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光学相干断层扫描与视盘照相评估在青光眼筛查中的比较。

Optical Coherence Tomography Versus Optic Disc Photo Assessment in Glaucoma Screening.

机构信息

Department of Ophthalmology, Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, FL.

出版信息

J Glaucoma. 2024 Aug 1;33(Suppl 1):S21-S25. doi: 10.1097/IJG.0000000000002392. Epub 2024 Mar 28.

Abstract

PRCIS

Optical coherence tomography (OCT) and optic disc photography present valuable but distinct capabilities for glaucoma screening.

OBJECTIVE

This review article examines the strengths and limitations of OCT and optic disc photography in glaucoma screening.

METHODS

A comprehensive literature review was conducted, focusing on the accuracy, feasibility, cost-effectiveness, and technological advancements in OCT and optic disc photography for glaucoma screening.

RESULTS

OCT is highly accurate and reproducible but faces limitations due to its cost and less portable nature, making widespread screening challenging. In contrast, optic disc photos are more accessible and cost-effective but are hindered by subjective interpretation and inconsistent grading reliability. A critical challenge in glaucoma screening is achieving a high PPV, particularly given the low prevalence of the disease, which can lead to a significant number of false positives. The advent of artificial intelligence (AI) and deep learning models shows potential in improving the diagnostic accuracy of optic disc photos by automating the detection of glaucomatous optic neuropathy and reducing subjectivity. However, the effectiveness of these AI models hinges on the quality of training data. Using subjective gradings as training data, will carry the limitations of human assessment into the AI system, leading to potential inaccuracies. Conversely, training AI models using objective data from OCT, such as retinal nerve fiber layer thickness, may offer a promising direction.

CONCLUSION

Both OCT and optic disc photography present valuable but distinct capabilities for glaucoma screening. An approach integrating AI technology might be key in optimizing these methods for effective, large-scale screening programs.

摘要

PRCIS

光学相干断层扫描(OCT)和视盘照相术在青光眼筛查方面具有有价值但不同的能力。

目的

本文综述了 OCT 和视盘照相术在青光眼筛查中的优势和局限性。

方法

我们进行了全面的文献回顾,重点关注 OCT 和视盘照相术在青光眼筛查中的准确性、可行性、成本效益以及技术进步。

结果

OCT 具有高度准确性和可重复性,但由于其成本和便携性较差,使得广泛筛查具有挑战性。相比之下,视盘照相术更容易获得且具有成本效益,但受到主观解释和不一致的分级可靠性的限制。在青光眼筛查中,一个关键的挑战是实现高阳性预测值(PPV),特别是考虑到疾病的低患病率,这可能导致大量的假阳性。人工智能(AI)和深度学习模型的出现显示出通过自动化检测青光眼性视神经病变和减少主观性来提高视盘照相术诊断准确性的潜力。然而,这些 AI 模型的有效性取决于训练数据的质量。使用主观分级作为训练数据将把人类评估的局限性带入 AI 系统,导致潜在的不准确性。相反,使用来自 OCT 的客观数据(例如视网膜神经纤维层厚度)来训练 AI 模型可能是一个有前途的方向。

结论

OCT 和视盘照相术在青光眼筛查方面都具有有价值但不同的能力。结合人工智能技术的方法可能是优化这些方法以用于有效、大规模筛查计划的关键。

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