Pham Alex T, Bradley Chris, Hou Kaihua, Herbert Patrick, Yohannan Jithin
University of Maryland School of Medicine, Baltimore, MD, USA.
Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Sci Rep. 2025 Feb 11;15(1):5013. doi: 10.1038/s41598-025-86217-2.
Multiple glaucoma studies have attempted to generate visual field (VF) mean deviation (MD) estimates using cross-sectional optical coherence tomography (OCT) data. However, whether such models offer any value in detecting longitudinal VF progression is unclear. We address this by developing a machine learning (ML) model to convert OCT data to MD and assessing its ability to detect longitudinal worsening. In this study, we created a model dataset of 70,575 paired OCT/VFs to train an ML model to convert OCT to VF-MD. We created a separate progression dataset of 4,044 eyes with ≥ 5 paired OCT/VFs to assess the ability of OCT-derived MD to detect worsening. The progression dataset eyes had 2 additional unpaired VFs (≥ 7 total) to establish a "ground truth" rate of progression defined by MD slope. We used the ML model to generate longitudinal OCT-MD estimates for each OCT scan for progression dataset eyes. We calculated MD slopes after substituting/supplementing VF-MD with OCT-MD and measured the ability to detect progression. We labeled true progressors using a ground truth MD slope < 0.5 dB/year calculated from ≥ 7 VF-MD measurements. We compared the area under the curve (AUC) of MD slopes calculated using both VF-MD (with < 7 measurements) and OCT-MD. Because we found OCT-MD substitution had a statistically inferior AUC to VF-MD, we simulated the effect of reducing OCT-MD mean absolute error (MAE) on the ability to detect worsening. Our model's OCT-MD estimates had an MAE of 1.62 dB (better than that of any previously published models). However, we found the AUC of MD slopes with partial OCT-MD substitution was significantly worse than the VF-MD slope. Supplementing VF-MD with OCT-MD also did not improve AUC, regardless of MAE. We found that OCT-MD estimates needed an MAE ≤ 1.00 dB before AUC was statistically similar to VF-MD alone. Overall, our ML model converting OCT data to VF-MD had error levels lower than those published in prior work and was inferior to VF-MD data for detecting trend-based VF progression. Our data suggest that future models converting OCT data to VF-MD must achieve better prediction errors (MAE ≤ 1 dB) to be clinically valuable at detecting VF worsening.
多项青光眼研究试图利用横断面光学相干断层扫描（OCT）数据生成视野（VF）平均偏差（MD）估计值。然而，此类模型在检测纵向VF进展方面是否具有任何价值尚不清楚。我们通过开发一种机器学习（ML）模型来将OCT数据转换为MD，并评估其检测纵向恶化的能力来解决这一问题。在本研究中，我们创建了一个包含70575对OCT/VF的模型数据集，以训练一个将OCT转换为VF-MD的ML模型。我们创建了一个单独的包含4044只眼睛的进展数据集，这些眼睛有≥5对OCT/VF，以评估OCT衍生的MD检测恶化的能力。进展数据集的眼睛还有另外2个未配对的VF（总共≥7个），以确定由MD斜率定义的“真实”进展率。我们使用ML模型为进展数据集的眼睛的每次OCT扫描生成纵向OCT-MD估计值。我们在用OCT-MD替代/补充VF-MD后计算MD斜率，并测量检测进展的能力。我们使用从≥7次VF-MD测量计算出的真实MD斜率<0.5 dB/年对真正的进展者进行标记。我们比较了使用VF-MD（测量次数<7次）和OCT-MD计算的MD斜率的曲线下面积（AUC）。由于我们发现OCT-MD替代的AUC在统计学上低于VF-MD，我们模拟了降低OCT-MD平均绝对误差（MAE）对检测恶化能力的影响。我们模型的OCT-MD估计值的MAE为1.62 dB（优于任何先前发表的模型）。然而，我们发现部分OCT-MD替代的MD斜率的AUC明显低于VF-MD斜率。用OCT-MD补充VF-MD也没有改善AUC，无论MAE如何。我们发现，在AUC在统计学上与单独的VF-MD相似之前，OCT-MD估计值的MAE必须≤1.00 dB。总体而言，我们将OCT数据转换为VF-MD的ML模型的误差水平低于先前工作中公布的水平，并且在检测基于趋势的VF进展方面不如VF-MD数据。我们的数据表明，未来将OCT数据转换为VF-MD的模型必须实现更好的预测误差（MAE≤1 dB），才能在检测VF恶化方面具有临床价值。
