Literature DB >> 33449234

[Artificial intelligence in cardiology : Relevance, current applications, and future developments].

Bettina Zippel-Schultz1, Carsten Schultz2, Dirk Müller-Wieland3, Andrew B Remppis4, Martin Stockburger5, Christian Perings6, Thomas M Helms7,8.   

Abstract

Big data and applications of artificial intelligence (AI), such as machine learning or deep learning, will enrich healthcare in the future and become increasingly important. Among other things, they have the potential to avoid unnecessary examinations as well as diagnostic and therapeutic errors. They could enable improved, early and accelerated decision-making. In the article, the authors provide an overview of current AI-based applications in cardiology. The examples describe innovative solutions for risk assessment, diagnosis and therapy support up to patient self-management. Big data and AI serve as a basis for efficient, predictive, preventive and personalised medicine. However, the examples also show that research is needed to further develop the solutions for the benefit of the patient and the medical profession, to demonstrate the effectiveness and benefits in health care and to establish legal and ethical standards.

Entities:  

Keywords:  Acceptance; Big data; Decision-making support; Risk assessment; Self-management

Year:  2021        PMID: 33449234     DOI: 10.1007/s00399-020-00735-2

Source DB:  PubMed          Journal:  Herzschrittmacherther Elektrophysiol        ISSN: 0938-7412


  32 in total

Review 1.  A systematic review of the main mechanisms of heart failure disease management interventions.

Authors:  Alexander M Clark; Kelly S Wiens; Davina Banner; Jennifer Kryworuchko; Lorraine Thirsk; Lianne McLean; Kay Currie
Journal:  Heart       Date:  2016-02-23       Impact factor: 5.994

Review 2.  Diabetes, heart failure, and renal dysfunction: The vicious circles.

Authors:  Eugene Braunwald
Journal:  Prog Cardiovasc Dis       Date:  2019-08-01       Impact factor: 8.194

3.  Economic impact assessment from the use of a mobile app for the self-management of heart diseases by patients with heart failure in a Spanish region.

Authors:  José Antonio Cano Martín; Borja Martínez-Pérez; Isabel de la Torre-Díez; Miguel López-Coronado
Journal:  J Med Syst       Date:  2014-07-04       Impact factor: 4.460

4.  Smartwatch Algorithm for Automated Detection of Atrial Fibrillation.

Authors:  Joseph M Bumgarner; Cameron T Lambert; Ayman A Hussein; Daniel J Cantillon; Bryan Baranowski; Kathy Wolski; Bruce D Lindsay; Oussama M Wazni; Khaldoun G Tarakji
Journal:  J Am Coll Cardiol       Date:  2018-03-10       Impact factor: 24.094

5.  Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy.

Authors:  Maja Cikes; Sergio Sanchez-Martinez; Brian Claggett; Nicolas Duchateau; Gemma Piella; Constantine Butakoff; Anne Catherine Pouleur; Dorit Knappe; Tor Biering-Sørensen; Valentina Kutyifa; Arthur Moss; Kenneth Stein; Scott D Solomon; Bart Bijnens
Journal:  Eur J Heart Fail       Date:  2018-10-17       Impact factor: 15.534

6.  Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram.

Authors:  Zachi I Attia; Suraj Kapa; Francisco Lopez-Jimenez; Paul M McKie; Dorothy J Ladewig; Gaurav Satam; Patricia A Pellikka; Maurice Enriquez-Sarano; Peter A Noseworthy; Thomas M Munger; Samuel J Asirvatham; Christopher G Scott; Rickey E Carter; Paul A Friedman
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

Review 7.  Challenges in personalised management of chronic diseases-heart failure as prominent example to advance the care process.

Authors:  Hans-Peter Brunner-La Rocca; Lutz Fleischhacker; Olga Golubnitschaja; Frank Heemskerk; Thomas Helms; Thom Hoedemakers; Sandra Huygen Allianses; Tiny Jaarsma; Judita Kinkorova; Jan Ramaekers; Peter Ruff; Ivana Schnur; Emilio Vanoli; Jose Verdu; Bettina Zippel-Schultz
Journal:  EPMA J       Date:  2016-01-30       Impact factor: 6.543

Review 8.  Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling.

Authors:  Chris D Cantwell; Yumnah Mohamied; Konstantinos N Tzortzis; Stef Garasto; Charles Houston; Rasheda A Chowdhury; Fu Siong Ng; Anil A Bharath; Nicholas S Peters
Journal:  Comput Biol Med       Date:  2018-10-18       Impact factor: 4.589

9.  Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.

Authors:  Nicolas Coudray; Paolo Santiago Ocampo; Theodore Sakellaropoulos; Navneet Narula; Matija Snuderl; David Fenyö; Andre L Moreira; Narges Razavian; Aristotelis Tsirigos
Journal:  Nat Med       Date:  2018-09-17       Impact factor: 53.440

10.  Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks.

Authors:  Byoung Geol Choi; Seung Woon Rha; Suhng Wook Kim; Jun Hyuk Kang; Ji Young Park; Yung Kyun Noh
Journal:  Yonsei Med J       Date:  2019-02       Impact factor: 2.759

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