Smartphone-based community screening for eye disease in rural India

项目摘要 这是一个R21/R33奖项的申请，标题为“基于智能手机的前眼社区筛查 印度农村的疾病”。来自约翰霍普金斯大学和Aravind眼科医院的研究人员对 眼科学、生物医学工程、机器学习、流行病学、生物统计学和 资源匮乏环境中的移动的保健技术。世界上2.75亿盲人和视障者中的90% 低收入和中等收入国家（LMICs）。白内障、屈光不正、角膜混浊和其他 前眼部疾病占全球失明的大多数。在农村低收入中等收入国家， 训练有素的眼科护理提供者，如眼科医生，是及时诊断和治疗前房性青光眼的关键障碍。 眼部疾病。由训练有素的眼科医生进行的定期“眼营”筛查一直是 几十年来一直是农村眼科疾病筛查的支柱，但有几个局限性，包括缺乏获得 没有受过训练的眼科医生，没有到达最偏远的社区，缺乏一致的日历覆盖面 由于设备、人员和社区宣传的原因，眼睛营地的日期已经超出了费用。我们提出 社区卫生工作者新型智能手机设备的开发、验证和实施 （CHW）领导的筛查，诊断，并在农村低收入国家的眼科疾病转诊。我们将开发这个 使用迭代原型设计和以用户为中心的设计方法的智能手机平台。示威后 可行性，我们将使用智能手机平台评估CHW引导的筛查的诊断有效性 与传统的由眼科医生亲自进行的眼科夏令营检查相比。在收集了足够的数据和 图像使用的平台，我们将设计，验证和实施机器学习算法，以允许真实的- 由CHW做出及时诊断和转诊决定，而无需依赖眼科医生。该项目将 执行有关转诊模式、失访、成本效益和差异的详细数据收集 这将有助于在弱势群体中取得更好的成果，以便能够采取更有针对性的卫生干预措施。这个项目是 旨在克服长期存在的关键地理、财务、运营和人力资源限制， 眼睛筛查，同时确保诊断有效性、质量控制以及与现有卫生系统的互操作性 基础设施演进我们的可扩展方法有可能改变低资源环境下的全球眼科护理服务。 这项合作还将永久改善Aravind的移动的健康和研究能力 眼科医院。

PROJECT SUMMARY This is an application for an R21/R33 award titled “Smartphone-based community screening of anterior eye diseases in rural India.” The investigators from Johns Hopkins University and Aravind Eye Hospital have diverse expertise in ophthalmology, biomedical engineering, machine learning, epidemiology, biostatistics, and use of mobile health technology in low-resource settings. 90% of the world’s 275 million blind and visually impaired people live in low- and middle-income countries (LMICs). Cataract, refractive error, corneal opacities, and other anterior eye diseases account for the majority of global blindness. In rural LMIC settings, lack of access to highly trained eye care providers such as ophthalmologists is a key barrier to timely diagnosis and treatment for anterior eye diseases. Periodic “eye camp” screenings performed by highly trained ophthalmologists have been the mainstay of rural eye disease screening for many decades but have several limitations including lack of access to trained ophthalmologists, failure to reach the most remote communities, lack of consistent calendar coverage beyond eye camp dates, and high costs due to equipment, personnel, and community publicity. We propose development, validation, and implementation of a novel smartphone based device for community health worker (CHW)-led screening, diagnosis, and referral for eye diseases in rural LMIC settings. We will develop this smartphone platform using iterative prototyping and user-centered design approaches. After demonstrating feasibility, we will evaluate the diagnostic validity of CHW-led screenings using the smartphone platform compared to traditional in-person eye camp exams by an ophthalmologist. After collecting sufficient data and images using the platform, we will design, validate, and implement machine learning algorithms to permit real- time diagnosis and referral decisions by CHWs without dependence on ophthalmologists. This project will perform detailed data collection regarding referral patterns, loss to follow-up, cost-effectiveness, and differential outcomes among vulnerable groups in order to enable more targeted health interventions. This project is intended to overcome key longstanding geographic, financial, operational, and human resource constraints to eye screening while ensuring diagnostic validity, quality control, and interoperability with existing health system infrastructure. Our scalable approach has potential to transform global eye care delivery in low-resource settings. This collaboration will also result in permanent improvements in mobile health and research capacity at Aravind Eye Hospital.