Liu James C, Jammal Alessandro A, Scherer Rafael, da Costa Douglas R, Kass Michael, Gordon Mae, Medeiros Felipe A
Department of Ophthalmology, Washington University, St Louis, Missouri.
Bascom Palmer Eye Institute, University of Miami, Miami, Florida.
JAMA Ophthalmol. 2025 Jun 26. doi: 10.1001/jamaophthalmol.2025.1740.
Deep learning predictions of retinal nerve fiber layer (RNFL) thickness derived from optic disc photographs may help to determine risk for development of primary open-angle glaucoma (POAG) in patients with ocular hypertension.
To predict mean RNFL thickness from the optic disc photographs from the Ocular Hypertension Treatment Study (OHTS) and assess the utility of predicted RNFL thickness as a risk factor for the development of POAG.
DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study evaluated 3272 eyes from 1636 participants with ocular hypertension but without POAG at the time of enrollment in the OHTS 1 and 2 trials. The OHTS was a multicenter study, with OHTS 1 and OHTS 2 collectively extending from February 28, 1994, to December 30, 2008. Optic disc photographs, baseline demographics, and clinical examination findings were included in the analysis. An OCT-trained deep learning model (machine-to-machine [M2M] model) was used to generate predicted RNFL thicknesses from 66 714 optic disc photographs.
The primary outcomes were factors (including predicted RNFL) that correlated with conversion to POAG from the OHTS cohort, identified by proportional hazards models.
Among 1444 participants with ocular hypertension from the OHTS cohort, mean (SD) age was 56.0 (9.5) years, and 833 participants (57.7%) were female. Mean (SD) baseline predicted RNFL was 94.1 (7.1) μm for eyes that converted to POAG and 97.1 (7.0) μm for eyes that did not convert to POAG (mean difference, 3.0; 95% CI, 2.2-3.8; P < .001). Predicted baseline RNFL was a predictor of conversion to POAG during follow-up in Cox proportional hazards models in univariable analysis (hazard ratio [HR], 1.97; 95% CI, 1.60-2.42; P < .001) and multivariable analysis (HR, 1.83; 95% CI, 1.49-2.25; P < .001) per 10-μm thinner in predicted RNFL. Baseline age, intraocular pressure, central corneal thickness, pattern standard deviation, mean deviation, and cup-disc ratio remained predictors of conversion to POAG in both univariable and multivariable analysis. Longitudinal change in predicted RNFL (per 1-μm/year faster loss) was also a predictor of conversion to POAG (HR, 6.01; 95% CI, 3.33-10.64; P < .001).
In this diagnostic study, baseline M2M-predicted RNFL thickness and longitudinal rate of change in predicted RNFL were putative risk factors for the development of glaucoma in patients with ocular hypertension. These findings support the utility of M2M-predicted RNFL thickness to assess baseline glaucoma risk and monitor for glaucoma progression.
从视盘照片得出的视网膜神经纤维层(RNFL)厚度的深度学习预测可能有助于确定高眼压症患者原发性开角型青光眼(POAG)的发病风险。
从眼压治疗研究(OHTS)的视盘照片预测平均RNFL厚度,并评估预测的RNFL厚度作为POAG发病风险因素的效用。
设计、设置和参与者:这项诊断性研究评估了OHTS 1和2试验入组时1636名高眼压症患者但无POAG的3272只眼睛。OHTS是一项多中心研究,OHTS 1和OHTS 2总共从1994年2月28日持续到2008年12月30日。视盘照片、基线人口统计学数据和临床检查结果纳入分析。使用经过光学相干断层扫描(OCT)训练的深度学习模型(机器对机器[M2M]模型)从66714张视盘照片生成预测的RNFL厚度。
主要结局是通过比例风险模型确定的与OHTS队列中转为POAG相关的因素(包括预测的RNFL)。
在OHTS队列的1444名高眼压症参与者中,平均(标准差)年龄为56.0(9.5)岁,833名参与者(57.7%)为女性。转为POAG的眼睛的平均(标准差)基线预测RNFL为94.1(7.1)μm,未转为POAG的眼睛为97.1(7.0)μm(平均差异为3.0;95%置信区间为2.2 - 3.8;P <.001)。在单变量分析(风险比[HR],1.97;95%置信区间为1.60 - 2.42;P <.001)和多变量分析(HR,1.83;95%置信区间为1.49 - 2.25;P <.001)中,Cox比例风险模型显示,预测的RNFL每薄10μm,基线预测RNFL就是随访期间转为POAG的一个预测指标。基线年龄、眼压、中央角膜厚度、模式标准差、平均偏差和杯盘比在单变量和多变量分析中仍然是转为POAG的预测指标。预测的RNFL的纵向变化(每年每快损失1μm)也是转为POAG的一个预测指标(HR,6.01;95%置信区间为3.33 - 10.64;P <.001)。
在这项诊断性研究中,基线M2M预测的RNFL厚度和预测的RNFL的纵向变化率是高眼压症患者青光眼发病的假定风险因素。这些发现支持了M2M预测的RNFL厚度在评估基线青光眼风险和监测青光眼进展方面的效用。