Literature DB >> 31320024

Impact of Artificial Intelligence on Interventional Cardiology: From Decision-Making Aid to Advanced Interventional Procedure Assistance.

Partha Sardar1, J Dawn Abbott1, Amartya Kundu2, Herbert D Aronow1, Juan F Granada3, Jay Giri4.   

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.
Copyright © 2019. Published by Elsevier Inc.

Entities:  

Keywords:  artificial intelligence; interventional cardiology

Year:  2019        PMID: 31320024     DOI: 10.1016/j.jcin.2019.04.048

Source DB:  PubMed          Journal:  JACC Cardiovasc Interv        ISSN: 1936-8798            Impact factor:   11.195


  10 in total

1.  Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography.

Authors:  Daniel Sierra-Lara Martinez; Peter A Noseworthy; Oguz Akbilgic; Joerg Herrmann; Kathryn J Ruddy; Abdulaziz Hamid; Ragasnehith Maddula; Ashima Singh; Robert Davis; Fatma Gunturkun; John L Jefferies; Sherry-Ann Brown
Journal:  Am Heart J Plus       Date:  2022-04-01

2.  A simplified approach to identification of risk status in patients with atherosclerotic cardiovascular disease.

Authors:  Aparna Sajja; Hsin-Fang Li; Kateri J Spinelli; Amir Ali; Salim S Virani; Seth S Martin; Ty J Gluckman
Journal:  Am J Prev Cardiol       Date:  2021-04-27

Review 3.  The application of big data to cardiovascular disease: paths to precision medicine.

Authors:  Jane A Leopold; Bradley A Maron; Joseph Loscalzo
Journal:  J Clin Invest       Date:  2020-01-02       Impact factor: 14.808

4.  Development and Human Factors Considerations for Extended Reality Applications in Medicine: The Enhanced ELectrophysiology Visualization and Interaction System (ĒLVIS).

Authors:  Jennifer N Avari Silva; Mary Beth Privitera; Michael K Southworth; Jonathan R Silva
Journal:  Virtual Augment Mixed Real (2020)       Date:  2020-07-10

Review 5.  Applications of Machine Learning in Cardiology.

Authors:  Karthik Seetharam; Sudarshan Balla; Christopher Bianco; Jim Cheung; Roman Pachulski; Deepak Asti; Nikil Nalluri; Astha Tejpal; Parvez Mir; Jilan Shah; Premila Bhat; Tanveer Mir; Yasmin Hamirani
Journal:  Cardiol Ther       Date:  2022-07-12

Review 6.  Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.

Authors:  Giorgio Quer; Ramy Arnaout; Michael Henne; Rima Arnaout
Journal:  J Am Coll Cardiol       Date:  2021-01-26       Impact factor: 24.094

7.  Artificial intelligence and the cardiologist: what you need to know for 2020.

Authors:  Antonio de Marvao; Timothy Jw Dawes; James Philip Howard; Declan P O'Regan
Journal:  Heart       Date:  2020-01-23       Impact factor: 5.994

Review 8.  Influential Usage of Big Data and Artificial Intelligence in Healthcare.

Authors:  Yan Cheng Yang; Saad Ul Islam; Asra Noor; Sadia Khan; Waseem Afsar; Shah Nazir
Journal:  Comput Math Methods Med       Date:  2021-09-06       Impact factor: 2.238

9.  Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning.

Authors:  Matej Pičulin; Tim Smole; Bojan Žunkovič; Enja Kokalj; Marko Robnik-Šikonja; Matjaž Kukar; Dimitrios I Fotiadis; Vasileios C Pezoulas; Nikolaos S Tachos; Fausto Barlocco; Francesco Mazzarotto; Dejana Popović; Lars S Maier; Lazar Velicki; Iacopo Olivotto; Guy A MacGowan; Djordje G Jakovljević; Nenad Filipović; Zoran Bosnić
Journal:  JMIR Med Inform       Date:  2022-02-02

Review 10.  Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble.

Authors:  Walid Ben Ali; Ahmad Pesaranghader; Robert Avram; Pavel Overtchouk; Nils Perrin; Stéphane Laffite; Raymond Cartier; Reda Ibrahim; Thomas Modine; Julie G Hussin
Journal:  Front Cardiovasc Med       Date:  2021-12-08
  10 in total

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