Guo Xin, Khalid Muhammad Arslan, Domingos Ivo, Michala Anna Lito, Adriko Moses, Rowell Candia, Ajambo Diana, Garrett Alice, Kar Shantimoy, Yan Xiaoxiang, Reboud Julien, Tukahebwa Edridah M, Cooper Jonathan M
Division of Biomedical Engineering, The James Watt School of Engineering, University of Glasgow, G12 8LT Glasgow, United Kingdom.
School of Computing Science, University of Glasgow, Glasgow, G12 8RZ, UK.
Nat Electron. 2021 Aug 2;4(8):615-624. doi: 10.1038/s41928-021-00612-x.
In infectious disease diagnosis, results need to be rapidly communicated to doctors once testing has been completed, in order for care pathways to be implemented. This is a challenge when testing in remote low-resource rural communities, in which such diseases often create the largest burden. Here we report a smartphone-based end-to-end platform for multiplexed DNA malaria diagnosis. The approach uses a low-cost paper-based microfluidic diagnostic test, which is combined with deep learning algorithms for local decision support and blockchain technology for secure data connectivity and management. We validate the approach via field tests in rural Uganda, where it correctly identified more than 98% of tested cases. Our platform also provides secure geotagged diagnostic information, which creates the possibility of integrating infectious disease data within surveillance frameworks.
在传染病诊断中，检测完成后需要迅速将结果传达给医生，以便实施治疗方案。在偏远、资源匮乏的农村社区进行检测时，这是一项挑战，因为这些疾病往往在这些社区造成最大的负担。在此，我们报告一种基于智能手机的端到端平台，用于多重DNA疟疾诊断。该方法使用低成本的纸质微流控诊断测试，结合深度学习算法以提供本地决策支持，并利用区块链技术实现安全的数据连接和管理。我们通过在乌干达农村地区的现场测试对该方法进行了验证，在那里它正确识别了超过98%的检测病例。我们的平台还提供安全的带有地理标记的诊断信息，这使得在监测框架内整合传染病数据成为可能。
