基于人工智能的决策支持系统在压力和失禁相关皮肤炎管理中以护理为中心的开发 - 混合方法研究
Khalid Majjouti1, Vanessa Priester2, Michaela Tapp-Herrenbrueck3
1Department of Nursing Development and Nursing Research, University Hospital Essen, Essen, Germany. khalid.majjouti@uk-essen.de.
BMC nursing
|July 2, 2025
概括
这项研究开发了一个人工智能系统,以帮助护士区分压力/伤害 (PU/PI) 和失禁相关皮肤炎 (IAD). 人工智能整合了临床数据和伤口图像,提高了伤口护理中的诊断准确性.
科学领域:
- 护理科学 护理科学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 在第一或第二阶段的压力/伤害 (PU/PI) 和失禁相关皮肤炎 (IAD) 之间的区别是具有挑战性的,因为视觉上的相似性.
- 错误的评估可能导致治疗延迟和不适当的干预.
- 该KIADEKU项目旨在支持PU/PI和IAD评估和文档使用基于AI的应用程序在护理护理.
研究的目的:
- 研究将护理科学和临床实践证据集成到用于伤口评估的AI系统中.
- 开发基于人工智能的应用程序,以支持护士区分PU/PI和IAD.
- 通过人工智能在护理中加强基于证据的护理的实施.
主要方法:
- 进行了对伤口评估护理标准的文献综述.
- 通过护理专家对人工智能培训数据注释进行代评估,定义了一个最小数据集 (MDS).
- 收集并验证了来自大学医院的1521张伤口图像的数据集,并通过多次专家评估,遵守TRIPOD-AI指南.
主要成果:
- 经过验证的MDS包括18个与伤口相关的和4个病因学类别,整合了患者的视觉和上下文数据.
- 人工智能系统将伤口图像与患者的分类信息结合起来进行评估.
- 可靠性分析显示,关于伤口类型分类 (α=0.64) 的基本一致性,以及关于PU/PI (α=0.57) 和IAD (α=0.27) 分类的中等到中等一致性.
结论:
- 综合护理科学和实践证据的混合方法方法为人工智能系统奠定了坚实的基础.
- 该研究展示了对常规护理数据的创新性使用,用于人工智能在伤口护理方面的培训.
- 开发的AI系统通过改善医疗保健专业人员的诊断支持来推进AI驱动的伤口护理解决方案.
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