Integration of Artificial Intelligence (AI) and Distributed Ledger Technologies to Improve Interpretability and Reportability for Point-of-Care Medica

登革病毒(DENV)是一种通过蚊子传播的病毒性疾病，其全球发病率在过去十年中急剧上升，目前的估计表明，全球每年的发病率高达3.9亿人。纸基微流控装置由于其相对较小的尺寸(大约几毫米到两厘米的长度和宽度)、便携性和相对便宜的成本，已经显示出作为现场使用的护理点设置的各种成功。然而，大多数Upad设备仍然无法被非专业用户解读。病毒的感染倾向被低估，以及这些地方性病毒发生在低资源环境中，导致求医率较低，这加剧了这个问题。使用人工智能(AI)和分布式分类账技术的集成系统可以与微流控技术一起解决这些痛点。库珀教授的研究小组最近开发了一个具有区块链通信的AI平台，用于在乌干达检测疟疾。这项研究使用了监督学习算法，通过照片和基于CAN的学习平台的链接来识别积极和消极的结果。然而，区块链技术的使用是能源密集型的，而且连接到云进行数据分析需要持续的网络访问。在这个项目中，我们将建立新的分布式分类帐架构，支持边缘计算(在无需网络访问的情况下执行分析的设备上)。新平台将与DHIS2等国家数字卫生平台相结合，自动创建一个(去)集中监测系统，改善以前发现的官方报告不佳的问题，这有可能导致新的公共卫生措施(例如减少媒介人口)。这一综合系统的可用性的提高可能会提高可解释性，即使对于其专长不在病毒感染本身的医学专家(例如，护士或全科医生等普通卫生工作者)也是如此。总体而言，这将增加针对登革热病毒未来地方性暴发的医疗系统管理的稳健性。

Dengue virus (DENV) is a mosquito-borne viral disease whose global incidence has increased dramatically in the past decade with current estimates indicating an incidence level of up to 390 million infections per year globally. Paper-based microfluidic devices have shown various successes as a point-of-care settings for field use due to their relatively small size (approximately a few mm to 2 cm in length and width), portability and relatively inexpensive cost. However, most uPAD devices are still unable to be interpreted by non-professional users. This problem is compounded by the virus' infections propensity to be under-reported and the low-resource settings these endemic viruses occur in leading to poor rates in seeking medical treatment.An integrated system using artificial intelligence (AI) and distributed ledger technologies could be utilized to tackle these pain points together with microfluidics. Professor Cooper's research group has recently developed an AI platform with blockchain communication for the detection of malaria in Uganda. The research used supervised learning algorithms to identify positive and negative results via photograph with links to could-based learning platform. However, the use of blockchain technologies is energy-intensive and the link to the cloud for data analysis requires constant network access. In this project, we will establish new distributed ledger architectures enabling edge computing (on the device performing the assay without the need for network access). The new platform will be integrating with national digital health platforms, such as DHIS2, to create a (de-)centralized surveillance system automatically, improving the previous identified problem of poor official reporting, which has the potential to lead to new public health measures (e.g. to reduce vector populations). The improved usability of this integrated system would potentially improve interpretability even for medical experts whose point of expertise lie outside of the viral infection itself (e.g., general health workers such as nurses or general practitioners). Overall, this would increase the robustness of healthcare systems management for dengue virus for future endemic outbreaks.