Near future artificial intelligence in interventional cardiology: new opportunities and challenges to improve the care of STEMI patients

RR Bond, Khaled Rjoob, D Finlay, V. E. McGilligan, Stephen James Leslie, Charles Knoery, Aleeha Iftikhar, Anne McShane, Irina Andra Tache, Pardis Biglarbeigi, Matthew Manktelow, Aaron Peace

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This ‘point-of-view’ paper outlines the sub-disciplines of artificial intelligence (AI) and discusses a series of opportunities for AI to enhance a typical pathway of a primary percutaneous coronary intervention service. The paper outlines AI applications in interventional cardiology from diagnosis (AI based cardiac diagnostics), to treatment (AI in the Cath-lab) and recovery (AI based patient rehabilitation, patient monitoring and automated clinical feedback). AI applications at the diagnostic phase can include the use of machine learning to assist in clinical decision making, for example when triaging patients, reading 12-lead electrocardiograms to auto-detect coronary occlusions (not just ST elevation myocardial infarctions or STEMIs) or predicting patient mortality. Opportunities for AI applications at the peri-intervention stage can include computer vision to aid angiographic analysis, which comprise of automatic tracking of coronary arteries and anatomy to automated thrombolysis in myocardial infarction (TIMI) and syntax scoring systems (an angiographic tool) which can be used to grade complexity of coronary artery disease by grading 11 types of lesions including (Total occlusion, Trifurcation, Bifurcation, Aorto, Dominance, Length, Heavy calcification, Thrombus, Diffuse disease, Number of diseased segments, Tortuosity). Post-intervention AI opportunities might involve intelligent monitoring systems to track patients and AI chatbot technologies to provide personalized coaching for cardiac rehabilitation to improve recovery and reduce readmissions. Automated activity logging in the Cath-Lab using internet of things systems, sensors and cameras could also be used in the post-intervention phase to provide clinical feedback to cardiology teams showing important trends, associations and patterns during procedures. Whilst these opportunities exist, there are also non-technological challenges such as regulatory and ethical challenges related to the deployment of AI systems.
Original languageEnglish
Number of pages15
JournalJournal of ESC Digital Health
Publication statusPublished (in print/issue) - 31 Mar 2020


  • cardiology
  • acute myocardial infarction
  • AI
  • machine learning


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