Enaiblers, Uppsala, Sweden.
IDLab, Ghent University - Imec, Zwijnaarde.
Curr Opin Infect Dis. 2024 Oct 1;37(5):376-384. doi: 10.1097/QCO.0000000000001048. Epub 2024 Aug 2.
This opinion piece aims to explore the transformative potential of integrating artificial intelligence with digital microscopy to enhance diagnostics for soil-transmitted helminthiasis (STH) and schistosomiasis (SCH), two pervasive neglected tropical diseases (NTDs). By aligning innovative artificial intelligence-driven solutions with WHO's strategic objectives and calls for better, more accessible, and more integrated diagnostics, we highlight the latest advancements that may support improved health outcomes in affected communities.
The review covers recent advancements in artificial intelligence-based diagnostic technologies, emphasizing automated egg detection and quantification. These technologies promise to mitigate challenges such as human error and the need for skilled technicians.
The findings have significant implications for public health, ethical considerations and regulatory pathways, particularly in resource-limited settings. The authors advocate for interdisciplinary collaboration and a strategic focus on meeting WHO target product profiles to ensure uptake, ultimately to support reaching WHO NTD targets.
综述目的:本文旨在探讨将人工智能与数字显微镜相结合,以提高土壤传播性蠕虫病(STH)和血吸虫病(SCH)诊断的潜力。这两种疾病都是普遍存在的被忽视的热带病(NTD)。通过将创新的人工智能驱动解决方案与世界卫生组织(WHO)的战略目标以及对更好、更易获得和更综合诊断的呼吁相结合,我们强调了可能支持受影响社区改善健康结果的最新进展。
最新发现:综述涵盖了基于人工智能的诊断技术的最新进展,重点介绍了自动检测和定量虫卵的技术。这些技术有望减轻人为错误和对熟练技术人员的需求等挑战。
总结:这些发现对公共卫生、伦理考虑和监管途径具有重大意义,特别是在资源有限的环境下。作者主张进行跨学科合作,并战略性地关注符合世界卫生组织目标产品概况的要求,以确保采用,最终支持实现世界卫生组织的 NTD 目标。