Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence.
Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence.
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DOI：
10.1167/tvst.10.9.16
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发表时间：
2021-08-02
影响因子：
3
通讯作者：
Yousefi S
中科院分区：
文献类型：
--
作者：
Huang X;Sun J;Majoor J;Vermeer KA;Lemij H;Elze T;Wang M;Boland MV;Pasquale LR;Mohammadzadeh V;Nouri-Mahdavi K;Johnson C;Yousefi S
本研究的目的是评估人工神经网络(ANN)在估计光学相干断层扫描(OCT)测量的视网膜乳头周围神经纤维层(RNFL)厚度的平均偏差(MD)严重程度方面的准确性。使用1796对视野和1796只眼睛的OCT测量对模型进行训练，以根据RNFL数据估计视野MD。检验了多元线性回归模型、随机森林回归模型、支持向量回归模型和一维卷积神经网络(CNN)模型。使用由698、256和691对视野和OCT测量组成的三个独立子集来验证模型。估计误差被可视化，以主观地评估模型的性能。使用平均绝对误差(MAE)、均方根误差(RMSE)、中位数绝对误差、皮尔逊相关性和R平方度量来客观评估模型的性能。基于测试数据集的ANN模型的MAE和RMSE分别为4.0dB(95%可信区间=3.8~4.2)和5.2dB(95%可信区间=5.1~5.4)。在独立数据集上，ANN模型的MAE和RMSE范围分别为3.3-5.9dB和4.4-8.4dB。所提出的ANN模型比多变量线性回归模型、随机森林模型、支持向量回归模型和一维CNN模型更好地从RNFL测量数据中估计MD。该模型可推广到来自不同中心和不同种族的独立数据。人工神经网络模型的成功开发可能有助于临床医生根据客观的OCT测量来评估青光眼的视觉功能，而对主观视野测试的依赖性较小。
The purpose of this study was to assess the accuracy of artificial neural networks (ANN) in estimating the severity of mean deviation (MD) from peripapillary retinal nerve fiber layer (RNFL) thickness measurements derived from optical coherence tomography (OCT). Models were trained using 1796 pairs of visual field and OCT measurements from 1796 eyes to estimate visual field MD from RNFL data. Multivariable linear regression, random forest regressor, support vector regressor, and 1D convolutional neural network (CNN) models with sectoral RNFL thickness measurements were examined. Three independent subsets consisting of 698, 256, and 691 pairs of visual field and OCT measurements were used to validate the models. Estimation errors were visualized to assess model performance subjectively. Mean absolute error (MAE), root mean square error (RMSE), median absolute error, Pearson correlation, and R-squared metrics were used to assess model performance objectively. The MAE and RMSE of the ANN model based on the testing dataset were 4.0 dB (95% confidence interval = 3.8–4.2) and 5.2 dB (95% confidence interval = 5.1–5.4), respectively. The ranges of MAE and RMSE of the ANN model on independent datasets were 3.3–5.9 dB and 4.4–8.4 dB, respectively. The proposed ANN model estimated MD from RNFL measurements better than multivariable linear regression model, random forest, support vector regressor, and 1-D CNN models. The model was generalizable to independent data from different centers and varying races. Successful development of ANN models may assist clinicians in assessing visual function in glaucoma based on objective OCT measures with less dependence on subjective visual field tests.
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影响因子：
4.4
作者：
Guo Z;Kwon YH;Lee K;Wang K;Wahle A;Alward WLM;Fingert JH;Bettis DI;Johnson CA;Garvin MK;Sonka M;Abràmoff MD
通讯作者：
Abràmoff MD
影响因子：
13.7
作者：
Christopher M;Bowd C;Belghith A;Goldbaum MH;Weinreb RN;Fazio MA;Girkin CA;Liebmann JM;Zangwill LM
通讯作者：
Zangwill LM
影响因子：
17.8
作者：
Hood DC;Raza AS;de Moraes CG;Liebmann JM;Ritch R
通讯作者：
Ritch R
影响因子：
4.4
作者：
Bogunovic, Hrvoje;Kwon, Young H.;Abramoff, Michael D.
通讯作者：
Abramoff, Michael D.