用人工智能软件 (SPOT-TB) 识别的热点在巴基斯坦发现结核病例的地理定位:实用阶梯集群随机对照试验的研究协议
Syed Mohammad Asad Zaidi1, Amna Mahfooz2, Abdullah Latif3
1WHO Centre for Tuberculosis Research and Innovation, Institute for Global Health, University College London, London, UK syed.zaidi.22@ucl.ac.uk.
BMJ open respiratory research
|July 11, 2024
概括
这项研究测试了人工智能 (AI) 软件是否可以改善巴基斯坦结核病例的发现. 通过人工智能地理定位活跃病例发现 (ACF) 显示出有希望的结果,提高了结核病检测产量.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗保健中的人工智能
背景情况:
- 巴基斯坦已经扩大了结核病 (TB) 的活跃病例发现 (ACF),但产量低于预期.
- 结核病发病率的地理差异表明,有针对性的ACF可以提高计划的有效性.
- 该SPOT-TB研究解决了改善结核病例检测策略的需要.
研究的目的:
- 评估以人工智能 (AI) 软件 (MATCH-AI) 为指导的地理目标ACF方法是否可以提高巴基斯坦的结核病检测产量.
- 为了比较人工智能驱动的ACF选址选择与传统方法的有效性.
主要方法:
- 一个务实的,阶梯集群随机试验,涉及30个移动X射线单元.
- 干预区域使用MATCH-AI软件进行分区结核病流行模型和热点识别,用于ACF地点选择.
- 控制区使用基于历史数据和工作人员专业知识的现有ACF选址方法.
主要成果:
- 主要结局是干预和对照区域之间检测到的细菌学证实事件结核病例的差异.
- 分析将集中在针对人工智能与传统目标地点的ACF收益率上.
- 所有其他ACF程序在两种方法中都保持标准化.
结论:
- 这项研究旨在提供关于人工智能引导的,地理定向ACF在改善结核病例发现方面的有效性的证据.
- 调查结果将为政策变化提供信息,并优化对巴基斯坦和类似地区的结核病控制计划的资源配置.
- 成功实施可能会导致更高效和有效的策略,以全球打击结核病.
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