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Updated: Jul 25, 2025

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设计思维的应用在开发用于部骨折检测的深度学习算法中的应用.

Chun-Hsiang Ouyang1, Chih-Chi Chen2, Yu-San Tee1

  • 1Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan.

Bioengineering (Basel, Switzerland)
|June 28, 2023
PubMed
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设计思维通过专注于临床需求,改善了创伤护理的深度学习 (DL) 算法. 这种以用户为中心的方法提高了从骨盆X射线检测股骨骨折的诊断准确性.

科学领域:

  • 医疗成像医学成像
  • 医疗保健中的人工智能
  • 临床决策支持 临床决策支持

背景情况:

  • 深度学习 (DL) 在临床实践中表现有前途,但面临着整合挑战.
  • 设计思维提供了一个以用户为中心的解决问题的框架,适用于医疗保健.

研究的目的:

  • 应用设计思维原则来开发和完善临床使用的DL算法.
  • 提高创伤护理DL算法的性能和临床适用性.

主要方法:

  • 设计思维被采用,涉及采访临床医生,以了解需求.
  • 使用Xception卷积神经网络开发了一个DL算法,用于从骨盆X射线 (PXRs) 检测骨折.
  • 在整合设计思维见解之前和之后,DL模型的性能进行了比较.

主要成果:

  • 该研究确定了减少在紧急情况下错误诊断大腿骨折的必要性.
  • 使用了4235个PXRs的数据集,其中2146个 (51%) 显示关节骨折.
  • 设计思维整合使诊断准确度从0.91提高到0.95,灵敏度从0.97提高到0.97,特异性从0.84提高到0.93.

结论:

  • 设计思维确保DL解决方案以创伤护理为中心.
关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.设计思维 设计思维部骨折 骨折 部骨折 部骨折一个创伤的创伤创伤.

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  • 这种方法有效地满足了患者和医疗保健提供者的需求.
  • 该研究强调了设计思维在优化临床环境中DL部署方面的价值.