深度学习的应用,以压力损伤阶段化
Han Liu1, Juan Hu2, Jieying Zhou3
1Jiulongpo District People's Hospital, Chongqing, China.
Journal of wound care
|April 29, 2024
概括
一个深度学习系统准确地分阶段压力损伤 (PI),帮助护士在临床实践中. 这种AI工具提高了PI评估效率,并降低了医疗保健成本.
科学领域:
- 医疗技术 医学技术
- 医疗保健中的人工智能
- 伤口护理信息学 伤口护理信息学
背景情况:
- 准确的压力损伤 (PI) 阶段对患者的结果至关重要.
- 由于经验有限,初级和非专业护士经常在PI分期方面扎.
- 现有的PI评估方法可能耗时且容易出现不准确性.
研究的目的:
- 开发和评估基于深度学习的系统,用于自动化PI分期和组织分类.
- 提高临床环境中PI评估的准确性和效率.
- 提供一个具有成本效益的工具,以支持护士在PI管理.
主要方法:
- 使用了1610个临床PI病例的数据集,专家验证了分期和组织标签.
- 训练了一个基于Mask区域的卷积神经网络 (Mask R-CNN),用于对象检测和实例细分.
- 从远程上传的PI照片开发了一个自动分期和组织分类的识别系统.
主要成果:
- 深度学习模型在100个样本的测试组上实现了PI阶段识别的平均精度为0.603.
- 在比较评估中,该模型的性能超过了医疗人员,包括肠胃治疗师.
- 该系统的评价表现与经过专业培训的伤口护理护士的评价表现相当.
结论:
- 开发的深度学习系统为准确和高效的PI分期提供了可行的解决方案.
- 这种低成本的系统可以帮助经验较少的护士克服PI识别方面的挑战.
- 该工具提供了有价值的辅助临床信息,有可能改善患者护理并降低医疗保健费用.
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