CoChat
Sign Up
Cited by 151Artificial Intelligence in Healthcare and EducationCoronary Interventions and DiagnosticsArtificial Intelligence in Healthcare

Impact of Artificial Intelligence on Interventional Cardiology

Open access
Sardar, Partha, Abbott, J. Dawn, Kundu, Amartya, Aronow, Herbert D., Granada, Juan F., Giri, Jay·2019·JACC: Cardiovascular Interventions
DOI 10.1016/j.jcin.2019.04.048
Open this paper in CoChat — free account

Abstract

Access to big data analyzed by supercomputers using advanced mathematical algorithms (i.e., deep machine learning) has allowed for enhancement of cognitive output (i.e., visual imaging interpretation) to previously unseen levels and promises to fundamentally change the practice of medicine. This field, known as "artificial intelligence" (AI), is making significant progress in areas such as automated clinical decision making, medical imaging analysis, and interventional procedures, and has the potential to dramatically influence the practice of interventional cardiology. The unique nature of interventional cardiology makes it an ideal target for the development of AI-based technologies designed to improve real-time clinical decision making, streamline workflow in the catheterization laboratory, and standardize catheter-based procedures through advanced robotics. This review provides an introduction to AI by highlighting its scope, potential applications, and limitations in interventional cardiology.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.jcin.2019.04.048

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI, ML, and DL can automate many repetitive and mundane tasks in robotics, freeing up human resources to focus on more complex tasks.

    “Automation: AI, ML, and DL can automate many repetitive and mundane tasks in robotics, freeing up human resources to focus on more complex tasks [49] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [49]

    The Robotics passage (section labeled Abstract; page unavailable) says AI has potential to automate procedures in the catheterization lab, and the paper describes an automated robotic guidewire movement. It does not say that AI, ML, and DL automate many repetitive or mundane robotic tasks, or that such automation frees personnel to focus on more complex work. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 27

  1. [1]1. Shameer K., Johnson K.W., Glicksberg B.S., Dudley J.T., Sengupta P.P. Machine learning in cardiovascular medicine: are we there yet? Heart 2018;104:1156-1164. Crossref PubMed Google Scholar a [...] object/word recognition and decision-making b [...] class of algorithms called neural networks c [...] features, and ultimately provides an output d [...] environment to maximize a reward e [...] data, and select personalized medications f [...] images, including digital video frames g [...] of requiring that physicians postulate them h [...] bleeding, contrast nephropathy, and stroke i [...] include a variety of vascular interventions j [...] trackers and health care system analysis k [...] which are based on a ‘‘black box’’ design l [...] the data in clinically meaningful ways m [...] shallow in intelligence in its current form
  2. [2]2. Krittanawong C., Zhang H., Wang Z., Aydar M., Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol 2017;69:2657-2664. Crossref PubMed Google Scholar a [...] object/word recognition and decision-making b [...] superior and more robust predictions c [...] features, and ultimately provides an output d [...] source data have to be labeled by humans e [...] from hidden patterns present in the data f [...] environment to maximize a reward g [...] learning from its own success and failures h [...] data, and select personalized medications i [...] guidelines, and various Internet sources j [...] analyzed, and used to their full potential” k [...] add hidden risk factors to existing models l [...] mortality risk with procedures, and so on m [...] bleeding, contrast nephropathy, and stroke n [...] the optimal duration of medical therapy o [...] healthy lifestyle and medication adherence p [...] chain robots, and personal assistance q [...] the addition of ML does not improve results
  3. [4]4. Henglin M., Stein G., Hushcha P.V., Snoek J., Wiltschko A.B., Cheng S. Machine learning approaches in cardiovascular imaging. Circ Cardiovasc Imaging 2017;10:e005614. Crossref PubMed Google Scholar a [...] as well as advance technological automation b [...] different data, may mitigate this issue. c [...] simplification of imaging interpretation d [...] with suspected coronary artery disease
  4. [6]6. Jiang F., Jiang Y., Zhi H., et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017;2:230-243. Crossref PubMed Google Scholar a [...] as well as advance technological automation b [...] source data have to be labeled by humans c [...] learning from its own success and failures d [...] ability to understand human language e [...] claims coding, and to improve workflow f [...] of requiring that physicians postulate them g [...] mortality risk with procedures, and so on h [...] trackers and health care system analysis
  5. [8]8. Al'Aref S.J., Anchouche K., Singh G., et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J 2019;40:1975-1986. Crossref PubMed Google Scholar a [...] from large, complex, and heterogeneous data b [...] and optical coherence tomography) c [...] echocardiographic mitral regurgitation d [...] simplification of imaging interpretation e [...] the data in clinically meaningful ways
  6. [9]9. Azzalini L., Vilca L.M., Lombardo F., et al. Incidence of contrast-induced acute kidney injury in a large cohort of all-comers undergoing percutaneous coronary intervention: Comparison of five contrast media. Int J Cardiol 2018;273:69-73. Go to Citation Crossref PubMed Google Scholar
  7. [10]10. Narula S., Shameer K., Salem Omar A.M., Dudley J.T., Sengupta P.P. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol 2016;68:2287-2295. Go to Citation Crossref PubMed Google Scholar
  8. [11]11. Shah S.J., Katz D.H., Selvaraj S., et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 2015;131:269-279. Go to Citation Crossref PubMed Google Scholar
  9. [12]12. Small A.M., Kiss D.H., Zlatsin Y., et al. Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease. J Biomed Inform 2017;72:77-84. Go to Citation Crossref PubMed Google Scholar
  10. [13]13. Sohn S., Ye Z., Liu H., Chute C.G., Kullo I.J. Identifying abdominal aortic aneurysm cases and controls using natural language processing of radiology reports. AMIA Jt Summits Transl Sci Proc 2013;2013:249-253. Go to Citation PubMed Google Scholar
  11. [14]14. Du T., Xie L., Liu X., et al. Intelligent recognition of coronary angiography by deep learning technology: a novel computer-aided diagnostic system. Paper presented at: Transcatheter Cardiovascular Therapeutics Symposium (TCT) 18; September 21–25, 2018; San Diego, CA. Google Scholar a [...] . A study by Du et al. b [...] 82.6%, 85.5%, and 85.8%, respectively
  12. [15]15. Ciusdel C., Turcea A., Puiu A., et al. An artificial intelligence based solution for fully automated cardiac phase and end-diastolic frame detection on coronary angiographies. Paper presented at: Transcatheter Cardiovascular Therapeutics Symposium (TCT) 18; September 21–25, 2018; San Diego, CA. Google Scholar a [...] . Another study by Ciusdel et al. b [...] specificity of 92.9% on the evaluation set
  13. [16]16. Molony D., Hosseini H., Samady H. Deep IVUS: a machine learning framework for fully automatic IVUS segmentation. Paper presented at: Transcatheter Cardiovascular Therapeutics Symposium (TCT) 18; September 21–25, 2018; San Diego, CA. Go to Citation Google Scholar
  14. [17]17. Fairbairn T.A., Nieman K., Akasaka T., et al. Real-world clinical utility and impact on clinical decision-making of coronary computed tomography angiography-derived fractional flow reserve: lessons from the ADVANCE registry. Eur Heart J 2018;39:3701-3711. Go to Citation Crossref PubMed Google Scholar
  15. [18]18. Fearon W.F., Achenbach S., Engstrøm T. Accuracy of fractional flow reserve derived from coronary angiography. Circulation 2019;139:477-484. Go to Citation Crossref PubMed Google Scholar
  16. [20]20. Davies J. CEREBRIA-1: machine learning vs expert human opinion to determine physiologically optimized coronary revascularization strategies. Paper presented at: Transcatheter Cardiovascular Therapeutics Symposium (TCT) 18; September 24, 2018; San Diego, CA. Google Scholar a [...] with stable coronary artery disease b [...] of PCI and determining PCI strategy
  17. [21]21. Somashekhar S.P., Sepúlveda M.J., Norden A.D., et al. Early experience with IBM Watson for Oncology (WFO) cognitive computing system for lung and colorectal cancer treatment (abstr). J Clin Oncol 2017;35:15 Suppl:8527. Go to Citation Crossref Google Scholar
  18. [23]23. Silva J.N.A., Southworth M., Raptis C., Silva J. Emerging applications of virtual reality in cardiovascular medicine. J Am Coll Cardiol Basic Trans Science 2018;3:420-430. Go to Citation Crossref PubMed Google Scholar
  19. [25]25. Steinhubl S.R., Topol E.J. Now we’re talking: bringing a voice to digital medicine. Lancet 2018;392:627. Go to Citation Crossref Google Scholar
  20. [26]26. Boone D. Cardiovascular disease and percutaneous coronary intervention in hematological malignancies: knowledge is power. Paper presented at: Transcatheter Cardiovascular Therapeutics Symposium (TCT) 18; September 21–25, 2018; San Diego, CA. Go to Citation Google Scholar
  21. [27]27. Ganim N., Monlezun D., Balan P. Racial disparities in procedural complications after transcatheter aortic valve replacement (TAVR): a retrospective single-center case-control study. Paper presented at: Transcatheter Cardiovascular Therapeutics Symposium (TCT) 18; September 21–25, 2018; San Diego, CA. Go to Citation Google Scholar
  22. [29]29. Corindus Receives FDA Clearance for First Automated Robotic Movement in technIQ™ Series for CorPath GRX Platform [press release]. March 5, 2018. Available at: https://goo.gl/Sb1B2i. Accessed October 10, 2018. Go to Citation Google Scholar
  23. [31]31. Elson D.S., Cleary K., Dupont P., Merrifield R., Riviere C. Medical robotics. Ann Biomed Eng 2018;46:1433-1436. Crossref PubMed Google Scholar a [...] chain robots, and personal assistance b [...] trackers and health care system analysis
  24. [32]32. Beam A.L., Kohane I.S. Big data and machine learning in health care. JAMA 2018;319:1317-1318. Crossref PubMed Google Scholar a [...] device that can spin data into gold” b [...] fairness, equitability, or even veracity”
  25. [33]33. Frizzell J.D., Liang L., Schulte P.J., et al. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol 2017;2:204-209. Go to Citation Crossref PubMed Google Scholar
  26. [34]34. Topol E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44-56. Go to Citation Crossref PubMed Google Scholar
  27. [36]36. Sengupta P.P., Adjeroh D.A. Will artificial intelligence replace the human echocardiographer? Circulation 2018;138:1639-1642. Crossref PubMed Google Scholar a [...] where images are just sensed or represented b [...] shallow in intelligence in its current form

Cited by

Sorted by citations

Authors
Sardar, P., Abbott, J. D., Kundu, A., Aronow, H. D., Granada, J. F., & Giri, J.
Affiliations
  • Brown University (US)
  • University of Massachusetts Chan Medical School (US)
  • Columbia University Irving Medical Center (US)
  • Leonard Davis Institute of Health Economics (US)
Corresponding author
Giri, Jay
ORCID
Venue
JACC: Cardiovascular Interventions
Publisher
Elsevier BV
Published
2019-07
Volume
12
Issue
14
Pages
1293-1303
ISSN
  • 1876-7605 (ISSN-L)
  • 1936-8798
DOI
10.1016/j.jcin.2019.04.048
OpenAlex
W2962231994
Cited by
151
References
4
Citations by year
2026: 16 · 2025: 28 · 2024: 26 · 2023: 28 · 2022: 16 · 2021: 21 · 2020: 14 · 2019: 2
FWCI
0.65
Type
journal article
Language
en
Update notices
erratum (2019-08-26)
Open access
Licence
Locations
  • JACC: Cardiovascular Interventions (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Surgery › Coronary Interventions and Diagnostics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence, interventional cardiology, deep learning, medical image analysis, big data
MeSH terms
Clinical Decision-Making, Machine Learning, Supervised Machine Learning, Unsupervised Machine Learning, Deep Learning, Cardiovascular Diseases, Decision Support Techniques, Diagnosis, Computer-Assisted, Diffusion of Innovation, Cardiac Catheterization, Humans, Predictive Value of Tests, Robotics, Therapy, Computer-Assisted, Patient Selection, Workflow

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review