Development of smartphone imaging technology for screening maternal and neonatal anaemia

研究背景的简要描述，包括潜在的影响贫血影响着世界人口的四分之一，在孕妇中的患病率更高，为42%。该项目的目的是使用负担得起的智能手机成像作为孕产妇和新生儿贫血的诊断策略。目前，诊断需要验血，这是昂贵的和侵入性的。在没有有效的交通和医院系统的国家和地区，例如在经历政治动荡的国家，可能无法进行血液检测。贫血的及时诊断可以使治疗更容易（例如，通过补铁），更有可能获得疗效。该方法基于眼睛和眼周区域（包括下眼睑）的成像。我们的目标是开发一个名为综合孕产妇和新生儿护理应用程序的智能手机应用程序，该应用程序将提供有关贫血和黄疸的诊断信息（在本项目之外），用于怀孕期间的妇女，出生后不久的婴儿和学龄前儿童。该研究小组此前曾利用类似的技术检测新生儿黄疸，这为临床研究建立了联系，并获得了媒体的广泛关注。目的和目标-主要目标是开发孕产妇和新生儿综合护理（iMANC）应用程序，用于筛查孕产妇贫血，可供受过培训的医护人员、家长和患者使用。具体目标是：o改进图像采集和预处理：-通过机器学习开发巩膜分割，以识别图像的适当区域，用于进一步分析-开发自动化流程，以确保质量，检测和替换不适合分析的图像-调查并利用经济实惠的智能手机成像技术的进步（如双摄像头传感器），以提高图像质量改善图像分析：-使用可编程摄像头设置来研究最大限度地减少不同环境照明影响的技术-研究采集图像中可能提高诊断能力的其他参数临床验证：- 通过临床研究的设计、实施和分析，对iMANC应用的准确性进行统计分析-了解该技术在临床使用中面临的挑战，包括伦理和财务问题，以及对临床医生工作流程的影响，这可能会对更广泛的使用构成障碍研究方法的新颖性尽管研究小组已经使用该技术进行了试点研究，试点研究并没有在所有亚群体中发挥作用。为了克服这一点，该研究将开发两个领域的新技术：（1）人机交互，用于指导图像采集，自动获取收集数据中的感兴趣区域，并以可访问和负责任的方式呈现输出数据;（2）优化诊断算法。这包括开发新技术来控制环境光的影响，并使用新的智能手机摄像头技术，这些技术尚未被广泛使用。与EPSRC的战略和研究领域相一致该项目属于医疗技术主题，特别是与临床技术，医学成像和分析科学领域相一致。这是因为它的目的是开发新的技术，通过控制医学图像的采集来分析临床环境中的生物系统。该项目还可能通过开发自动图像分割算法与人工智能技术主题保持一致。任何参与的公司或合作者合作者：Sara Hillman博士（UCLH），Judith Meek博士（UCLH），Vatsla Dadhwal博士（AIIMS），Anubhuti Rana博士（AIIMS）公司：加纳大学全印度医学科学研究所，大阿克拉地区医院

Brief description of the context of the research including potential impactAnaemia affects a quarter of the world population with a greater prevalence of 42% in pregnant women. The aim of the project is to use affordable smartphone imaging as a diagnostic strategy in maternal and neonatal anaemia. At present, diagnosis requires a blood test, which is costly and invasive. Blood tests may not be accessible in countries and regions without effective transport and hospital systems, such as in countries experiencing political unrest. A timely diagnosis of anaemia can allow for easier treatment (for example, via iron supplementation) with a greater chance of efficacy.The approach is based upon imaging of the eye and the periocular region, including the lower eyelids. We aim to develop a smartphone application called the integrated maternal and neonatal care app which will provide diagnostic information on anaemia and jaundice (outside this project), to be used on women during pregnancy, their babies shortly after birth, and preschool-aged children. The group have previously utilised similar technology in detection of neonatal jaundice, which has established links for clinical studies and achieved widespread media attention.Aims and Objectives- The primary objective is to develop the integrated maternal and neonatal care (iMANC) app for screening for maternal anaemia, which can be used by trained healthcare workers, parents and patients.- The specific objectives are to:o Improve image acquisition and pre-processing:- Develop sclera segmentation by machine learning in order to identify appropriate regions of the image for further analysis- Develop automated processes for quality assurance to detect and replace images which are not suitable for analysis- Investigate and utilise advances in affordable smartphone imaging technology (such as dual camera sensors) in order to improve image qualityo Improve image analysis:- Use the programmable camera settings to investigate techniques for minimising the effects of varying ambient lighting- Investigate additional parameters within the acquired image which might improve diagnostic abilityo Clinical validation:- Statistically analyse the accuracy of the iMANC application through design, implementation, and analysis of clinical studies- Understand the challenges to clinical use of this technique, including both ethical and financial concerns, as well as effects on clinician workflow which may pose barriers to wider useNovelty of Research MethodologyAlthough the research group have used this technology to carry out pilot studies, the pilot studies have not worked in all subpopulations. In order to overcome this, the research will develop novel techniques with two areas: (1) human-computer interaction, for guiding image acquisition, automatically obtaining regions of interest in the gathered data, and presenting the output data in an accessible and responsible manner; and (2) optimising the diagnostic algorithm. This includes development of new techniques for controlling for the effect of ambient light and using new smartphone camera technologies which have not been used widely.Alignment to EPSRC's strategies and research areasThis project lies within the healthcare technology theme, in particular aligning with the areas of clinical technology, medical imaging, and analytical science. This is because it aims to develop novel techniques to analyse a biological system in a clinical setting through controlled acquisition of a medical image. This project may also align against the artificial intelligence technologies theme through development of automatic image segmentation algorithms.Any companies or collaborators involvedCollaborators: Dr Sara Hillman (UCLH), Dr Judith Meek (UCLH), Dr Vatsla Dadhwal (AIIMS), Dr Anubhuti Rana (AIIMS)Companies: All India Institute of Medicine Science, University of Ghana, Greater Accra Regional Hosp