The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields
The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields
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DOI：
10.1038/s41598-020-64869-6
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发表时间：
2020-05-12
影响因子：
4.6
通讯作者：
Shoji, Nobuyuki
中科院分区：
文献类型：
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作者：
Asaoka, Ryo;Murata, Hiroshi;Shoji, Nobuyuki
本研究的目的是探讨使用变分自动编码器（VAE）处理视野（VF）的有用性。训练数据由来自16，836只眼睛的82，433个VF组成。测试数据集1由来自104只患有开角型青光眼的眼睛的测试-再测试VF组成。测试数据集2是来自患有开角型青光眼的638只眼睛的10个VF的系列。使用训练数据集开发了用于重建VF的VAE模型。然后使用训练的VAE重建测试数据集1中的VF，并计算平均总偏差（mTD）（mTD（VAE））。在测试数据集2中，使用较短的VF系列预测第10个VF的mTD值。使用加权线性回归进行类似计算，其中权重等于mTD和mTD之间的绝对差值（VAE）。在测试数据集1中，来自第一VF的mTD和mTD（VAE）之间的差异与第一和第二VF中的mTD之间的差异之间存在显著关系。在测试数据集2中，加权mTD趋势分析的均方预测误差显著小于未加权mTD趋势分析的均方预测误差。
The aim of the study was to investigate the usefulness of processing visual field (VF) using a variational autoencoder (VAE). The training data consisted of 82,433 VFs from 16,836 eyes. Testing dataset 1 consisted of test-retest VFs from 104 eyes with open angle glaucoma. Testing dataset 2 was series of 10 VFs from 638 eyes with open angle glaucoma. A VAE model to reconstruct VF was developed using the training dataset. VFs in the testing dataset 1 were then reconstructed using the trained VAE and the mean total deviation (mTD) was calculated (mTD(VAE)). In testing dataset 2, the mTD value of the tenth VF was predicted using shorter series of VFs. A similar calculation was carried out using a weighted linear regression where the weights were equal to the absolute difference between mTD and mTD(VAE). In testing dataset 1, there was a significant relationship between the difference between mTD and mTD(VAE) from the first VF and the difference between mTD in the first and second VFs. In testing dataset 2, mean squared prediction errors with the weighted mTD trend analysis were significantly smaller than those form the unweighted mTD trend analysis.