Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council.
Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council.
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DOI：
10.1016/j.jcmg.2020.07.015
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发表时间：
2020-09
期刊：
影响因子：
--
通讯作者：
Arnaout R
中科院分区：
文献类型：
--
作者：
Sengupta PP;Shrestha S;Berthon B;Messas E;Donal E;Tison GH;Min JK;D'hooge J;Voigt JU;Dudley J;Verjans JW;Shameer K;Johnson K;Lovstakken L;Tabassian M;Piccirilli M;Pernot M;Yanamala N;Duchateau N;Kagiyama N;Bernard O;Slomka P;Deo R;Arnaout R
机器学习 (ML) 在心脏病学领域的应用越来越广泛，特别是在心血管成像领域。由于机器学习算法固有的复杂性和灵活性，模型性能和解释可能会出现不一致。最近发表了几篇评论文章，为心脏病专家介绍了机器学习的基本原理和临床应用。本文以这些介绍性原则为基础，概述了开发机器学习模型时需要完成的更全面的关键职责列表。本文旨在为参与机器学习研究的研究人员、数据科学家、作者、编辑和审稿人提供科学基础，以统一报告机器学习研究。由机器学习专家、临床医生和统计学家组成的独立多学科小组共同审查了 7 组可能减少算法错误和偏差的要求背后的理论依据。最后，本文将报告项目列表总结为逐项清单，重点介绍了确保正确应用机器学习模型以及模型规范和结果报告一致的步骤。预计研究和开发的快速发展以及现实世界证据可用性的增加可能需要定期更新清单。
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.
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影响因子：
5.4
作者：
Al'Aref, Subhi J.;Singh, Gurpreet;Minutello, Robert M.
通讯作者：
Minutello, Robert M.
影响因子：
7.7
作者：
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Rose, Sherri
影响因子：
9.3
作者：
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影响因子：
5
作者：
Chawla, NV;Bowyer, KW;Kegelmeyer, WP
通讯作者：
Kegelmeyer, WP
影响因子：
10.9
作者：
Avendi, M. R.;Kheradvar, Arash;Jafarkhani, Hamid
通讯作者：
Jafarkhani, Hamid