针对诊断肺结核的机器学习模型准确性的系统审查:对护理实践和实施的影响
Kewalin Pongsuwun1, Wimolrat Puwarawuttipanit1, Sunisa Nguantad2
1Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand.
Nursing & health sciences
|March 9, 2025
概括
机器学习 (ML) 模型显示,使用诸如胸部X射线和呼吸分析等功能诊断肺结核是有前途的. 这些人工智能工具可以改善早期检测,并指导护理策略,以获得更好的患者结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 肺部医学 肺部医学
背景情况:
- 肺结核 (TB) 诊断仍然是一个挑战,需要先进的方法来早期和准确的检测.
- 机器学习 (ML) 的整合提供了一种新的方法来提高结核病治疗的诊断准确性和效率.
- 护理实践和实施策略可以通过ML驱动的诊断见解得到显著的信息.
研究的目的:
- 系统地审查ML模型在诊断肺结核中的应用.
- 评估ML在告知护理实践和实施结核病诊断策略方面的潜力.
- 确定用于结核病检测的关键诊断特征和ML算法.
主要方法:
- 在主要数据库 (Scopus,PubMed,Medline等) 进行系统的文献搜索. 对于在2019年至2024年之间发表的研究.
- 遵守PRISMA指南,严格选择研究,并采用详细的纳入和排除标准.
- 综合了来自12项包括研究的数据,以确定共同的诊断特征和ML算法.
主要成果:
- 在选了734个记录后,最终分析包括了12项研究.
- 用于ML模型的关键诊断特征包括胸部X射线,CT扫描,唾液涂抹图像,呼出的呼吸和个人信息.
- 卷积神经网络 (CNN) 是最常用的ML算法,在分析成像数据方面表现出卓越的性能.
结论:
- 机器学习技术在提高肺结核早期诊断方面具有变革性的潜力.
- 基于机器学习的洞察力可以优化护理实践,从而改善临床决策和患者管理.
- 这些发现支持进一步开发和实施结核病诊断中的ML工具,以改善患者的治疗结果.
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