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.

项目概要 疟疾是发展中国家的主要健康问题之一。疟疾流行已 归因于实验室水平的诊断不良。这常常导致疾病误诊 和耐药性。许多发展中国家面临着缺乏足够的实验室的问题 技术人员通过显微镜的黄金标准机制诊断疾病，这 使其中一些国家本已严峻的局势进一步恶化。世界各地的流行趋势 现在的技术基于机器学习和深度学习技术。这些可以是 与智能手机相结合来改善疾病诊断。然而，大多数 之前关于显微镜诊断自动化的工作是在 实验室环境和似乎没有研究提供实用的现场可部署解决方案。的目标 拟议的研究是开发一种快速、低成本、准确且简单的现场筛选系统 应对疟疾等显微镜挑战。具体来说，本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 提议的解决方案基于我们早期在马凯雷雷进行的移动显微镜工作 大学人工智能实验室，通过利用最近的技术来应对自动化显微镜 3D 打印技术的进步使得低成本 3D 打印适配器的开发成为可能。 这使得可以在显微镜上连接各种智能手机，此外，我们 实施了用于病原体检测的深度学习模型，以生产有效的硬件 和软件分别。 我们工作的软件部分是训练机器学习方法来识别不同的 病原体对象。然而，诊断解决方案在目前的状态下是临时的，其中 不同的条件设置（例如图像缩放、手机分辨率和网格读数）不适用 标准化，因此模型在现场测试中部署时性能较差。我们的 因此，内场自动筛查试验将通过以下方式实现稳健的结果：1) 标准化现场显微镜机器学习方法的开发 乌干达的疟疾诊断。 2）构建实时补充框架 通过现场诊断监测和改进疟疾平台的诊断 研究。这里提出的基于现场的护理点诊断系统解决了一个主要问题 未满足的公共卫生疟疾筛查和监测需求，以可靠地为公共卫生提供信息 疟疾控制和预防干预措施。