Deep learning for cardiovascularmedicine: A practical primer

Chayakrit Krittanawong, Kipp W. Johnson, Robert S. Rosenson, Zhen Wang, Mehmet Aydar, Usman Baber, James K. Min, W. H. Wilson Tang, Jonathan L. Halperin, Sanjiv M. Narayan

Research output: Contribution to journalReview articlepeer-review

38 Scopus citations

Abstract

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the 'black-box' criticism), its need for extensive adjudicated ('labelled') data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.

Original languageEnglish (US)
Pages (from-to)2058-2069C
JournalEuropean heart journal
Volume40
Issue number25
DOIs
StatePublished - 2019

Keywords

  • Artificial intelligence
  • Big data
  • Cardiovascular medicine
  • Deep learning
  • Precision medicine

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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