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。疟疾的流行 都是因为实验室诊断不准确这往往导致疾病误诊 和耐药性。许多发展中国家面临着缺乏足够数量的实验室 技术人员通过显微镜的金标准机制诊断疾病， 使其中一些国家本已严峻的局势更加恶化。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 结合智能手机来提高疾病诊断。但大部分 显微镜诊断自动化的前期工作已在 实验室环境和没有研究似乎给出了一个实际的现场部署的解决方案。的目标 提出的研究是开发一种快速、低成本、准确和简单的现场筛选系统 for microscopic显微镜challenges挑战like malaria疟疾.具体而言，本研究将测试和验证开发的 用于疟疾真实的实时现场诊断和监测的图像分析模型。的 建议的解决方案是基于我们在Makerere进行的移动的显微镜的早期工作 大学人工智能实验室，通过利用最近的 3D打印的技术进步，以实现低成本3D打印适配器的开发。 这使得能够在显微镜上安装各种智能手机，此外，我们 已实施用于病原体检测的深度学习模型以生产有效的硬件 软件分别。 我们工作的软件部分是训练机器学习方法来识别不同的 病原体然而，诊断解决方案在其当前状态下是特别的， 不同的条件设置，如图像缩放，手机分辨率和网格读数， 标准化，因此在现场测试中部署时模型的性能较差。我们 因此，现场自动筛选试验将通过以下方式实现稳健的结局：1） 标准化基于场的显微镜的机器学习方法的开发 乌干达的疟疾诊断2)构建真实的补充框架 通过实地诊断监测和改进疟疾诊断平台 问题研究这里提出的基于现场的即时诊断系统解决了一个主要问题， 未得到满足的公共卫生疟疾筛查和监测需要可靠地告知公共卫生 疟疾控制和预防的干预措施。