Automated Mobile Microscopy for Malaria Diagnosis and surveillance in Uganda

项目摘要 疟疾是发展中国家的主要健康问题之一。疟疾地方性有 被归因于实验室层面的糟糕诊断。这常常会导致疾病误诊。 以及抗药性。许多发展中国家面临缺乏临界质量实验室的问题。 技术人员通过显微镜的黄金标准机制来诊断疾病 使其中一些国家本已严峻的局势雪上加霜。全球风潮 现在的技术是基于机器学习和深度学习技术的。这些可以是 与智能手机相结合，提高疾病诊断水平。然而，大多数 以前关于显微镜诊断自动化的工作已经在 实验室环境下，似乎没有研究给出一个实用的现场可部署的解决方案。的目标是 拟研究的是开发一种快速、低成本、准确、简单的现场筛选系统 以应对像疟疾这样的显微镜挑战。具体地说，这项研究将测试和验证开发的 用于疟疾实时现场诊断和监测的图像分析模型。这个 建议的解决方案建立在我们之前在Makerere进行的移动显微镜工作的基础上 大学人工智能实验室，通过利用最近的 3D打印技术的进步使低成本3D打印适配器的开发成为可能。 这使得广泛的智能手机能够连接到显微镜上，而且，我们 实施了用于病原体检测的深度学习模型，以产生有效的硬件 和软件。 我们工作的软件部分是训练机器学习方法来识别不同的 病原体物体。然而，诊断解决方案在其当前状态下一直是特别的，其中 不同的条件设置，如图像缩放、手机分辨率和网格读数 在现场测试中部署时，该模型标准化，因此性能较差。我们的 因此，内场自动筛查试验将通过1)实现稳健的结果 用于标准化现场显微镜的机器学习方法的发展 乌干达的疟疾诊断。2)构建实时互补框架 通过现场诊断对疟疾平台进行监测和改进诊断 学习。这里提出的基于现场护理点的诊断系统解决了一个主要的 未得到满足的公共卫生疟疾筛查和监测需要可靠地为公共卫生提供信息 疟疾控制和预防的干预措施。

Project Summary Malaria is one of the leading health problems of the developing world. Malaria endemicity has been attributed to poor diagnosis at the lab level. This quite often leads to disease misdiagnosis and drug resistance. Many developing countries are faced with a lack of critical mass of lab technicians to diagnose the disease through a gold standard mechanism of microscopy and this has worsened the already dire situation in some of these Countries. World over the trending technologies are now based on machine learning and deep learning techniques. These can be leveraged with the combination of smartphones to improve disease diagnosis. However, most of the previous work on automation for microscopy diagnosis has been carried out adhocly in the lab environment and no study seems to give a practical field deployable solution. The goal of the proposed research is to develop a rapid, low cost, accurate and simple in-field screening system for microscopy challenges like malaria. Specifically, this study will test and validate developed image analysis models for real time field-based diagnostics and surveillance of malaria. The proposed solution builds from our earlier work on mobile microscopy carried out at Makerere University AI Lab, that has confronted automated microscopy through exploiting recent technological advances in 3D printing to enable development of a low-cost 3D printed adapter. This has enabled attachment of a wide range of Smartphones on a microscope, furthermore, we have implemented deep learning models for pathogen detection to produce effective hardware and software respectively. The software component of our work is to train machine learning methods to recognise different pathogen objects. The diagnosis solutions have however been ad-hoc in its current state where different conditional settings like image scaling, phone resolutions and grid readings were not standardized and therefore poor performance of the model when deployed in field testing. Our Infield automated screening trials will therefore involve achieving robust outcomes, through 1) Development of machine learning approaches for standardised field-based microscopy of malaria diagnosis in Uganda. 2) Building a complementary framework for real time surveillance and improved diagnosis of malaria platform through in-field diagnostic studies. The point-of-care field-based diagnostic system proposed here addresses a major unmet public health malaria screening and surveillance need to reliably inform public health interventions in malaria control and prevention.