Visual Field Prediction using Recurrent Neural Network
Visual Field Prediction using Recurrent Neural Network
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DOI：
10.1038/s41598-019-44852-6
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发表时间：
2019-06-10
影响因子：
4.6
通讯作者：
Lee, Jiwoong
中科院分区：
文献类型：
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作者：
Park, Keunheung;Kim, Jinmi;Lee, Jiwoong
人工智能能力最近有了很大的提高。在过去的几年中，深度学习算法之一，递归神经网络（RNN），在序列标记和序列数据预测任务中表现出了出色的能力。我们使用RNN建立了一个可靠的视野预测算法，并将其性能与传统的逐点普通线性回归（OLR）方法进行了比较。总共1，408只眼睛被用作训练数据集，另一个数据集，包括281只眼睛，被用作测试数据集。五个连续的视野测试被提供给构建的RNN作为输入，第六个视野测试与RNN的输出进行比较。通过预测测试数据集中的第6个视野，将RNN的性能与OLR的性能进行了比较。RNN的整体预测性能明显优于OLR。RNN的逐点预测误差显着小于OLR在大多数地区已知的是容易受到昏迷损害。RNN在视野检查恶化方面也更加稳健和可靠。因此，在临床实践中，RNN模型可以帮助做出进一步治疗青光眼的决策。
Artificial intelligence capabilities have, recently, greatly improved. In the past few years, one of the deep learning algorithms, the recurrent neural network (RNN), has shown an outstanding ability in sequence labeling and prediction tasks for sequential data. We built a reliable visual field prediction algorithm using RNN and evaluated its performance in comparison with the conventional pointwise ordinary linear regression (OLR) method. A total of 1,408 eyes were used as a training dataset and another dataset, comprising 281 eyes, was used as a test dataset. Five consecutive visual field tests were provided to the constructed RNN as input and a 6th visual field test was compared with the output of the RNN. The performance of the RNN was compared with that of OLR by predicting the 6th visual field in the test dataset. The overall prediction performance of RNN was significantly better than OLR. The pointwise prediction error of the RNN was significantly smaller than that of the OLR in most areas known to be vulnerable to glaucomatous damage. The RNN was also more robust and reliable regarding worsening in the visual field examination. In clinical practice, the RNN model can therefore assist in decision-making for further treatment of glaucoma.