Literature DB >> 34362709

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

Jethro C C Kwong1, Louise C McLoughlin1, Masoom Haider2, Mitchell G Goldenberg3, Lauren Erdman4, Mandy Rickard5, Armando J Lorenzo6, Andrew J Hung7, Monica Farcas3, Larry Goldenberg8, Chris Nguan8, Luis H Braga9, Muhammad Mamdani10, Anna Goldenberg11, Girish S Kulkarni12.   

Abstract

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

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Year:  2021        PMID: 34362709     DOI: 10.1016/j.euf.2021.07.004

Source DB:  PubMed          Journal:  Eur Urol Focus        ISSN: 2405-4569


  2 in total

1.  Explainable artificial intelligence to predict the risk of side-specific extraprostatic extension in pre-prostatectomy patients.

Authors:  Jethro C C Kwong; Adree Khondker; Christopher Tran; Emily Evans; Adrian I Cozma; Ashkan Javidan; Amna Ali; Munir Jamal; Thomas Short; Frank Papanikolaou; John R Srigley; Benjamin Fine; Andrew Feifer
Journal:  Can Urol Assoc J       Date:  2022-06       Impact factor: 2.052

2.  The silent trial - the bridge between bench-to-bedside clinical AI applications.

Authors:  Jethro C C Kwong; Lauren Erdman; Adree Khondker; Marta Skreta; Anna Goldenberg; Melissa D McCradden; Armando J Lorenzo; Mandy Rickard
Journal:  Front Digit Health       Date:  2022-08-16
  2 in total

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