在医学成像中深度学习的可重现性检查清单
Mana Moassefi1, Yashbir Singh1, Gian Marco Conte1
1Mayo Clinic Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
Journal of imaging informatics in medicine
|March 14, 2024
概括
研究人员使用Delphi方法开发了一份26项检查清单,以提高医学成像中的深度学习 (DL) 模型的可重复性和可靠性. 本检查清单增强了医疗保健中DL应用程序的透明文档.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 可重复的研究可重复性研究
背景情况:
- 深度学习 (DL) 在医学预后,诊断和治疗规划方面提供了变革性的潜力.
- 透明的文档对于提高可复制性和完善医学中的DL技术至关重要.
- 现有的报告准则可能无法完全解决医疗成像中DL的独特挑战.
研究的目的:
- 开发一个全面和验证的检查清单,用于报告医学成像中的深度学习模型.
- 提高DL应用在医疗领域的可复制性和可靠性.
- 为医疗保健中使用的DL模型提供标准化框架.
主要方法:
- 使用了经过修改的Delphi方法,涉及11名医学成像和DL专家.
- 从现有的文献和指南中编制了一个初步的检查清单.
- 使用利克特尺度和内容有效率比率 (CVR) 进行了两轮调查,以改进项目并达成共识.
主要成果:
- 德尔菲过程导致了报告DL模型的最终26项检查清单.
- 检查清单显示了高面和内容有效性,CVR截止值为0.59.
- 通过专家的共识,最终的检查清单得到了完善,排除了非必要的项目,并纳入了新的建议.
结论:
- 开发的26项检查清单促进了医疗成像中DL工具的可重复报告.
- 这种标准化检查清单使科学家能够复制研究结果,并提高DL应用的可靠性.
- 检查清单解决了在医疗DL的快速发展领域需要透明文件的需求.
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