Smartphone-based community screening for eye disease in rural India

印度农村地区基于智能手机的眼病社区筛查

• 批准号: 10704721
• 负责人: Kunal S Parikh
• 金额: $ 18.34万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10704721
• 关键词: Algorithms; Anterior; Anterior eyeball segment structure; Artificial Intelligence; Award; Biomedical Engineering; Biometry; Blindness; Calendar; Caring; Cataract; Cellular Phone; Clinical Data; Collaborations; Communities; Community Health Aides; Computer software; Continuity of Patient Care; Corneal Opacity; Data; Data Collection; Data Set; Dependence; Development; Devices; Diagnosis; Diagnostic; Disease; Ensure; Epidemiology; Equipment; Eye; Eye diseases; Failure; Geography; Health; Health Technology; Health system; Healthcare; Hospitals; Human Resources; Image; Improve Access; India; Information Technology; Infrastructure; Intervention; Machine Learning; Modeling; Ophthalmologist; Ophthalmology; Outcome; Patients; Pattern; Performance; Periodicals; Persons; Phase; Public Health Practice; Quality Control; Refractive Errors; Research; Research Personnel; Resource-limited setting; Rural; Specialist; Standardization; System; Time; Training; Translating; Universities; Validation; Visually Impaired Persons; Vulnerable Populations; acceptability and feasibility; algorithm training; biomedical referral center; blind; care delivery; care providers; collaborative trial; cost; cost effectiveness; deep neural network; design; effectiveness evaluation; follow-up; improved; innovative technologies; interoperability; low and middle-income countries; mHealth; machine learning algorithm; member; novel; ophthalmic examination; patient screening; prototype; remote communities; remote grading; rural patients; rural setting; screening; skills; smartphone based device; user centered design; user-friendly

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.

项目概要 这是一份名为“基于智能手机的前眼社区筛查”的 R21/R33 奖项申请 印度农村地区的疾病。”来自约翰霍普金斯大学和阿拉文眼科医院的研究人员有不同的研究 眼科、生物医学工程、机器学习、流行病学、生物统计学和使用的专业知识 全球 2.75 亿盲人和视力障碍者中 90% 的人使用移动医疗技术。 人们生活在低收入和中等收入国家 (LMIC)，患有白内障、屈光不正、角膜混浊等。 在中低收入国家农村地区，眼前部疾病是造成全球失明的主要原因。 训练有素的眼科护理人员（例如眼科医生）是及时诊断和治疗前房角眼病的主要障碍 眼科疾病由训练有素的眼科医生定期进行“眼科训练营”检查。 几十年来一直是农村眼病筛查的支柱，但存在一些局限性，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 超出眼营日期，以及由于设备、人员和社区宣传而导致的高成本。 为社区卫生工作者开发、验证和实施基于智能手机的新型设备 (CHW) 主导的农村 LMIC 环境中的眼病筛查、诊断和转诊。 演示后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。 可行性，我们将使用智能手机平台评估社区卫生工作者主导的筛查的诊断有效性 与由眼科医生进行的传统现场眼科检查相比，在收集足够的数据和数据后。 使用该平台的图像，我们将设计、验证和实施机器学习算法，以允许真实的 该项目将由社区卫生工作者及时做出诊断和转诊决定，而无需依赖眼科医生。 收集有关转诊模式、随访损失、成本效益和差异的详细数据 该项目旨在评估弱势群体的成果，以实现更有针对性的健康干预措施。 旨在克服长期存在的关键地理、财务、运营和人力资源限制， 眼部筛查，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 我们的可扩展方法有潜力改变资源匮乏环境下的全球眼保健服务。 此次合作还将永久提高 Aravind 的移动健康和研究能力 眼科医院。