Constructing a software platform by harnessing emerging data science tools for improved analytics and monitoring of female genital schistosomiasis and

该项目的目标是开发一个开源的即时数据平台，将自动视觉检查技术和个人时空风险概况相结合，以协助女性生殖器血吸虫病（FGS）诊断决策。四个较小的工作包，专注于低资源密集型数据收集/聚合，机器学习和贝叶斯网络，将全部纳入最后的第五个包，以开发即时护理平台。就传染病对公共卫生的影响而言，血吸虫病仅次于疟疾，而FGS是一种特别被忽视的疾病。目前还没有安全、准确和低成本的FGS诊断解决方案，在了解FGS症状和传播动力学方面仍存在差距。本项目完成的工作将通过提供一个供临床医生使用的即时诊断软件平台，包括那些没有接受过阴道镜图像审查专业培训的医生，帮助改善当前的FGS前景。早期和准确地检测到FGS不仅意味着对个体疾病的早期治疗，而且对控制流行地区的社区传播具有积极的影响。第一工作包将侧重于开发和试用獾数据系统。该系统目前处于原型阶段，基于Pimoroni开发的信用卡大小的Badger 2040 W电子墨水设备。该系统将被编程为每日妇科日记，并作为试点纵向研究的一部分提供给女性，嵌套在Schista！在赞比亚进行研究，以调查整个月经周期中FGS的疾病动态和进展。这一试点项目的产出不仅将促进对森林全球统计的理解，而且还将导致开发一个可在各种情况下应用的开放源码数据收集系统。FGS可以通过阴道镜对子宫颈进行目视检查来识别。工作包二（WP 2）和三（WP 3）将涉及改进阴道镜图像预处理（WP 2）的技术，然后构建用于FGS识别和分类的机器学习框架（WP 3），基本上使计算机能够在阴道镜图像中“看到”FGS。这个过程被称为自动视觉检查（AVE）。AVE的各种深度学习方法（卷积神经网络（CNN），ResNET，Deep SVDD）将在WP 3中进行试验，并根据灵敏度和特异性性能选择最终的数据平台。在预处理（WP 2）方面，WP 3中的机器学习将只与用于训练的图像一样好，因此质量和体积都很重要。为了增加训练图像的容量，将评估AI图像增强和像素放大软件在放大阴道镜图像方面的准确性。如果发现增强是真实的，那么这些图像将被馈送到WP 3中，以增加软件平台的灵敏度和特异性。该升级软件还可以帮助准确处理低分辨率图像，以便可以使用手持成像设备（智能手机，数码相机，手持阴道镜）。在工作包4（WP 4）中，为了加强AVE的结果，将收集来自各种来源的数据，包括WP 1的结果，以进一步提高模型的灵敏度。这些变量将包括个体人口统计学、时间、空间和环境数据，并将使用各种回归技术和地理信息系统（例如，ArcGIS）。为了破解疾病传播的复杂网络，解释变量将被整合到贝叶斯网络中，以产生一个风险模型，该模型将被整合到软件平台中，以进行更准确的诊断分类。

The goal of this project is to develop an open-source, point-of-care data platform that combines automated visual examination technology and individual spatio-temporal risk profiles to assist in female genital schistosomiasis (FGS) diagnostic decision-making. Four smaller work packages, focussing on low-resource intensive data collection/aggregation, machine learning and Bayesian networks, will all feed into a final fifth package to develop the point-of-care platform. Schistosomiasis is second only to malaria in terms for infectious disease public health impact, and FGS is a particularly neglected form of the disease. Safe, accurate, and low-cost diagnostic solutions for FGS are not readily available, and there are still gaps in understanding FGS symptoms and transmission dynamics. The work completed in this project will help improve the current FGS landscape by providing a point-of-care diagnostic software platform to be used by clinicians, including those not speciality trained to review colposcope images. Early and accurate detection of FGS not only means earlier treatment of the disease in an individual but also has positive ramifications for controlling community transmission in endemic areas. Work package one (WP1) will focus on developing and trialling the Badger Data System (BDS). This system is currently at a prototype phase, based on the credit card-sized Badger 2040W E-ink device developed by Pimoroni. This system will be programmed as a daily symptomology diary and given to women as part of a pilot longitudinal study, nested within the Schista! Study in Zambia, to investigate disease dynamics and progression of FGS throughout the menstrual cycle. Not only will the output of this pilot further the understanding of FGS, but it will also result in the development of an open-source data collection system that can be applied in a range of contexts. FGS can be identified through visual examination of the cervix using a colposcope. Work package two (WP2) and three (WP3) will involve refining techniques for colposcope image pre-processing (WP2) and then building a machine learning framework (WP3) for FGS identification and classification, essentially giving a computer the ability to 'see' FGS in a colposcope image. This process is known as automated visual examination (AVE). Various deep learning methods for AVE (convolutional neural networks (CNN), ResNET, Deep SVDD) will be trialled in WP3 and selected for the final data platform based on sensitivity and specificity performance. In regards to the pre-processing (WP2), the machine learning in WP3 will only be as good as the images used for training so both quality and volume are important. To increase the volume of training images, AI-powered image enhancement and pixel upscaling software will be assessed for their accuracy in upscaling colposcope images. If the enhancement is found to be true to life, then these images will be fed into WP3 to increase the sensitivity and specificity of the software platform. This upscaling software may also assist with accurately processing lower-resolution images so that handheld imaging devices (smartphones, digital cameras, handheld colposcopes) can be used.In work package four (WP4), to strengthen the results of the AVE, data from various sources, including the results of WP1, will be collected on variables to increase model sensitivity further. These variables will include individual demographics, temporal, spatial, and environmental data and will be assessed for their association with FGS using various regression techniques and geographic information systems (Eg. ArcGIS). To decode the complex web of disease transmission, the explanatory variables will be integrated into a Bayesian network to produce a risk model, which will be integrated into the software platform for more accurate diagnostic classifications.