Evidence map›Paper›PMID 32912474›Full record

ReviewJACC. Cardiovascular imaging2020

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 and 14 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JACC. Cardiovascular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
114citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

114 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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54 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Partho P SenguptaWest Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia. Electronic address: Partho.Sengupta@wvumedicine.org.
Sirish ShresthaWest Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia.
Béatrice BerthonPhysique pour la Médecine Paris, Inserm U1273, CNRS FRE 2031, ESPCI Paris, PSL Research University, Paris, France.
Emmanuel MessasUniversité Paris Descartes, Sorbonne Paris Cité, Paris, France.
Erwan DonalDépartement de Cardiologie et Maladies Vasculaires, Service de Cardiologie et maladies vasculaires, CHU Rennes, Rennes, France.
Geoffrey H TisonDivision of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California.
James K MinCleerly, Inc., New York, New York.
Jan D'hoogeLaboratory on Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Jens-Uwe VoigtDepartment of Cardiovascular Science, KU Leuven, Leuven, Belgium; Department of Cardiovascular Diseases, University Hospitals Leuven, Belgium.
Joel DudleyDepartment of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
Johan W VerjansAustralian Institute for Machine Learning, University of Adelaide, North Terrace, Adelaide, South Australia, Australia; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
Khader ShameerDepartment of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
Kipp JohnsonDepartment of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
Lasse LovstakkenDepartment of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
Mahdi TabassianLaboratory on Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Marco PiccirilliWest Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia.
Mathieu PernotPhysique pour la Médecine Paris, Inserm U1273, CNRS FRE 2031, ESPCI Paris, PSL Research University, Paris, France.
Naveena YanamalaWest Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia.
Nicolas DuchateauCREATIS, CNRS UMR 5220, INSERM U1206, Université Lyon 1, INSA-LYON, France.
Nobuyuki KagiyamaWest Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia.
Olivier BernardCREATIS, CNRS UMR 5220, INSERM U1206, Université Lyon 1, INSA-LYON, France.
Piotr SlomkaDepartment of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California.
Rahul DeoDivision of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California.
Rima ArnaoutDivision of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California.

Funding

Toward efficient performance for deep learning on medical imagingR01HL150394 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Rima Arnaout · 2020 to 2026
$6.2M
Genetics and Structure of Trabecular Myocardium in Development and DiseaseK08HL125945 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ARNAOUT, RIMA · 2015 to 2019
$863k
NHLBI NIH HHS K08 HL125945NHLBI NIH HHS R01 HL150394
6 · The paper itself

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.

Indexed as

CardiologyChecklistDelivery of Health CareHumansMachine LearningPredictive Value of TestsUnited Statesartificial intelligencecardiovascular imagingchecklistdigital healthmachine learningreporting guidelinesreproducible research

Identifiers

PMID32912474
PMCPMC7953597

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.