基于人工智能的数字病理学的全面评估,以监测大规模的除虫计划,以防止土壤传播的虫:研究协议
Peter K Ward1,2,3, Sara Roose1, Mio Ayana4
1Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium.
PloS one
|October 28, 2024
概括
一个新的人工智能系统 (AI-DP) 正在评估,以改善土壤传播虫 (STH) 诊断. 这项研究评估了其性能,成本效益和用于除虫程序的可用性.
科学领域:
- 被忽视的热带疾病被忽视
- 诊断病理学的诊断病理学
- 人工智能在医学中的应用
背景情况:
- 手动Kato-Katz (KK) 涂抹查是监测土壤传播虫 (STHs) 的标准.
- 一个基于人工智能的数字病理学 (AI-DP) 系统已被开发用于KK涂抹分析.
- 预先的AI-DP性能数据令人鼓舞,需要进一步验证.
研究的目的:
- 对世界卫生组织 (WHO) 目标产品配置文件进行AI-DP系统的全面评估.
- 评估AI-DP的诊断性能,可重复性,时间到结果,成本效益和可用性.
- 确定AI-DP是否可以作为STH控制计划的成本高效,端到端的诊断工具.
主要方法:
- 一项研究方案旨在评估AI-DP系统的五个关键属性.
- 实验旨在验证AI-DP (KK2.0) 与手动KK查 (KK1.0) 相比,AI-DP (KK2.0) 的非劣势性.
- 该研究将在埃塞俄比亚和乌干达进行,重点关注在除虫计划中的学龄儿童.
主要成果:
- 这项研究旨在提供数据,以基于证据的决定AI-DP的适合性STH计划的数据.
- 该协议旨在收集高质量的数据,以支持世卫组织的批准.
- 结果将告知AI-DP在资源有限的环境中诊断其他被忽视的热带疾病的潜力.
结论:
- 该研究将产生关键数据,以验证用于STH监测的AI-DP系统.
- 成功验证可能会导致世卫组织批准和更广泛的采用.
- 将传播方法和统计数据,以促进其他疾病的AI-DP开发.
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