远程眼科设备:弥合农村眼科护理的差距。

IF 2.7
Bharat Gurnani, Kirandeep Kaur
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引用次数: 0

摘要

由于缺乏眼科资源、路途遥远以及糖尿病视网膜病变、青光眼和老年性黄斑变性等视力威胁疾病的诊断延迟,农村人口承受着不成比例的可预防的视力丧失负担。远程眼科利用便携式成像设备、数据连接和远程口译,为服务不足的社区提供了诊断能力,并在发生不可逆转的视力丧失之前进行早期干预,从而提供了一种关键的解决方案。涵盖领域:本报告回顾了用于眼科成像的手持式眼底相机和基于智能手机的适配器的发展和性能,以及用于高分辨率结构评估的便携式和家用OCT系统。我们总结了验证设备准确性的关键研究,并研究了具有监管许可的视网膜图像自主分级的人工智能算法。我们还讨论了实施方面的挑战,包括基础设施差距、图像质量限制、成本效用数据和概述策略,如混合人工智能-专家工作流程、联邦学习以及将远程眼保健集成到现有的公共卫生计划中。专家意见:远程眼科设备已经成熟为可靠的工具,可以民主化的眼睛护理。它们的成功取决于强大的数字基础设施、严格的设备和算法验证以及可持续的融资模式。投资于地方培训、社区参与和可互操作的转诊网络,对于将技术进步转化为有意义地减少农村视力障碍至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Teleophthalmology enabled devices: bridging the gap in rural eye care.

Introduction: Rural populations bear a disproportionate burden of preventable vision loss due to scant ophthalmic resources, long travel distances, and delayed diagnoses of vision threatening conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Teleophthalmology leveraging portable imaging devices, data connectivity, and remote interpretation, offers a critical solution by bringing diagnostic capabilities to underserved communities and enabling earlier intervention before irreversible vision loss occurs.

Areas covered: This report reviews the evolution and performance of handheld fundus cameras and smartphone-based adapters for ophthalmic imaging, as well as portable and home-based OCT systems for high-resolution structural assessment. We summarize key studies validating device accuracy, and examine artificial-intelligence algorithms that autonomously grade retinal images with regulatory clearance. We also discuss implementation challenges including infrastructure gaps, image-quality limitations, cost-utility data and outline strategies such as hybrid AI - expert workflows, federated learning, and integration of tele-eye care into existing public health programs.

Expert opinion: Teleophthalmology devices have matured into reliable tools that can democratize eye care. Their success depends on robust digital infrastructure, rigorous device and algorithm validation, and sustainable financing models. Investment in local training, community engagement, and interoperable referral networks is essential to translate technological advances into meaningful reductions in rural vision impairment.

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