Smartphone-based community screening for eye disease in rural India

项目总结 这是一份R21/R33奖项的申请表，名为“基于智能手机的前眼社区筛查” 印度农村地区的疾病。来自约翰霍普金斯大学和阿拉文德眼科医院的研究人员有不同的 在眼科、生物医学工程、机器学习、流行病学、生物统计学和使用 低资源环境下的移动医疗技术。全世界2.75亿盲人和视障人士中的90% 人们生活在中低收入国家(LMIC)。白内障、屈光不正、角膜混浊等 前部眼病是全球致盲的主要原因。在农村LMIC环境中，缺乏获得高度 眼科医生等训练有素的眼科护理人员是及时诊断和治疗前房疾病的关键障碍 眼疾。由训练有素的眼科医生进行的定期“眼营”筛查是 几十年来一直是农村眼病筛查的支柱，但有几个限制，包括缺乏机会 对于训练有素的眼科医生来说，无法到达最偏远的社区，缺乏一致的日历覆盖范围 超过了眼部露营日期，以及由于设备、人员和社区宣传而导致的高昂成本。我们建议 面向社区卫生工作者的新型智能手机设备的开发、验证和实施 (CHW)领导的农村LMIC环境中眼病的筛查、诊断和转诊。我们将开发这一技术 智能手机平台采用迭代原型和以用户为中心的设计方法。在演示之后 可行性，我们将使用智能手机平台评估CHW领导的筛查的诊断有效性 与眼科医生传统的面对面眼科夏令营检查相比。在收集了足够的数据和 使用该平台，我们将设计、验证和实现机器学习算法，以允许真实的- 由CHWS做出的时间诊断和转诊决定，不依赖于眼科医生。这个项目将 执行有关转诊模式、后续行动损失、成本效益和差异化的详细数据收集 在弱势群体中取得成果，以便能够采取更有针对性的卫生干预措施。这个项目是 旨在克服关键的长期地理、财务、运营和人力资源限制，以 眼睛筛查，同时确保诊断有效性、质量控制和与现有医疗系统的互操作性 基础设施。我们的可扩展方法具有在低资源环境下转变全球眼科护理服务的潜力。 这一合作还将永久性地改善Aravind的移动医疗和研究能力 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.