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Artificial Intelligence in Intracoronary Imaging

  • Cardiac PET, CT, and MRI (P Schoenhagen and P-H Chen, Section Editors)
  • Published:
Current Cardiology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

This paper investigates present uses and future potential of artificial intelligence (AI) applied to intracoronary imaging technologies.

Recent Findings

Advances in data analytics and digitized medical imaging have enabled clinical application of AI to improve patient outcomes and reduce costs through better diagnosis and enhanced workflow. Applications of AI to IVUS and IVOCT have produced improvements in image segmentation, plaque analysis, and stent evaluation. Machine learning algorithms are able to predict future coronary events through the use of imaging results, clinical evaluations, laboratory tests, and demographics.

Summary

The application of AI to intracoronary imaging holds significant promise for improved understanding and treatment of coronary heart disease. Even in these early stages, AI has demonstrated the ability to improve the prediction of cardiac events. Large curated data sets and databases are needed to speed the development of AI and enable testing and comparison among algorithms.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Hanekamp C, Koolen J, Pijls N, Michels H, Bonnier H. Comparison of quantitative coronary angiography, intravascular ultrasound, and coronary pressure measurement to assess optimum stent deployment. Circulation. 1999;99:1015–21.

    CAS  PubMed  Google Scholar 

  2. Kim S-W, Mintz GS, Ohlmann P, Hassani S-E, Fernandez S, Lu L, et al. Frequency and severity of plaque prolapse within cypher and taxus stents as determined by sequential intravascular ultrasound analysis. Am J Cardiol. 2006;98:1206.

    PubMed  Google Scholar 

  3. Cook S, Wenaweser P, Togni M, Billinger M, Morger C, Seiler C, et al. Incomplete stent apposition and very late stent thrombosis after drug-eluting stent implantation. Circulation. 2007;115:2426–34. https://doi.org/10.1161/circulationaha.106.658237.

    Article  CAS  PubMed  Google Scholar 

  4. Mehta SK, McCrary JR, Frutkin AD, Dolla WJS, Marso SP. Intravascular ultrasound radiofrequency analysis of coronary atherosclerosis: an emerging technology for the assessment of vulnerable plaque. Eur Heart J. 2007;28:1283–8. https://doi.org/10.1093/eurheartj/ehm112.

    Article  PubMed  Google Scholar 

  5. Siqueira DA, Abizaid AA, Costa Jd R, Feres F, Mattos LA, Staico R, et al. Late incomplete apposition after drug-eluting stent implantation: incidence and potential for adverse clinical outcomes. Eur Heart J. 2007;28:1304–9. https://doi.org/10.1093/eurheartj/ehm114.

    Article  PubMed  Google Scholar 

  6. Feres F, Costa JR, Abizaid A. Very late thrombosis after drug-eluting stents. Catheter Cardiovasc Interv. 2006;68:83–8.

    PubMed  Google Scholar 

  7. Bouma BE, Tearney GJ, Yabushita H, Shishkov M, Kauffman CR, DeJoseph Gauthier D, et al. Evaluation of intracoronary stenting by intravascular optical coherence tomography. Heart. 2003;89:317–20. https://doi.org/10.1136/heart.89.3.317.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Pinto TL, Waksman R. Clinical applications of optical coherence tomography. J Interv Cardiol. 2006;19:566.

    PubMed  Google Scholar 

  9. Jeremias A, Sylvia B, Bridges J, Kirtane AJ, Bigelow B, Pinto DS, et al. Stent thrombosis after successful sirolimus-eluting stent implantation. Circulation. 2004;109:1930–2. https://doi.org/10.1161/01.cir.0000127105.99982.21.

    Article  PubMed  Google Scholar 

  10. Guagliumi G, Sirbu V. Optical coherence tomography: high resolution intravascular imaging to evaluate vascular healing after coronary stenting. Catheter Cardiovasc Interv. 2008;72:237–47.

    PubMed  Google Scholar 

  11. Hong SJ, Kim BK, Shin DH, Nam CM, Kim JS, Ko YG, et al. Effect of intravascular ultrasound-guided vs angiography-guided everolimus-eluting stent implantation: the IVUS-XPL randomized clinical trial. JAMA. 2015;314:2155–63. https://doi.org/10.1001/jama.2015.15454.

    Article  CAS  PubMed  Google Scholar 

  12. Kataoka Y, Puri R, Andrews J, Honda S, Nishihira K, Asaumi Y, et al. In vivo visualization of lipid coronary atheroma with intravascular near-infrared spectroscopy. Expert Rev Cardiovasc Ther. 2017;15:775–85. https://doi.org/10.1080/14779072.2017.1367287.

    Article  CAS  PubMed  Google Scholar 

  13. Stone GW, Maehara A, Lansky AJ, de Bruyne B, Cristea E, Mintz GS, et al. A prospective natural-history study of coronary atherosclerosis. N Engl J Med. 2011;364:226–35. https://doi.org/10.1056/NEJMoa1002358.

    Article  CAS  PubMed  Google Scholar 

  14. Wu B, Abbott T, Fishman D, McMurray W, Mor G, Stone K, et al. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics (Oxford, England). 2003;19:1636–43. https://doi.org/10.1093/bioinformatics/btg210.

    Article  CAS  Google Scholar 

  15. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographsaccuracy of a deep learning algorithm for detection of diabetic retinopathyaccuracy of a deep learning algorithm for detection of diabetic retinopathy. JAMA. 2016;316:2402–10. https://doi.org/10.1001/jama.2016.17216.

    Article  PubMed  Google Scholar 

  16. Berner ES, Ozaydin B. Benefits and risks of machine learning decision support systems benefits and risks of machine learning decision support systems letters. JAMA. 2017;318:2353–4. https://doi.org/10.1001/jama.2017.16619.

    Article  PubMed  Google Scholar 

  17. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–8. https://doi.org/10.1038/nature21056.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhu L, Zheng WJ. Informatics, data science, and artificial intelligence informatics, data science, and artificial intelligence informatics, data science, and artificial intelligence. JAMA. 2018;320:1103–4. https://doi.org/10.1001/jama.2018.8211.

    Article  PubMed  Google Scholar 

  19. Naylor CD. On the prospects for a (deep) learning health care system prospects for a deep learning health care system prospects for a deep learning health care system. JAMA. 2018;320:1099–100. https://doi.org/10.1001/jama.2018.11103.

    Article  PubMed  Google Scholar 

  20. Hinton G. Deep learning—a technology with the potential to transform health carethe potential of deep learning technology to transform health care the potential of deep learning technology to transform health care. JAMA. 2018;320:1101–2. https://doi.org/10.1001/jama.2018.11100.

    Article  PubMed  Google Scholar 

  21. Carin L, Pencina MJ. On deep learning for medical image analysis on deep learning for medical image analysis on deep learning for medical image analysis. JAMA. 2018;320:1192–3. https://doi.org/10.1001/jama.2018.13316.

    Article  PubMed  Google Scholar 

  22. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data potential biases in machine learning algorithms using electronic health record data potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018;178:1544–7. https://doi.org/10.1001/jamainternmed.2018.3763.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hatt M, Parmar C, Qi J, Naqa IE. Machine (deep) learning methods for image processing and radiomics. IEEE Transactions on Radiation and Plasma Medical Sciences. 2019;3:104–8. https://doi.org/10.1109/TRPMS.2019.2899538.

    Article  Google Scholar 

  24. Yan J, Yang X, Sun X, Chen Z, Liu H. A lightweight ultrasound probe for wearable human–machine interfaces. IEEE Sensors J. 2019;19:5895–903. https://doi.org/10.1109/JSEN.2019.2905243.

    Article  Google Scholar 

  25. Tweedy L, Witzel P, Heinrich D, Insall RH, Endres RG. Screening by changes in stereotypical behavior during cell motility. Sci Rep. 2019;9:8784. https://doi.org/10.1038/s41598-019-45305-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lou B, Doken S, Zhuang T, Wingerter D, Gidwani M, Mistry N, et al. An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction. The Lancet Digital Health. 2019;1:e136–47. https://doi.org/10.1016/S2589-7500(19)30058-5.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Eraslan G, Avsec Z, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019;20:389–403. https://doi.org/10.1038/s41576-019-0122-6.

    Article  CAS  PubMed  Google Scholar 

  28. Mintz GS, Nissen SE, Anderson WD, Bailey SR, Erbel R, Fitzgerald PJ, et al. American College of Cardiology clinical expert consensus document on standards for acquisition, measurement and reporting of intravascular ultrasound studies (ivus): a report of the american college of cardiology task force on clinical expert consensus documents developed in collaboration with the european society of cardiology endorsed by the society of cardiac angiography and interventions. J Am Coll Cardiol. 2001;37:1478–92.

    CAS  PubMed  Google Scholar 

  29. Nair A, Kuban BD, Obuchowski N, Vince DG. Assessing spectral algorithms to predict atherosclerotic plaque composition with normalized and raw intravascular ultrasound data. Ultrasound Med Biol. 2001;27:1319–31.

    CAS  PubMed  Google Scholar 

  30. • Nair A, Kuban BD, Tuzcu EM, Schoenhagen P, Nissen SE, Vince DG. Coronary plaque classification with intravascular ultrasound radiofrequency data analysis. Circulation. 2002;106:2200–6. https://doi.org/10.1161/01.cir.0000035654.18341.5e. Successful clinical implementation of AI for intra-coronary imaging which added previously unavailable information and reduced subjectivity in image interpretation.

    Article  PubMed  Google Scholar 

  31. Nair A, Margolis MP, Kuban BD, Vince DG. Automated coronary plaque characterisation with intravascular ultrasound backscatter: ex vivo validation. EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology. 2007a;3:113.

    Google Scholar 

  32. Nissen SE. Atherosclerosis in 2010: new therapeutic insights. Nat Rev Cardiol. 8:70–2.

  33. Nicholls SJ, Uno K, Tuzcu EM, Nissen SE. Lessons from coronary intravascular ultrasound on the importance of raising high-density lipoprotein cholesterol. Curr Atheroscler Rep. 12:301–7. https://doi.org/10.1007/s11883-010-0125-4.

  34. Lavoie AJ, Bayturan O, Uno K, Hsu A, Wolski K, Schoenhagen P, et al. Plaque progression in coronary arteries with minimal luminal obstruction in intravascular ultrasound atherosclerosis trials. Am J Cardiol. 105:1679–83. https://doi.org/10.1016/j.amjcard.2010.01.345.

  35. Nicholls SJ, Hsu A, Wolski K, Hu B, Bayturan O, Lavoie A, et al. Intravascular ultrasound-derived measures of coronary atherosclerotic plaque burden and clinical outcome. J Am Coll Cardiol. 55:2399–407. https://doi.org/10.1016/j.jacc.2010.02.026.

  36. Nissen SE, Nicholls SJ, Sipahi I, Libby P, Raichlen JS, Ballantyne CM, et al. Effect of very high-intensity statin therapy on regression of coronary atherosclerosis. JAMA. 2006;295:1556–65. https://doi.org/10.1001/jama.295.13.jpc60002.

    Article  CAS  PubMed  Google Scholar 

  37. Nissen SE, Tuzcu EM, Schoenhagen P, Brown BG, Ganz P, Vogel RA, et al. Effect of intensive compared with moderate lipid-lowering therapy on progression of coronary atherosclerosis. JAMA. 2004;291:1071–80. https://doi.org/10.1001/jama.291.9.1071.

    Article  CAS  PubMed  Google Scholar 

  38. Andrews J, Puri R, Kataoka Y, Nicholls SJ, Psaltis PJ. Therapeutic modulation of the natural history of coronary atherosclerosis: lessons learned from serial imaging studies. Cardiovasc Diagn Ther. 2016;6:282–303. https://doi.org/10.21037/cdt.2015.10.02.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ako J, Morino Y, Honda Y, Hassan A, Sonoda S, Yock PG, et al. Late incomplete stent apposition after sirolimus-eluting stent implantation: a serial intravascular ultrasound analysis. J Am Coll Cardiol. 2005;46:1002–5. https://doi.org/10.1016/j.jacc.2005.05.068.

    Article  CAS  PubMed  Google Scholar 

  40. Mintz GS, Shah VM, Weissman NJ. Regional remodeling as the cause of late stent malapposition. Circulation. 2003;107:2660–3. https://doi.org/10.1161/01.cir.0000074778.46065.24.

    Article  PubMed  Google Scholar 

  41. Hong M-K, Mintz GS, Lee CW, Kim Y-H, Lee S-W, Song J-M, et al. Incidence, mechanism, predictors, and long-term prognosis of late stent malapposition after bare-metal stent implantation. Circulation. 2004;109:881–6. https://doi.org/10.1161/01.cir.0000116751.88818.10.

    Article  PubMed  Google Scholar 

  42. Costa MA, Angiolillo DJ, Tannenbaum M, Driesman M, Chu A, Patterson J, et al. Impact of Stent Deployment Procedural Factors on Long-Term Effectiveness and Safety of Sirolimus-Eluting Stents (Final Results of the Multicenter Prospective STLLR Trial). Am J Cardiol. 2008;101:1704–11.

    CAS  PubMed  Google Scholar 

  43. Matsumoto D, Shite J, Shinke T, Otake H, Tanino Y, Ogasawara D, et al. Neointimal coverage of sirolimus-eluting stents at 6-month follow-up: evaluated by optical coherence tomography. Eur Heart J. 2007;28:961–7. https://doi.org/10.1093/eurheartj/ehl413.

    Article  CAS  PubMed  Google Scholar 

  44. Kawase Y, Hoshino K, Yoneyama R, McGregor J, Hajjar RJ, Jang I-K, et al. In vivo volumetric analysis of coronary stent using optical coherence tomography with a novel balloon occlusion-flushing catheter: a comparison with intravascular ultrasound. Ultrasound Med Biol. 2005;31:1343.

    PubMed  Google Scholar 

  45. Kume T, Akasaka T, Kawamoto T, Watanabe N, Toyota E, Neishi Y, et al. Assessment of coronary arterial plaque by optical coherence tomography. Am J Cardiol. 2006;97:1172.

    PubMed  Google Scholar 

  46. Madjid M, Zarrabi A, Litovsky S, Willerson JT, Casscells W. Finding vulnerable atherosclerotic plaques: is it worth the effort? Arterioscler Thromb Vasc Biol. 2004;24:1775–82. https://doi.org/10.1161/01.atv.0000142373.72662.20.

    Article  CAS  PubMed  Google Scholar 

  47. Stamper D, Weissman NJ, Brezinski M. Plaque characterization with optical coherence tomography. J Am Coll Cardiol. 2006;47:69–79.

    Google Scholar 

  48. Puri R, Worthley MI, Nicholls SJ. Intravascular imaging of vulnerable coronary plaque: current and future concepts. Nat Rev Cardiol. 2011;8:131–9.

    PubMed  Google Scholar 

  49. Oemrawsingh RM, Garcia-Garcia HM, van Geuns RJ, Lenzen MJ, Simsek C, de Boer SP, et al. Integrated Biomarker and Imaging Study 3 (IBIS-3) to assess the ability of rosuvastatin to decrease necrotic core in coronary arteries. EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology. 2016;12:734–9. https://doi.org/10.4244/eijv12i6a118.

    Article  Google Scholar 

  50. Jaguszewski M, Klingenberg R, Landmesser U. Intracoronary near-infrared spectroscopy (NIRS) imaging for detection of lipid content of coronary plaques: current experience and future perspectives. Curr Cardiovasc Imaging Rep. 2013;6:426–30. https://doi.org/10.1007/s12410-013-9224-2.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Michail M, Serruys PW, Stettler R, Crake T, Torii R, Tenekecioglu E, et al. Intravascular multimodality imaging: feasibility and role in the evaluation of coronary plaque pathology. Eur Heart J Cardiovasc Imaging. 2017;18:613–20. https://doi.org/10.1093/ehjci/jew330.

    Article  PubMed  Google Scholar 

  52. Fard AM, Vacas-Jacques P, Hamidi E, Wang H, Carruth RW, Gardecki JA, et al. Optical coherence tomography--near infrared spectroscopy system and catheter for intravascular imaging. Opt Express. 2013;21:30849–58. https://doi.org/10.1364/oe.21.030849.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Kanwar SS, Stone GW, Singh M, Virmani R, Olin J, Akasaka T, et al. Acute coronary syndromes without coronary plaque rupture. Nat Rev Cardiol. 2016;13:257–65. https://doi.org/10.1038/nrcardio.2016.19.

    Article  PubMed  Google Scholar 

  54. Libby P, Pasterkamp G, Crea F, Jang I-K. Reassessing the mechanisms of acute coronary syndromes. Circ Res. 2019;124:150–60. https://doi.org/10.1161/CIRCRESAHA.118.311098.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ahmadi A, Stone GW, Leipsic J, Shaw LJ, Villines TC, Kern MJ, et al. Prognostic determinants of coronary atherosclerosis in stable ischemic heart disease: anatomy, physiology, or morphology? Circ Res. 2016;119:317–29. https://doi.org/10.1161/CIRCRESAHA.116.308952.

    Article  CAS  PubMed  Google Scholar 

  56. • Partida RA, Libby P, Crea F, Jang IK. Plaque erosion: a new in vivo diagnosis and a potential major shift in the management of patients with acute coronary syndromes. Eur Heart J. 2018;39:2070–6. https://doi.org/10.1093/eurheartj/ehx786. Review of plaque rupture and plaque erosion suggesting that plaque erosion should be treated pharmacologically rather than mechanically, highlighting the need for IC imaging to determine rupture vs erosion.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Burke A, Virmani R. Pathophysiology of acute myocardial infarction. Med Clin North Am. 2007;91:553–72.

    PubMed  Google Scholar 

  58. Burke A, Virmani R, Galis Z, Haudenschild C, Muller J. Task force #2--what is the pathologic basis for new atherosclerosis imaging techniques? J Am Coll Cardiol. 2003;41:1874–86.

    PubMed  Google Scholar 

  59. Virmani R, Burke AP, Farb A, Kolodgie FD. Pathology of the vulnerable plaque. J Am Coll Cardiol. 2006;47:C13–8. https://doi.org/10.1016/j.jacc.2005.10.065.

    Article  CAS  PubMed  Google Scholar 

  60. Narula J, Nakano M, Virmani R, Kolodgie FD, Petersen R, Newcomb R, et al. Histopathologic characteristics of atherosclerotic coronary disease and implications of the findings for the invasive and noninvasive detection of vulnerable plaques. J Am Coll Cardiol. 2013;61:1041–51. https://doi.org/10.1016/j.jacc.2012.10.054.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Otsuka F, Joner M, Prati F, Virmani R, Narula J. Clinical classification of plaque morphology in coronary disease. Nat Rev Cardiol. 2014;11:379–89. https://doi.org/10.1038/nrcardio.2014.62.

    Article  PubMed  Google Scholar 

  62. Hellings WE, Peeters W, Moll FL, Piers SR, van Setten J, Van der Spek PJ, et al. Composition of carotid atherosclerotic plaque is associated with cardiovascular outcome: a prognostic study. Circulation. 2010;121:1941–50. https://doi.org/10.1161/CIRCULATIONAHA.109.887497.

    Article  PubMed  Google Scholar 

  63. Ritman EL, Lerman A. The dynamic vasa vasorum. Cardiovasc Res. 2007;75:649–58. https://doi.org/10.1016/j.cardiores.2007.06.020.

    Article  CAS  PubMed  Google Scholar 

  64. Kolodgie FD, Gold HK, Burke AP, Fowler DR, Kruth HS, Weber DK, et al. Intraplaque hemorrhage and progression of coronary atheroma. N Engl J Med. 2003;349:2316–25. https://doi.org/10.1056/NEJMoa035655.

    Article  CAS  PubMed  Google Scholar 

  65. Moreno PR, Purushothaman KR, Fuster V, Echeverri D, Truszczynska H, Sharma SK, et al. Plaque neovascularization is increased in ruptured atherosclerotic lesions of human aorta: implications for plaque vulnerability. Circulation. 2004;110:2032–8. https://doi.org/10.1161/01.cir.0000143233.87854.23.

    Article  PubMed  Google Scholar 

  66. Moreno PR, Purushothaman KR, Fuster V, O'Connor WN. Intimomedial interface damage and adventitial inflammation is increased beneath disrupted atherosclerosis in the aorta: implications for plaque vulnerability. Circulation. 2002;105:2504–11.

    PubMed  Google Scholar 

  67. Mulligan-Kehoe MJ, Simons M. Vasa vasorum in normal and diseased arteries. Circulation. 2014;129:2557–66. https://doi.org/10.1161/CIRCULATIONAHA.113.007189.

    Article  PubMed  Google Scholar 

  68. Choi BJ, Matsuo Y, Aoki T, Kwon TG, Prasad A, Gulati R, et al. Coronary endothelial dysfunction is associated with inflammation and vasa vasorum proliferation in patients with early atherosclerosis. Arterioscler Thromb Vasc Biol. 2014;34:2473–7. https://doi.org/10.1161/ATVBAHA.114.304445.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. ten Kate GL, Sijbrands EJ, Valkema R, ten Cate FJ, Feinstein SB, van der Steen AF, et al. Molecular imaging of inflammation and intraplaque vasa vasorum: a step forward to identification of vulnerable plaques? J Nucl Cardiol. 2010;17:897–912. https://doi.org/10.1007/s12350-010-9263-x.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Burke AP, Farb A, Malcom GT, Liang Y, Smialek JE, Virmani R. PLaque rupture and sudden death related to exertion in men with coronary artery disease. JAMA. 1999;281:921–6. https://doi.org/10.1001/jama.281.10.921.

    Article  CAS  PubMed  Google Scholar 

  71. Fleiner M, Kummer M, Mirlacher M, Sauter G, Cathomas G, Krapf R, et al. Arterial neovascularization and inflammation in vulnerable patients: early and late signs of symptomatic atherosclerosis. Circulation. 2004;110:2843–50. https://doi.org/10.1161/01.cir.0000146787.16297.e8.

    Article  PubMed  Google Scholar 

  72. Virmani R, Kolodgie FD, Burke AP, Finn AV, Gold HK, Tulenko TN, et al. Atherosclerotic plaque progression and vulnerability to rupture: angiogenesis as a source of intraplaque hemorrhage. Arterioscler Thromb Vasc Biol. 2005;25:2054–61. https://doi.org/10.1161/01.atv.0000178991.71605.18.

    Article  CAS  PubMed  Google Scholar 

  73. Saia F, Komukai K, Capodanno D, Sirbu V, Musumeci G, Boccuzzi G, et al. Eroded versus ruptured plaques at the culprit site of STEMI: in vivo pathophysiological features and response to primary PCI. J Am Coll Cardiol Img. 2015;8:566–75. https://doi.org/10.1016/j.jcmg.2015.01.018.

    Article  Google Scholar 

  74. Arbustini E, Narula N, Kodama T. Clinical imaging of ACS with ruptured or intact fibrous caps. J Am Coll Cardiol Img. 2015;8:576–8. https://doi.org/10.1016/j.jcmg.2015.03.004.

    Article  Google Scholar 

  75. Sugiyama T, Yamamoto E, Bryniarski K, Xing L, Lee H, Isobe M, et al. Nonculprit plaque characteristics in patients with acute coronary syndrome caused by plaque erosion vs plaque rupture: a 3-vessel optical coherence tomography study. JAMA Cardiol. 2018;3:207–14. https://doi.org/10.1001/jamacardio.2017.5234.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Niccoli G, Montone RA, Di Vito L, Gramegna M, Refaat H, Scalone G, et al. Plaque rupture and intact fibrous cap assessed by optical coherence tomography portend different outcomes in patients with acute coronary syndrome. Eur Heart J. 2015;36:1377–84. https://doi.org/10.1093/eurheartj/ehv029.

    Article  PubMed  Google Scholar 

  77. Hu S, Zhu Y, Zhang Y, Dai J, Li L, Dauerman H, et al. Management and outcome of patients with acute coronary syndrome caused by plaque rupture versus plaque erosion: an intravascular optical coherence tomography study. J Am Heart Assoc. 2017;6. https://doi.org/10.1161/jaha.116.004730.

  78. Yonetsu T, Lee T, Murai T, Suzuki M, Matsumura A, Hashimoto Y, et al. Plaque morphologies and the clinical prognosis of acute coronary syndrome caused by lesions with intact fibrous cap diagnosed by optical coherence tomography. Int J Cardiol. 2016;203:766–74. https://doi.org/10.1016/j.ijcard.2015.11.030.

    Article  PubMed  Google Scholar 

  79. Witzenbichler B, Maehara A, Weisz G, Neumann FJ, Rinaldi MJ, Metzger DC, et al. Relationship between intravascular ultrasound guidance and clinical outcomes after drug-eluting stents: the assessment of dual antiplatelet therapy with drug-eluting stents (ADAPT-DES) study. Circulation. 2014;129:463–70. https://doi.org/10.1161/CIRCULATIONAHA.113.003942.

    Article  CAS  PubMed  Google Scholar 

  80. Jang JS, Song YJ, Kang W, Jin HY, Seo JS, Yang TH, et al. Intravascular ultrasound-guided implantation of drug-eluting stents to improve outcome: a meta-analysis. JACC. Cardiovascular Interventions. 2014;7:233–43. https://doi.org/10.1016/j.jcin.2013.09.013.

    Article  PubMed  Google Scholar 

  81. Maehara A, Ben-Yehuda O, Ali Z, Wijns W, Bezerra HG, Shite J, et al. Comparison of stent expansion guided by optical coherence tomography versus intravascular ultrasound: the ILUMIEN II study (observational study of optical coherence tomography [OCT] in patients undergoing fractional flow reserve [FFR] and percutaneous coronary intervention). JACC Cardiovascular interventions. 2015;8:1704–14. https://doi.org/10.1016/j.jcin.2015.07.024.

    Article  PubMed  Google Scholar 

  82. Maehara A, Mintz GS, Witzenbichler B, Weisz G, Neumann FJ, Rinaldi MJ, et al. Relationship between intravascular ultrasound guidance and clinical outcomes after drug-eluting stents. Circ Cardiovasc Interv. 2018;11:e006243. https://doi.org/10.1161/circinterventions.117.006243.

    Article  PubMed  Google Scholar 

  83. Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart. 2018;104:1156–64. https://doi.org/10.1136/heartjnl-2017-311198.

    Article  PubMed  Google Scholar 

  84. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255–60. https://doi.org/10.1126/science.aaa8415.

    Article  CAS  PubMed  Google Scholar 

  85. Zhang Z, Sejdic E. Radiological images and machine learning: trends, perspectives, and prospects. Comput Biol Med. 2019;108:354–70. https://doi.org/10.1016/j.compbiomed.2019.02.017.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Kawasaki M, Bouma BE, Bressner J, Houser SL, Nadkarni SK, MacNeill BD, et al. Diagnostic accuracy of optical coherence tomography and integrated backscatter intravascular ultrasound images for tissue characterization of human coronary plaques. J Am Coll Cardiol. 2006;48:81–8.

    PubMed  Google Scholar 

  87. Kawasaki M, Takatsu H, Noda T, Sano K, Ito Y, Hayakawa K, et al. In vivo quantitative tissue characterization of human coronary arterial plaques by use of integrated backscatter intravascular ultrasound and comparison with angioscopic findings. Circulation. 2002;105:2487–92.

    PubMed  Google Scholar 

  88. Nasu K, Tsuchikane E, Katoh O, Vince DG, Virmani R, Surmely J-F, et al. Accuracy of in vivo coronary plaque morphology assessment: a validation study of in vivo virtual histology compared with in vitro histopathology. J Am Coll Cardiol. 2006;47:2405–12.

    PubMed  Google Scholar 

  89. Okubo M, Kawasaki M, Ishihara Y, Takeyama U, Kubota T, Yamaki T, et al. Development of integrated backscatter intravascular ultrasound for tissue characterization of coronary plaques. Ultrasound Med Biol. 2008;34:655–63.

    PubMed  Google Scholar 

  90. Sathyanarayana S, Carlier S, Li W, Thomas L, et al. Characterisation of atherosclerotic plaque by spectral similarity of radiofrequency intravascular ultrasound signals. EuroIntervention : Journal of EuroPCR in Collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology. 2009;5:133–9.

    Google Scholar 

  91. Thim T, Hagensen MK, Wallace-Bradley D, Granada JF, Kaluza GL, Drouet L, et al. Unreliable assessment of necrotic core by VHTM IVUS in porcine coronary artery disease. Circulation: Cardiovascular Imaging. 2010:CIRCIMAGING-109.

  92. Van JH, De GM, Ennekens G, Van PH, Herman A, Vrints C. Validation of in vivo plaque characterisation by virtual histology in a rabbit model of atherosclerosis. EuroIntervention: Journal of EuroPCR in Collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology. 2009;5:149–56.

    Google Scholar 

  93. Athanasiou LS, Karvelis PS, Tsakanikas VD, Naka KK, Michalis LK, Bourantas CV, et al. A novel semiautomated atherosclerotic plaque characterization method using grayscale intravascular ultrasound images: comparison with virtual histology. IEEE Trans Inf Technol Biomed. 2012;16:391–400.

    PubMed  Google Scholar 

  94. Brunenberg E, Pujol O, ter Haar Romeny B, Radeva P. Automatic IVUS segmentation of atherosclerotic plaque with stop & go snake,. 2006, pp. 9-16.

  95. Caballero KL, Barajas J, Pujol O, Salvatella N, Radeva P. In-vivo ivus tissue classification: a comparison between rf signal analysis and reconstructed images. 2006, pp. 137-146.

  96. V. G. Giannoglou, D. G. Stavrakoudis, and J. B. Theocharis, "IVUS-based characterization of atherosclerotic plaques using feature selection and SVM classification," 2012, pp. 715-720.

  97. Giannoglou VG, Stavrakoudis DG, Theocharis JB, Petridis V. Genetic fuzzy rule based classification systems for coronary plaque characterization based on intravascular ultrasound images. Eng Appl Artif Intell. 2015;38:203–20.

    Google Scholar 

  98. Hwang YN, Lee JH, Kim GY, Shin ES, Kim SM. Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier. Comput Methods Prog Biomed. 2018;153:83–92.

    Google Scholar 

  99. Kim GY, Lee JH, Hwang YN, Kim SM. A novel intensity-based multi-level classification approach for coronary plaque characterization in intravascular ultrasound images. Biomed Eng Online, in Press. 2018.

  100. O. Pujol, P. Radeva, J. Vitria, and J. Mauri, "Adaboost to classify plaque appearance in IVUS images," 2004, pp. 629-636.

  101. O. Pujol, D. Rotger, P. Radeva, O. Rodriguez, and J. Mauri, "Near real-time plaque segmentation of IVUS," 2003, pp. 69-72.

  102. Selvathi D, Emimal N, Selvaraj H. Automated characterization of atheromatous plaque in intravascular ultrasound images using neuro fuzzy classifier. International Journal of Electronics and Telecommunications. 2012;58:425–31.

    Google Scholar 

  103. Taki A, Hetterich H, Roodaki A, Setarehdan SK, Unal G, Rieber J, et al. A new approach for improving coronary plaque component analysis based on intravascular ultrasound images. Ultrasound Med Biol. 2010;36:1245–58.

    PubMed  Google Scholar 

  104. Taki A, Roodaki A, Setarehdan SK, Avansari S, Unal G, Navab N. An IVUS image-based approach for improvement of coronary plaque characterization. Comput Biol Med. 2013;43:268–80.

    PubMed  Google Scholar 

  105. Vince DG, Dixon KJ, Cothren RM, Cornhill JF. Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images. Comput Med Imaging Graph. 2000a;24:221–9.

    CAS  PubMed  Google Scholar 

  106. Zhang X, McKay CR, Sonka M. Tissue characterization in intravascular ultrasound images. IEEE Trans Med Imaging. 1998;17:889–99.

    CAS  PubMed  Google Scholar 

  107. Escalera S, Pujol O, Mauri J, Radeva P. Intravascular ultrasound tissue characterization with sub-class error-correcting output codes. Journal of Signal Processing Systems. 2009;55:35–47.

    Google Scholar 

  108. Granada JF, Wallace-Bradley D, Win HK, Alviar CL, Builes A, Lev EI, et al. In vivo plaque characterization using intravascular ultrasound–virtual histology in a porcine model of complex coronary lesions. Arterioscler Thromb Vasc Biol. 2007;27:387–93.

    CAS  PubMed  Google Scholar 

  109. Brown AJ, Obaid DR, Costopoulos C, Parker RA, Calvert PA, Teng Z, et al. Direct comparison of virtual-histology intravascular ultrasound and optical coherence tomography imaging for identification of thin-cap fibroatheroma clinical perspective. Circulation: Cardiovascular Imaging. 2015;8:e003487.

    Google Scholar 

  110. Maehara A, Mintz GS, Stone GW. OCT versus IVUS: accuracy versus clinical utility. JACC: Cardiovasc Imaging. 2013.

  111. Murray SW, Stables RH, Hart G, Palmer ND. Defining the magnitude of measurement variability in the virtual histology analysis of acute coronary syndrome plaques. European Heart Journal–Cardiovascular Imaging. 2012;14:167–74.

    PubMed  Google Scholar 

  112. de Korte CL, Sierevogel MJ, Mastik F, Strijder C, Schaar JA, Velema E, et al. Identification of atherosclerotic plaque components with intravascular ultrasound elastography in vivo: a Yucatan pig study. Circulation. 2002;105:1627–30.

    PubMed  Google Scholar 

  113. Katouzian A, Sathyanarayana S, Baseri B, Konofagou EE, Carlier SG. Challenges in atherosclerotic plaque characterization with intravascular ultrasound (IVUS): from data collection to classification. IEEE Trans Inf Technol Biomed. 2008;12:315–27.

    PubMed  Google Scholar 

  114. Diethrich EB, Pauliina Margolis M, Reid DB, Burke A, Ramaiah V, Rodriguez-Lopez JA, et al. Virtual histology intravascular ultrasound assessment of carotid artery disease: the carotid artery plaque virtual histology evaluation (CAPITAL) study. Journal of Endovascular Therapy : an Official Journal of the International Society of Endovascular Specialists. 2007;14:676–86. https://doi.org/10.1583/1545-1550(2007)14[676:VHIUAO]2.0.CO;2.

    Article  Google Scholar 

  115. •• Zhang L, Wahle A, Chen Z, Lopez JJ, Kovarnik T, Sonka M. Predicting locations of high-risk plaques in coronary arteries in patients receiving statin therapy. IEEE Transactions on Medical Imaging. 2018;37:151–61. https://doi.org/10.1109/tmi.2017.2725443. Demonstration of combining data from multiple sources to greatly improve risk stratification for coronary artery disease.

    Article  PubMed  Google Scholar 

  116. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer; 2009.

    Google Scholar 

  117. Nair A, Calvetti D, Kuban BD, Vince DG. Novel technique for normalization of intravascular ultrasound backscatter data: improvement in spatial accuracy of tissue maps. Am J Cardiol Suppl S. 2004a;94:123E.

    Google Scholar 

  118. Nair A, Calvetti D, Vince DG. Regularized autoregressive analysis of intravascular ultrasound backscatter: improvement in spatial accuracy of tissue maps. IEEE Trans Ultrason Ferroelectr Freq Control. 2004b;51:420–31. https://doi.org/10.1109/TUFFC.2004.1295427.

    Article  PubMed  Google Scholar 

  119. Breiman L. Random forests. Mach Learn. 2001;45:5–32. https://doi.org/10.1023/a:1010933404324.

    Article  Google Scholar 

  120. Campos CM, Fedewa RJ, Garcia-Garcia HM, Vince DG, Margolis MP, Lemos PA, et al. Ex vivo validation of 45 MHz intravascular ultrasound backscatter tissue characterization. Eur Heart J Cardiovasc Imaging. 2015;16:1112–9. https://doi.org/10.1093/ehjci/jev039.

    Article  PubMed  Google Scholar 

  121. Muramatsu T, Garcia-Garcia HM, Brugaletta S, Heo JH, Onuma Y, Fedewa RJ, et al. Reproducibility of intravascular ultrasound radiofrequency data analysis (virtual histology) with a 45-MHz rotational imaging catheter in ex vivo human coronary arteries. J Cardiol. 2015;65:134–42. https://doi.org/10.1016/j.jjcc.2014.05.004.

    Article  PubMed  Google Scholar 

  122. Moradi M, Abolmaesumi P, Siemens DR, Sauerbrei EE, Boag AH, Mousavi P. Augmenting detection of prostate cancer in transrectal ultrasound images using SVM and RF time series. IEEE Trans Biomed Eng. 2009;56:2214–24. https://doi.org/10.1109/TBME.2008.2009766.

    Article  PubMed  Google Scholar 

  123. Caixinha M, Amaro J, Santos M, Perdigao F, Gomes M, Santos J. In-vivo automatic nuclear cataract detection and classification in an animal model by ultrasounds. IEEE Trans Biomed Eng. 2016;63:2326–35. https://doi.org/10.1109/TBME.2016.2527787.

    Article  PubMed  Google Scholar 

  124. Karatzoglou A, Meyer D, Hornik K. Support vector machines in R. J Stat Softw. 2006;15.

  125. Meier DS, Cothren RM, Vince DG, Cornhill JF. Automated morphometry of coronary arteries with digital image analysis of intravascular ultrasound. Am Heart J. 1997;133:681–90.

    CAS  PubMed  Google Scholar 

  126. Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vis. 1988;1:321–s. https://doi.org/10.1007/BF00133570.

    Article  Google Scholar 

  127. Mendizabal-Ruiz G, Kakadiaris IA. A physics-based intravascular ultrasound image reconstruction method for lumen segmentation. Comput Biol Med. 2016;75:19–29. https://doi.org/10.1016/j.compbiomed.2016.05.007.

    Article  PubMed  Google Scholar 

  128. Klingensmith JD, Vince DG. B-spline methods for interactive segmentation and modeling of lumen and vessel surfaces in three-dimensional intravascular ultrasound. Comput Med Imaging Graph. 2002;26:429–38.

    PubMed  Google Scholar 

  129. Klingensmith VDJD, Nair A, Kuban BD. System and method for identifying a vascular border. USA Patent 8,630,492, January 14, 2014.

  130. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Lect Notes Comput Sc. 2015;9351:234–41. https://doi.org/10.1007/978-3-319-24574-4_28.

    Article  Google Scholar 

  131. Yang J, Faraji M, Basu A. Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net. Ultrasonics. 2019;96:24–33. https://doi.org/10.1016/j.ultras.2019.03.014.

    Article  PubMed  Google Scholar 

  132. Su S, Hu Z, Lin Q, Hau WK, Gao Z, Zhang H. An artificial neural network method for lumen and media-adventitia border detection in IVUS. Comput Med Imaging Graph. 2017;57:29–39. https://doi.org/10.1016/j.compmedimag.2016.11.003.

    Article  PubMed  Google Scholar 

  133. Qian CJ, Yang XP. An integrated method for atherosclerotic carotid plaque segmentation in ultrasound image. Comput Methods Program Biomed. 2018;153:19–32. https://doi.org/10.1016/j.cmpb.2017.10.002.

    Article  Google Scholar 

  134. Attizzani GF, Bezerra HG. Contemporary assessment of stent strut coverage by OCT. Int J Card Imaging. 2013;29:23–7. https://doi.org/10.1007/s10554-012-0046-0.

    Article  Google Scholar 

  135. Wang A, Eggermont J, Dekker N, Garcia-Garcia HM, Pawar R, Reiber JHC, et al. Automatic stent strut detection in intravascular optical coherence tomographic pullback runs. Int J Card Imaging. 2013;29:29–38. https://doi.org/10.1007/s10554-012-0064-y.

    Article  Google Scholar 

  136. Bruining N, Sihan K, Ligthart J, Winter SD, Regar E. Automated three-dimensional detection of intracoronary stent struts in optical coherence tomography images. 2011 Computing in Cardiology. 2011:221–4.

  137. Gurmeric S, Isguder GG, Carlier S, Unal G. A new 3-D automated computational method to evaluate in-stent neointimal hyperplasia in in-vivo intravascular optical coherence tomography pullbacks. Med Image Comput Comput Assist Interv. 2009;12:776–85. https://doi.org/10.1007/978-3-642-04271-3_94.

    Article  PubMed  Google Scholar 

  138. Lu H, Gargesha M, Wang Z, Chamie D, Attizzani GF, Kanaya T, et al. Automatic stent detection in intravascular OCT images using bagged decision trees. Biomed Opt Express. 2012;3:2809–24. https://doi.org/10.1364/BOE.3.002809.

    Article  PubMed  PubMed Central  Google Scholar 

  139. Tsantis S, Kagadis GC, Katsanos K, Karnabatidis D, Bourantas G, Nikiforidis GC. Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography. Med Phys. 2012;39:503–13. https://doi.org/10.1118/1.3673067.

    Article  PubMed  Google Scholar 

  140. Wang Z, Jenkins MW, Linderman GC, Bezerra HG, Fujino Y, Costa MA, et al. 3-D stent detection in intravascular OCT using a Bayesian network and graph search. IEEE Trans Med Imaging. 2015;34:1549–61. https://doi.org/10.1109/TMI.2015.2405341.

    Article  PubMed Central  Google Scholar 

  141. Xu C, Schmitt JM, Akasaka T, Kubo T, Huang K. Automatic detection of stent struts with thick neointimal growth in intravascular optical coherence tomography image sequences. Phys Med Biol. 2011;56:6665–75. https://doi.org/10.1088/0031-9155/56/20/010.

    Article  PubMed  Google Scholar 

  142. Ughi GJ, Van Dyck CJ, Adriaenssens T, Hoymans VY, Sinnaeve P, Timmermans J-P, et al. Automatic assessment of stent neointimal coverage by intravascular optical coherence tomography. Eur Heart J Cardiovasc Imaging. 2014;15:195–200. https://doi.org/10.1093/ehjci/jet134.

    Article  PubMed  Google Scholar 

  143. Adriaenssens T, Ughi GJ, Dubois C, Onsea K, De Cock D, Bennett J, et al. Automated detection and quantification of clusters of malapposed and uncovered intracoronary stent struts assessed with optical coherence tomography. Int J Card Imaging. 2014;30:839–48. https://doi.org/10.1007/s10554-014-0406-z.

    Article  Google Scholar 

  144. Nam HS, Kim C-S, Lee JJ, Song JW, Kim JW, Yoo H. Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage. Med Phys. 2016;43:1662–75. https://doi.org/10.1118/1.4943374.

    Article  PubMed  Google Scholar 

  145. Lu H, Lee J, Ray S, Tanaka K, Bezerra HG, Rollins AM, et al. Automated stent coverage analysis in intravascular OCT (IVOCT) image volumes using a support vector machine and mesh growing. Biomed Opt Express. 2019;10:2809–28. https://doi.org/10.1364/BOE.10.002809.

    Article  PubMed  PubMed Central  Google Scholar 

  146. Phipps JE, Vela D, Hoyt T, Halaney D, Mancuso JJ, Buja LM, et al. Macrophages and intravascular optical coherence tomography bright spots: a quantitative study. JACC Cardiovasc Imaging. 2015;8:63–72. https://doi.org/10.1016/j.jcmg.2014.07.027.

    Article  PubMed  Google Scholar 

  147. Tearney Guillermo J, Yabushita H, Houser Stuart L, Aretz HT, Jang I-K, Schlendorf Kelly H, et al. Quantification of macrophage content in atherosclerotic plaques by optical coherence tomography. Circulation. 2003;107:113–9. https://doi.org/10.1161/01.CIR.0000044384.41037.43.

    Article  CAS  PubMed  Google Scholar 

  148. Wang Z, Jia H, Tian J, Soeda T, Vergallo R, Minami Y, et al. Computer-aided image analysis algorithm to enhance in vivo diagnosis of plaque erosion by intravascular optical coherence tomography. Circ Cardiovasc Imaging. 2014;7:805–10. https://doi.org/10.1161/CIRCIMAGING.114.002084.

    Article  PubMed  Google Scholar 

  149. Tearney GJ, Regar E, Akasaka T, Adriaenssens T, Barlis P, Bezerra HG, et al. Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the international working group for intravascular optical coherence tomography standardization and validation. J Am Coll Cardiol. 2012;59:1058–72. https://doi.org/10.1016/j.jacc.2011.09.079.

    Article  PubMed  Google Scholar 

  150. van Soest G, Goderie T, Regar E, Koljenović S, van Leenders GLJH, Gonzalo N, et al. Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging. J Biomed Opt. 2010;15:011105. https://doi.org/10.1117/1.3280271.

    Article  PubMed  Google Scholar 

  151. Gargesha M, Shalev R, Prabhu D, Tanaka K, Rollins AM, Costa M, et al. Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography. J Med Imaging (Bellingham). 2015;2. https://doi.org/10.1117/1.JMI.2.1.016001.

  152. Vermeer KA, Mo J, Weda JJA, Lemij HG, Boer JF d. Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography. Biomed Opt Express. 2014;5:322–37. https://doi.org/10.1364/BOE.5.000322.

    Article  Google Scholar 

  153. Liu S, Sotomi Y, Eggermont J, Nakazawa G, Torii S, Ijichi T, et al. Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images. J Biomed Opt. 2017;22:1–16. https://doi.org/10.1117/1.JBO.22.9.096004.

    Article  PubMed  Google Scholar 

  154. Athanasiou LS, Exarchos TP, Naka KK, Michalis LK, Prati F, Fotiadis DI. Atherosclerotic plaque characterization in optical coherence tomography images. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 4485-4488.

  155. Ughi GJ, Adriaenssens T, Sinnaeve P, Desmet W, D’hooge J. Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images. Biomed Optics Express. 2013;4:1014–30.

    Google Scholar 

  156. Zhou P, Zhu T, He C, Li Z. Automatic classification of atherosclerotic tissue in intravascular optical coherence tomography images. J Opt Soc Am A Opt Image Sci Vis. 2017;34:1152–9. https://doi.org/10.1364/JOSAA.34.001152.

    Article  CAS  PubMed  Google Scholar 

  157. Rico-Jimenez JJ, Campos-Delgado DU, Villiger M, Otsuka K, Bouma BE, Jo JA. Automatic classification of atherosclerotic plaques imaged with intravascular OCT. Biomed Opt Express. 2016;7:4069–85. https://doi.org/10.1364/BOE.7.004069.

    Article  PubMed  PubMed Central  Google Scholar 

  158. Xu C, Schmitt JM, Carlier SG, Virmani R. Characterization of atherosclerosis plaques by measuring both backscattering and attenuation coefficients in optical coherence tomography. J Biomed Opt. 2008;13:034003.

    PubMed  Google Scholar 

  159. Abdolmanafi A, Duong L, Dahdah N, Cheriet F. Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. Biomed Opt Express. 2017;8:1203–20. https://doi.org/10.1364/BOE.8.001203.

    Article  PubMed  PubMed Central  Google Scholar 

  160. Yong YL, Tan LK, McLaughlin RA, Chee KH, Liew YM. Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography. J Biomed Opt. 2017;22:1–9. https://doi.org/10.1117/1.JBO.22.12.126005.

    Article  PubMed  Google Scholar 

  161. Abdolmanafi A, Duong L, Dahdah N, Adib IR, Cheriet F. Characterization of coronary artery pathological formations from OCT imaging using deep learning. Biomed Opt Express. 2018;9:4936–60. https://doi.org/10.1364/BOE.9.004936.

    Article  PubMed  PubMed Central  Google Scholar 

  162. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60:84–90. https://doi.org/10.1145/3065386.

    Article  Google Scholar 

  163. He S, Zheng J, Maehara A, Mintz G, Tang D, Anastasio M, et al. Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images. Medical Imaging 2018: Image Processing. 107:2018. https://doi.org/10.1117/12.2293957.

  164. Gessert N, Lutz M, Heyder M, Latus S, Leistner DM, Abdelwahed YS, et al. Automatic plaque detection in IVOCT pullbacks using convolutional neural networks. IEEE Trans Med Imaging. 2019;38:426–34. https://doi.org/10.1109/TMI.2018.2865659.

    Article  PubMed  Google Scholar 

  165. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.

  166. Huang G, Liu Z, Maaten LVD, Weinberger KQ. Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261-2269.

  167. Prabhu D, Kolluru C, Gharaibeh Y, Mehanna E, Wen D, Wu H, et al. Automated A-line plaque classification of intravascular optical coherence tomography using 3D cryo-image/histology validation. J Biomed Opt. 2019a;24:1–15.

    PubMed  Google Scholar 

  168. Prabhu D, Bezerra HG, Chaitanya K, Gharaibeh Y, Emile M, Hao W, et al. Automated A-line coronary plaque classification of intravascular OCT images using hand-crafted features and large datasets. J Biomed Optics. 2019b;24(10):106002. https://doi.org/10.1117/1.JBO.24.10.106002.

    Article  Google Scholar 

  169. Lee J, Prabhu D, Kolluru C, Gharaibeh Y, Zimin VN, Bezerra HG, Wilson DL. Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images, Biomed Optics Express. 2019;10:6497–6515.

  170. Wang Z, Chamie D, Bezerra HG, Yamamoto H, Kanovsky J, Wilson DL, et al. Volumetric quantification of fibrous caps using intravascular optical coherence tomography. Biomed Optics Express. 2012;3:1413–26. https://doi.org/10.1364/BOE.3.001413.

    Article  Google Scholar 

  171. Wang Z, Kyono H, Bezerra HG, Wang H, Gargesha M, Alraies C, et al. Semiautomatic segmentation and quantification of calcified plaques in intracoronary optical coherence tomography images. J Biomed Opt. 2010;15:061711. https://doi.org/10.1117/1.3506212.

    Article  PubMed  Google Scholar 

  172. Kolluru C, Prabhu D, Gharaibeh Y, Bezerra H, Guagliumi G, Wilson D. Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images. J Med Imaging (Bellingham). 2018;5:044504. https://doi.org/10.1117/1.JMI.5.4.044504.

    Article  Google Scholar 

  173. Gharaibeh Y, Dong P, Prabhu D, Kolluru C, Lee J, Zimin V et al. Deep learning segmentation of coronary calcified plaque from intravascular optical coherence tomography (IVOCT) images with application to finite element modeling of stent deployment. In Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 2019, p. 109511C.

  174. Le AS, Aoki H, Murase F, Ishida K. A novel method for classifying driver mental workload under naturalistic conditions with information from near-infrared spectroscopy. Front Hum Neurosci. 2018;12:431–1. https://doi.org/10.3389/fnhum.2018.00431.

  175. Esperança PM, Blagborough AM, Da DF, Dowell FE, Churcher TS. Detection of Plasmodium berghei infected Anopheles stephensi using near-infrared spectroscopy. Parasit Vectors. 2018;11:377–7. https://doi.org/10.1186/s13071-018-2960-z.

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This project was supported by the National Heart, Lung, and Blood Institute through grants R01-HL114406 and R01-HL143484. The content of this report is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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  1. Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, 44195, USA

    Russell Fedewa, D. Geoffrey Vince & Aaron Fleischman

  2. Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, 44195, USA

    Rishi Puri

  3. Department of Internal Medicine, New York Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, 11215, USA

    Eitan Fleischman

  4. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    Juhwan Lee, David Prabhu & David L. Wilson

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Conflict of Interest

Rishi Puri, Eitan Fleischman, Juhwan Lee, David Prabhu, David Wilson, and Aaron Fleischman declare that they have no conflict of interest.

Russell Fedewa reports non-financial support from Siemens Medical Solutions USA, Inc., and grants from US Army Medical Research and Material Command CDMRP PRMRP W81XWH-16-1-0608. In addition, Dr. Fedewa has a patent on Advanced ultrasonic detection of different tissue types (WO2019075483A1) pending to Cleveland Clinic.

D. Geoffrey Vince reports grants from Cleveland Clinic (NIH R01 HL64686), and royalty payments from Volcano. In addition, Dr. Vince has a patent issued (6,200,268: Vascular Plaque Characterization. Vince DG, Kuban BD, Nair A. Issued March 13, 2001).

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Fedewa, R., Puri, R., Fleischman, E. et al. Artificial Intelligence in Intracoronary Imaging. Curr Cardiol Rep 22, 46 (2020). https://doi.org/10.1007/s11886-020-01299-w

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