Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.
Department of Ophthalmology, Osaka University Graduate School of Medicine, Osaka, Japan.
Sci Rep. 2021 Jan 26;11(1):2214. doi: 10.1038/s41598-020-79494-6.
We aimed to develop a model to predict visual field (VF) in the central 10 degrees in patients with glaucoma, by training a convolutional neural network (CNN) with optical coherence tomography (OCT) images and adjusting the values with Humphrey Field Analyzer (HFA) 24-2 test. The training dataset included 558 eyes from 312 glaucoma patients and 90 eyes from 46 normal subjects. The testing dataset included 105 eyes from 72 glaucoma patients. All eyes were analyzed by the HFA 10-2 test and OCT; eyes in the testing dataset were additionally analyzed by the HFA 24-2 test. During CNN model training, the total deviation (TD) values of the HFA 10-2 test point were predicted from the combined OCT-measured macular retinal layers' thicknesses. Then, the predicted TD values were corrected using the TD values of the innermost four points from the HFA 24-2 test. Mean absolute error derived from the CNN models ranged between 9.4 and 9.5 B. These values reduced to 5.5 dB on average, when the data were corrected using the HFA 24-2 test. In conclusion, HFA 10-2 test results can be predicted with a OCT images using a trained CNN model with adjustment using HFA 24-2 test.
我们旨在通过使用光学相干断层扫描 (OCT) 图像训练卷积神经网络 (CNN) 并调整亨氏视野分析仪 (HFA) 24-2 测试的数值，来开发一种预测青光眼患者中央 10 度视野 (VF) 的模型。训练数据集包括 312 名青光眼患者的 558 只眼和 46 名正常受试者的 90 只眼。测试数据集包括 72 名青光眼患者的 105 只眼。所有的眼睛都用 HFA 10-2 测试和 OCT 进行了分析; 测试数据集的眼睛还接受了 HFA 24-2 测试的分析。在 CNN 模型训练过程中，从 OCT 测量的黄斑视网膜层厚度的组合中预测 HFA 10-2 测试点的总偏差 (TD) 值。然后，使用 HFA 24-2 测试的最内四点的 TD 值对预测的 TD 值进行校正。CNN 模型得出的平均绝对误差在 9.4 到 9.5 B 之间。当使用 HFA 24-2 测试校正数据时，这些值平均降低到 5.5 dB。总之，使用经过训练的 CNN 模型，可以使用 OCT 图像预测 HFA 10-2 测试结果，并使用 HFA 24-2 测试进行调整。
