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初级眼保健中的青光眼诊断和人工智能应用在降低澳大利亚未检出青光眼患病率中的作用。

Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia.

机构信息

Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.

Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia.

出版信息

Eye (Lond). 2024 Aug;38(11):2003-2013. doi: 10.1038/s41433-024-03026-z. Epub 2024 Mar 21.

DOI:10.1038/s41433-024-03026-z
PMID:38514852
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11269618/
Abstract

Glaucoma is the commonest cause of irreversible blindness worldwide, with over 70% of people affected remaining undiagnosed. Early detection is crucial for halting progressive visual impairment in glaucoma patients, as there is no cure available. This narrative review aims to: identify reasons for the significant under-diagnosis of glaucoma globally, particularly in Australia, elucidate the role of primary healthcare in glaucoma diagnosis using Australian healthcare as an example, and discuss how recent advances in artificial intelligence (AI) can be implemented to improve diagnostic outcomes. Glaucoma is a prevalent disease in ageing populations and can have improved visual outcomes through appropriate treatment, making it essential for general medical practice. In countries such as Australia, New Zealand, Canada, USA, and the UK, optometrists serve as the gatekeepers for primary eye care, and glaucoma detection often falls on their shoulders. However, there is significant variation in the capacity for glaucoma diagnosis among eye professionals. Automation with Artificial Intelligence (AI) analysis of optic nerve photos can help optometrists identify high-risk changes and mitigate the challenges of image interpretation rapidly and consistently. Despite its potential, there are significant barriers and challenges to address before AI can be deployed in primary healthcare settings, including external validation, high quality real-world implementation, protection of privacy and cybersecurity, and medico-legal implications. Overall, the incorporation of AI technology in primary healthcare has the potential to reduce the global prevalence of undiagnosed glaucoma cases by improving diagnostic accuracy and efficiency.

摘要

青光眼是全球最常见的不可逆性失明原因,全球超过 70%的患者未被诊断。早期发现对于阻止青光眼患者的视觉损害进展至关重要,因为目前尚无治愈方法。本综述旨在:确定全球,尤其是澳大利亚,青光眼诊断率显著较低的原因;阐明初级保健在青光眼诊断中的作用,以澳大利亚的医疗保健为例;讨论如何利用人工智能(AI)的最新进展来改善诊断结果。青光眼在老年人群中较为普遍,通过适当的治疗可以改善视力预后,因此对一般医疗实践至关重要。在澳大利亚、新西兰、加拿大、美国和英国等国家,验光师是初级眼科保健的把关人,青光眼的检测通常落在他们的肩上。然而,眼科专业人员在青光眼诊断能力方面存在显著差异。使用人工智能(AI)对视神经照片进行自动化分析,可以帮助验光师快速、一致地识别高风险变化,减轻图像解释的挑战。尽管有潜力,但在人工智能能够在初级医疗保健环境中部署之前,仍需解决重大障碍和挑战,包括外部验证、高质量的实际实施、隐私和网络安全保护以及医疗法律影响。总的来说,将人工智能技术纳入初级医疗保健领域,有潜力通过提高诊断准确性和效率来降低全球未确诊青光眼病例的比例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/7342ad74909d/41433_2024_3026_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/b26671e02994/41433_2024_3026_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/d3223674c4e2/41433_2024_3026_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/3f01f8d5bed5/41433_2024_3026_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/58bc5c75128b/41433_2024_3026_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/7342ad74909d/41433_2024_3026_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/b26671e02994/41433_2024_3026_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/4b6bb778975f/41433_2024_3026_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/d3223674c4e2/41433_2024_3026_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/3f01f8d5bed5/41433_2024_3026_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/58bc5c75128b/41433_2024_3026_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d016/11269618/7342ad74909d/41433_2024_3026_Fig6_HTML.jpg

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