Proposed requirements for cardiovascular imaging-related machine learning evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation Council

Partho P. Sengupta, Sirish Shrestha, Béatrice Berthon, Emmanuel Messas, Erwan Donal, Geoffrey H. Tison, James K. Min, Jan D'hooge, Jens Uwe Voigt, Joel Dudley, Johan W. Verjans, Khader Shameer, Kipp Johnson, Lasse Lovstakken, Mahdi Tabassian, Marco Piccirilli, Mathieu Pernot, Naveena Yanamala, Nicolas Duchateau, Nobuyuki KagiyamaOlivier Bernard, Piotr Slomka, Rahul Deo, Rima Arnaout

Research output: Contribution to journalReview articlepeer-review

8 Citations (Scopus)

Abstract

Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.

Original languageEnglish
Pages (from-to)2017-2035
Number of pages19
JournalJACC: Cardiovascular Imaging
Volume13
Issue number9
DOIs
Publication statusPublished or Issued - Sep 2020
Externally publishedYes

Keywords

  • artificial intelligence
  • cardiovascular imaging
  • checklist
  • digital health
  • machine learning
  • reporting guidelines
  • reproducible research

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

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