Indiana University School of Optometry, Bloomington, Indiana, USA.
Department of Optometry, College of Applied Medical Sciences, Qassim University, Buraidah, Saudi Arabia.
Ophthalmic Physiol Opt. 2024 May;44(3):613-625. doi: 10.1111/opo.13289. Epub 2024 Feb 25.
To develop criteria to predict visual hemifields with deep perimetric defects based on retinal nerve fibre layer (RNFL) reflectance, in a transparent process whose components can be assessed by independent laboratories analysing data from their own small groups.
The analysis was carried out in four stages, using three independent groups of patients-30, 33 and 62 participants-with glaucoma and age-similar controls. The first stage used Group 1 to develop a criterion for RNFL reflectance images at 24, 36 or 48 μm below the inner limiting membrane (ILM). The second stage evaluated the criterion using Group 2. The third stage developed a second criterion to improve performance for Groups 1 and 2 combined. The fourth stage evaluated the second criterion with Group 3. Confidence intervals for sensitivity and specificity were then computed by combining results from all three groups.
The first criterion identified all hemifields with deep defects and no hemifields from controls, using a within-eye reference for healthy RNFL. For Group 2, specificity remained high but sensitivity was reduced. The second criterion improved sensitivity by using location-specific reference values. For Group 3, sensitivity remained high but reduced specificity was found. Confidence intervals showed substantial overlap for the two criteria.
We developed two criteria to identify patients with deep perimetric defects with high specificity and sensitivity. Several improvements are warranted: automated identification of the fovea-disc angle and optic disc locations, evaluation of normal variation in patterns of RNFL thickness, improved segmentation of ILM and major vasculature, reduction of within-eye variability in RNFL reflectance of healthy eyes, assessment of effects of image quality, assessment of effects of comorbidity and effectiveness of other devices.
基于视网膜神经纤维层（RNFL）反射率，制定预测深度周边视野缺损的标准，该过程透明，其组成部分可由分析来自自身小群组数据的独立实验室进行评估。
该分析分四个阶段进行，使用三个独立的患者组（每组 30、33 和 62 名患者）和年龄相似的对照组进行。第一阶段使用第 1 组开发内界膜（ILM）下方 24、36 或 48μm 的 RNFL 反射率图像的标准。第二阶段使用第 2 组评估标准。第三阶段开发了第二个标准，以提高第 1 组和第 2 组的性能。第四阶段使用第 3 组评估第二个标准。然后通过合并所有三组的结果计算敏感性和特异性的置信区间。
第一个标准使用健康 RNFL 的眼内参考值，识别了所有具有深度缺陷的视野和没有对照组的视野。对于第 2 组，特异性仍然很高，但敏感性降低。第二个标准通过使用特定位置的参考值来提高敏感性。对于第 3 组，敏感性仍然很高，但特异性降低。置信区间显示两个标准有很大的重叠。
我们开发了两种标准来识别具有高特异性和敏感性的深度周边视野缺损患者。需要进行多项改进：自动识别黄斑中心凹-视盘角度和视盘位置，评估 RNFL 厚度模式的正常变异性，改善 ILM 和主要血管的分割，降低健康眼中 RNFL 反射率的眼内变异性，评估图像质量的影响，评估合并症的影响以及其他设备的有效性。
