PURPOSE Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative spectral-domain (SD) OCT data to train a DL algorithm to quantify glaucomatous structural damage on optic disc photographs. DESIGN Cross-sectional study. PARTICIPANTS A total of 32 820 pairs of optic disc photographs and SD OCT retinal nerve fiber layer (RNFL) scans from 2312 eyes of 1198 participants. METHODS The sample was divided randomly into validation plus training (80%) and test (20%) sets, with randomization performed at the patient level. A DL convolutional neural network was trained to assess optic disc photographs and predict SD OCT average RNFL thickness. MAIN OUTCOME MEASURES The DL algorithm performance was evaluated in the test sample by evaluating correlation and agreement between the predictions and actual SD OCT measurements. We also assessed the ability to discriminate eyes with glaucomatous visual field loss from healthy eyes with area under the receiver operating characteristic (ROC) curves. RESULTS The mean prediction of average RNFL thickness from all 6292 optic disc photographs in the test set was 83.3±14.5 μm, whereas the mean average RNFL thickness from all corresponding SD OCT scans was 82.5±16.8 μm (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (Pearson r = 0.832; R2 = 69.3%; P < 0.001), with mean absolute error of the predictions of 7.39 μm. The area under the ROC curves for discriminating glaucomatous from healthy eyes with the DL predictions and actual SD OCT average RNFL thickness measurements were 0.944 (95% confidence interval [CI], 0.912-0.966) and 0.940 (95% CI, 0.902-0.966), respectively (P = 0.724). CONCLUSIONS We introduced a novel DL approach to assess fundus photographs and provide quantitative information about the amount of neural damage that can be used to diagnose and stage glaucoma. In addition, training neural networks to predict SD OCT data objectively represents a new approach that overcomes limitations of human labeling and could be useful in other areas of ophthalmology.

中文翻译：

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从机器到机器：经过OCT训练的深度学习算法，用于对眼底照片中的青光眼损伤进行客观量化。

目的以前使用深度学习（DL）算法对眼底照片上的青光眼损伤进行分类的方法受到了对参考训练集进行人工标记的要求的限制。我们提出了一种使用定量光谱域（SD）OCT数据训练DL算法来量化视盘照片上的青光眼结构损伤的新方法。设计横断面研究。参与者从1198名参与者的2312眼中总共进行了32 820对视盘照片和SD OCT视网膜神经纤维层（RNFL）扫描。方法将样本随机分为验证组，训练组（80％）和测试组（20％），并在患者水平上进行随机分组。训练了DL卷积神经网络以评估光盘照片并预测SD OCT平均RNFL厚度。主要观察指标通过评估预测值与实际SD OCT测量值之间的相关性和一致性来评估测试样本中的DL算法性能。我们还评估了在接收器工作特征（ROC）曲线以下面积的情况下，将具有青光眼视野丧失的眼睛与健康眼睛区分开的能力。结果测试集中所有6292张光盘照片的平均RNFL厚度的平均预测为83.3±14.5μm，而所有相应的SD OCT扫描的平均RNFL厚度的平均预测为82.5±16.8μm（P = 0.164）。RNFL厚度预测值与实测值之间存在非常强的相关性（Pearson r = 0.832； R2 = 69.3％； P <0.001），预测值的平均绝对误差为7.39μm。通过DL预测和实际SD OCT平均RNFL厚度测量可将健康眼与青光眼区分开的ROC曲线下面积分别为0.944（95％置信区间[CI]，0.912-0.966）和0.940（95％CI，0.902-0.966） ，分别为（P = 0.724）。结论我们引入了一种新颖的DL方法来评估眼底照片并提供有关可用于诊断和分期青光眼的神经损伤程度的定量信息。此外，训练神经网络以客观地预测SD OCT数据代表了一种新方法，该方法克服了人类标记的局限性，并且可能在眼科的其他领域中有用。结论我们引入了一种新颖的DL方法来评估眼底照片并提供有关可用于诊断和分期青光眼的神经损伤程度的定量信息。此外，训练神经网络以客观地预测SD OCT数据代表了一种新方法，该方法克服了人类标记的局限性，并且可能在眼科的其他领域中有用。结论我们引入了一种新颖的DL方法来评估眼底照片并提供有关可用于诊断和分期青光眼的神经损伤程度的定量信息。此外，训练神经网络以客观地预测SD OCT数据代表了一种新方法，该方法克服了人类标记的局限性，并且可能在眼科的其他领域中有用。