Literature DB >> 30571349

From Machine Learning to Artificial Intelligence Applications in Cardiac Care.

David Tsay1, Cam Patterson2.   

Abstract

Artificial intelligence offers the potential for transformational advancement in cardiovascular care delivery, yet practical applications of this technology have yet to be embedded in clinical workflows and systems. Recent advances in machine learning algorithms and accessibility to big data sources have created the ability for software to solve highly specialized problems outside of health care, such as autonomous driving, speech recognition, and game playing (chess and Go), at superhuman efficiency previously not thought possible. To date, high-order cognitive problems in cardiovascular research such as differential diagnosis, treatment options, and clinical risk stratification have been difficult to address at scale with artificial intelligence. The practical application of artificial intelligence in the underlying operational processes in the delivery of cardiac care may be more amenable where adoption has great potential to fundamentally transform care delivery while maintaining the core quality and service that our patients demand. In this article, we provide an overview on how these artificial intelligence platforms can be implemented to improve the operational delivery of care for patients with cardiovascular disease.

Entities:  

Keywords:  artificial intelligence; echocardiography; model; tomography

Mesh:

Year:  2018        PMID: 30571349     DOI: 10.1161/CIRCULATIONAHA.118.031734

Source DB:  PubMed          Journal:  Circulation        ISSN: 0009-7322            Impact factor:   29.690


  10 in total

Review 1.  Artificial Intelligence in radiotherapy: state of the art and future directions.

Authors:  Giulio Francolini; Isacco Desideri; Giulia Stocchi; Viola Salvestrini; Lucia Pia Ciccone; Pietro Garlatti; Mauro Loi; Lorenzo Livi
Journal:  Med Oncol       Date:  2020-04-22       Impact factor: 3.064

2.  Parental Attitudes toward Artificial Intelligence-Driven Precision Medicine Technologies in Pediatric Healthcare.

Authors:  Bryan A Sisk; Alison L Antes; Sara Burrous; James M DuBois
Journal:  Children (Basel)       Date:  2020-09-20

Review 3.  Looking back and thinking forwards - 15 years of cardiology and cardiovascular research.

Authors:  Jonathan M Kalman; Sergio Lavandero; Felix Mahfoud; Matthias Nahrendorf; Magdi H Yacoub; Dong Zhao
Journal:  Nat Rev Cardiol       Date:  2019-09-30       Impact factor: 32.419

4.  Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.

Authors:  Quincy A Hathaway; Skyler M Roth; Mark V Pinti; Daniel C Sprando; Amina Kunovac; Andrya J Durr; Chris C Cook; Garrett K Fink; Tristen B Cheuvront; Jasmine H Grossman; Ghadah A Aljahli; Andrew D Taylor; Andrew P Giromini; Jessica L Allen; John M Hollander
Journal:  Cardiovasc Diabetol       Date:  2019-06-11       Impact factor: 9.951

Review 5.  Translational AI and Deep Learning in Diagnostic Pathology.

Authors:  Ahmed Serag; Adrian Ion-Margineanu; Hammad Qureshi; Ryan McMillan; Marie-Judith Saint Martin; Jim Diamond; Paul O'Reilly; Peter Hamilton
Journal:  Front Med (Lausanne)       Date:  2019-10-01

6.  Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling.

Authors:  Khondker Mohammad Zobair; Louis Sanzogni; Luke Houghton; Md Zahidul Islam
Journal:  PLoS One       Date:  2021-09-24       Impact factor: 3.240

7.  Digital heart for life.

Authors:  Yin Hua Zhang
Journal:  Korean J Physiol Pharmacol       Date:  2019-08-26       Impact factor: 2.016

Review 8.  Aspirin for primary prevention of cardiovascular disease: Advice for a decisional strategy based on risk stratification.

Authors:  Alberto Aimo; Raffaele De Caterina
Journal:  Anatol J Cardiol       Date:  2020-01       Impact factor: 1.596

9.  Machine learning algorithms for predicting coronary artery disease: efforts toward an open source solution.

Authors:  Aravind Akella; Sudheer Akella
Journal:  Future Sci OA       Date:  2021-03-29

10.  Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey.

Authors:  Alison L Antes; Sara Burrous; Bryan A Sisk; Matthew J Schuelke; Jason D Keune; James M DuBois
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-20       Impact factor: 2.796

  10 in total

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