1016\/j.jcmg.2015.08.013"},{"key":"36","doi-asserted-by":"crossref","unstructured":"36. Maehara A, Matsumura M, Ali ZA, Mintz GS, Stone GW. IVUS-guided versus OCT-guided coronary stent implantation: A critical appraisal. <i>JACC Cardiovasc Imaging<\/i> 2017; 10: 1487\u20131503.","DOI":"10.1016\/j.jcmg.2017.09.008"}],"container-title":["Circulation Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/circj\/90\/8\/90_CJ-26-0020\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T03:27:42Z","timestamp":1784950062000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/circj\/90\/8\/90_CJ-26-0020\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,24]]},"references-count":36,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.1253\/circj.cj-26-0020","relation":{},"ISSN":["1346-9843","1347-4820"],"issn-type":[{"value":"1346-9843","type":"print"},{"value":"1347-4820","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,24]]},"article-number":"CJ-26-0020"}}