我们可以通过保持潘多拉的盒子关闭来彻底改变诊断成像吗?
Thomas C Kwee1, Derya Yakar1, Tim E Sluijter1
1Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
The British journal of radiology
|October 31, 2023
概括
偶然的成像发现往往导致不必要的护理. 一项新的策略建议使用深度学习来避免可视化这些发现,从而有可能提高诊断成像价值.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 医疗保健管理的管理
背景情况:
- 偶然的成像发现在医疗保健中构成了重大挑战,经常导致低价值和潜在有害的患者护理.
- 当前的实践很难管理一旦发现的偶然发现,因为医疗保健专业人员往往无法忽视它们.
研究的目的:
- 反思当前管理偶然成像发现的做法.
- 提出并讨论一种新的策略,以预防性地解决诊断成像中的偶然发现.
主要方法:
- 这篇文章是一篇意见稿,反映了现有的做法.
- 它提出了一个新的策略,以防止偶然发现的可视化为中心.
- 深度学习算法被建议作为实现这种预防的方法.
主要成果:
- 拟议的战略旨在通过不想象偶然发现来"保持潘多拉的盒子关闭".
- 这种方法利用深度学习算法进行先发制人的管理.
- 该概念有可能对诊断成像实践产生重大影响.
结论:
- 偶然发现带来了相当大的问题,往往导致低价值的护理.
- 提出了一种使用深度学习的新策略,以避免可视化偶然发现.
- 这种方法可以通过防止不必要的干预来彻底改变诊断成像.
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