Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework.

Humans Machine Learning Reproducibility of Results Urology

Journal

European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661

Informations de publication

Date de publication:
07 2021
Historique:
received: 07 07 2021
accepted: 19 07 2021
pubmed: 8 8 2021
medline: 14 4 2022
entrez: 7 8 2021
Statut: ppublish

Résumé

The Standardized Reporting of Machine Learning Applications in Urology (STREAM-URO) framework was developed to provide a set of recommendations to help standardize how machine learning studies in urology are reported. This framework serves three purposes: (1) to promote high-quality studies and streamline the peer review process; (2) to enhance reproducibility, comparability, and interpretability of results; and (3) to improve engagement and literacy of machine learning within the urological community.

Identifiants

DOI: 10.1016/j.euf.2021.07.004 PMID: 34362709
pubmed: 34362709
pii: S2405-4569(21)00183-8
doi: 10.1016/j.euf.2021.07.004
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

672-682

Informations de copyright

Copyright © 2021 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Auteurs

Jethro C C Kwong (JCC)

Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Canada.

Louise C McLoughlin (LC)

Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Canada.

Masoom Haider (M)

Joint Department of Medical Imaging, University of Toronto, Toronto, Canada; AI, Radiomics and Oncologic Imaging Research Lab, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.

Mitchell G Goldenberg (MG)

Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada.

Lauren Erdman (L)

Department of Computer Science, University of Toronto, Toronto, Ontario, Canada; Vector Institute, Toronto, Ontario, Canada.

Mandy Rickard (M)

Division of Urology, Hospital for Sick Children, Toronto, Ontario, Canada.

Armando J Lorenzo (AJ)

Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Division of Urology, Hospital for Sick Children, Toronto, Ontario, Canada.

Andrew J Hung (AJ)

Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA.

Monica Farcas (M)

Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada.

Larry Goldenberg (L)

Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.

Chris Nguan (C)

Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.

Luis H Braga (LH)

Division of Urology, Department of Surgery, McMaster University, Hamilton, Ontario, Canada.

Muhammad Mamdani (M)

Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Canada; Vector Institute, Toronto, Ontario, Canada; Unity Health Toronto, Toronto, Ontario, Canada.

Anna Goldenberg (A)

Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada; Vector Institute, Toronto, Ontario, Canada.

Girish S Kulkarni (GS)

Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Canada. Electronic address: girish.kulkarni@uhn.ca.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Standardized Reporting of Machine Learning...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Standardized Reporting of Machine Learning...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Méthodologie en recherche épidémiologique › Reproductibilité des résultats › Standardized Reporting of Machine Learning...
questionsmedicales.fr › Professions de santé › Médecine › Spécialités chirurgicales › Urologie › Standardized Reporting of Machine Learning...