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In JACC. Cardiovascular imaging

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.

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


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