Central visual field in glaucoma: An updated review.

IF 1 Q4 OPHTHALMOLOGY Taiwan Journal of Ophthalmology Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI:10.4103/tjo.TJO-D-24-00042
Kelvin H Du, Alireza Kamalipour, Sasan Moghimi
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Abstract

Evaluation of central vision in glaucoma is important due to its impact on patients' quality of life and activities of daily living such as reading, driving, and walking. The 10-2 visual field (VF) assessment remains a mainstay in the functional analysis of central vision in glaucoma diagnosis and progression. However, it may be underutilized in clinical practice. Monitoring of disease progression especially in advanced cases, glaucoma evaluation in certain ocular disorders such as high myopia, disc hemorrhage, low corneal hysteresis, and certain optic disc phenotypes, as well as earlier detection of central VF damage, are certain conditions where additional monitoring with the 10-2 pattern may provide complementary clinical information to the commonly utilized 24-2 pattern. In addition, the development of artificial intelligence techniques may assist clinicians to most effectively allocate limited resources by identifying more risk factors to central VF damage. In this study, we aimed to determine specific patient characteristics that make central VF damage more likely and to assess the benefit of incorporating the 10-2 VF in various clinical settings.

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青光眼的中心视野:最新综述。
对青光眼患者的中心视力进行评估非常重要,因为这影响到患者的生活质量和日常生活活动,如阅读、驾驶和行走。10-2 视野(VF)评估仍然是青光眼诊断和进展过程中中心视力功能分析的主要方法。然而,在临床实践中,这一方法可能未得到充分利用。在监测疾病进展(尤其是晚期病例)、评估某些眼部疾病(如高度近视、视盘出血、低角膜滞后和某些视盘表型)的青光眼以及更早地检测中心视野损伤等情况下,使用 10-2 模式进行额外监测可为常用的 24-2 模式提供补充性临床信息。此外,人工智能技术的发展可以帮助临床医生识别更多中心性 VF 损伤的风险因素,从而最有效地分配有限的资源。在本研究中,我们旨在确定更有可能造成中心 VF 损伤的特定患者特征,并评估在各种临床环境中采用 10-2 VF 的益处。
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来源期刊
CiteScore
1.80
自引率
9.10%
发文量
68
审稿时长
19 weeks
期刊最新文献
Advancing glaucoma care with big data and artificial intelligence innovations. Application of artificial intelligence in glaucoma care: An updated review. Artificial intelligence and big data integration in anterior segment imaging for glaucoma. Big data and electronic health records for glaucoma research. Big data for imaging assessment in glaucoma.
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