Automated Mobile Microscopy for Malaria Diagnosis and surveillance in Uganda

在乌干达使用自动移动显微镜进行疟疾诊断和监测

• 批准号: 10713248
• 负责人: Rose Nakasi
• 金额: $ 26.22万
• 依托单位: MAKERERE UNIVERSITY COLLEGE OF HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713248
• 关键词: 3D Print; Address; Africa; Artificial Intelligence; Automation; Calibration; Cellular Phone; Cessation of life; Communicable Diseases; Complement; Computer software; Country; Coupling; Data Science; Data Sources; Developing Countries; Development; Diagnosis; Diagnostic; Disease; Disease Surveillance; Drug resistance; Environment; Equipment; Goals; Health; Image; Image Analysis; Individual; Location; Machine Learning; Malaria; Malaria Diagnosis; Maps; Methods; Microscope; Microscopy; Modeling; Outcome; Pathogen detection; Performance; Pilot Projects; Prevention; Probability; Public Health; Reporting; Research; Resolution; Resources; Risk; Stains; Standardization; System; Techniques; Technology; Telephone; Testing; Time; Training; Uganda; Universities; Update; Work; cost; deep learning; deep learning model; design; diagnostic platform; disease diagnosis; disorder risk; experience; field study; implementation intervention; improved; innovation; machine learning method; meter; novel; pathogen; point of care; public health intervention; real time model; screening; tool; trend

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.

项目摘要 疟疾是发展中国家的主要健康问题之一。疟疾中流有 归因于实验室诊断不良。这常常导致疾病误诊 和耐药性。许多发展中国家面临缺乏实验室的关键质量 通过显微镜的黄金标准机制诊断疾病的技术人员，这 在其中一些国家，已经恶劣的情况恶化了。世界上的趋势 技术现在基于机器学习和深度学习技术。这些可以 通过智能手机的组合利用以改善疾病诊断。但是，大多数 先前关于显微镜诊断自动化的工作已在 实验室环境和没有研究似乎提供了实用的现场可部署解决方案。目标的目标 拟议的研究是开发快速，低成本，准确和简单的现场筛查系统 对于疟疾等显微镜挑战。具体而言，这项研究将测试并验证已发达的 实时基于现场诊断和疟疾监测的图像分析模型。这 提议的解决方案是根据我们早期在Makerere进行的移动显微镜检查的工作。 大学AI实验室，通过利用最近的显微镜面对自动显微镜 3D打印中的技术进步，以使低成本印刷适配器能够开发。 这使得在显微镜上的各种智能手机附加了，我们 已经实施了病原体检测的深度学习模型以产生有效的硬件 和软件。 我们工作的软件组成部分是训练机器学习方法以识别不同的 病原体对象。然而 不同的条件设置，例如图像缩放，电话分辨率和网格读数 部署在现场测试中时，模型的标准化，因此模型的性能不佳。我们的 因此，内场自动化筛查试验将涉及实现强大的结果，直到1） 开发用于标准化现场显微镜的机器学习方法 乌干达的疟疾诊断。 2）实时建立一个互补框架 通过现场诊断对疟疾平台的监视和改进的诊断 研究。这里提出的基于护理的基于现场的诊断系统解决了一个主要 未满足的公共卫生疟疾筛查和监视需要可靠地告知公共卫生 疟疾控制和预防的干预措施。