Pham Alex T, Bradley Chris, Hou Kaihua, Herbert Patrick, Unberath Mathias, Ramulu Pradeep Y, Yohannan Jithin
medRxiv. 2024 Oct 18:2024.10.17.24315710. doi: 10.1101/2024.10.17.24315710.
Multiple 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.
Retrospective, longitudinal study.
A model dataset of 70,575 paired OCT/VFs to train an ML model converting OCT to VF-MD. A separate progression dataset of 4,044 eyes with ≥ 5 paired OCT/VFs to assess the ability of OCT-derived MD to detect worsening. Progression dataset eyes had two additional unpaired VFs (≥ 7 total) to establish a "ground truth" rate of progression defined by MD slope.
We trained an ML model using paired VF/OCT data to estimate MD measurements for each OCT scan (OCT-MD). We used this ML model to generate longitudinal OCT-MD estimates 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.
AUC.
OCT-MD estimates had an MAE of 1.62 dB. 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. OCT-MD estimates needed an MAE ≤ 1.00 dB before AUC was statistically similar to VF-MD alone.
ML models converting OCT data to VF-MD with error levels lower than published in prior work (MAE: 1.62 dB) were inferior to VF-MD data for detecting trend-based VF progression. 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。
回顾性纵向研究。
一个包含70575对OCT/VF的模型数据集，用于训练将OCT转换为VF-MD的ML模型。一个单独的进展数据集，包含4044只眼睛，有≥5对OCT/VF，用于评估OCT衍生的MD检测恶化的能力。进展数据集的眼睛还有另外两对未配对的VF（总共≥7对），以确定由MD斜率定义的“真实”进展率。
我们使用配对的VF/OCT数据训练一个ML模型，以估计每次OCT扫描的MD测量值（OCT-MD）。我们使用这个ML模型为进展数据集的眼睛生成纵向OCT-MD估计值。在用OCT-MD替代/补充VF-MD后，我们计算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<0.5 dB/年的真实MD斜率标记真正的进展者。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）。因为我们发现OCT-MD替代的AUC在统计学上低于VF-MD，所以我们模拟了降低OCT-MD平均绝对误差（MAE）对检测恶化能力的影响。
AUC。
OCT-MD估计值的MAE为1.62 dB。部分OCT-MD替代的MD斜率的AUC显著低于VF-MD斜率。无论MAE如何，用OCT-MD补充VF-MD也不能提高AUC。在AUC在统计学上与单独的VF-MD相似之前，OCT-MD估计值需要MAE≤1.00 dB。
将OCT数据转换为VF-MD的ML模型，其误差水平低于先前工作中公布的水平（MAE：1.62 dB），在检测基于趋势的VF进展方面不如VF-MD数据。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。
