Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images
Abstract 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 d...
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Nature Portfolio
2021
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oai:doaj.org-article:fe4bdf1312fc4483a90537b7a7d8d0962021-12-02T14:16:42ZPredicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images10.1038/s41598-020-79494-62045-2322https://doaj.org/article/fe4bdf1312fc4483a90537b7a7d8d0962021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79494-6https://doaj.org/toc/2045-2322Abstract 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.Shotaro AsanoRyo AsaokaHiroshi MurataYohei HashimotoAtsuya MikiKazuhiko MoriYoko IkedaTakashi KanamotoJunkichi YamagamiKenji InoueNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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Medicine R Science Q Shotaro Asano Ryo Asaoka Hiroshi Murata Yohei Hashimoto Atsuya Miki Kazuhiko Mori Yoko Ikeda Takashi Kanamoto Junkichi Yamagami Kenji Inoue Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images |
| description |
Abstract 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. |
| format |
article |
| author |
Shotaro Asano Ryo Asaoka Hiroshi Murata Yohei Hashimoto Atsuya Miki Kazuhiko Mori Yoko Ikeda Takashi Kanamoto Junkichi Yamagami Kenji Inoue |
| author_facet |
Shotaro Asano Ryo Asaoka Hiroshi Murata Yohei Hashimoto Atsuya Miki Kazuhiko Mori Yoko Ikeda Takashi Kanamoto Junkichi Yamagami Kenji Inoue |
| author_sort |
Shotaro Asano |
| title |
Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images |
| title_short |
Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images |
| title_full |
Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images |
| title_fullStr |
Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images |
| title_full_unstemmed |
Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images |
| title_sort |
predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/fe4bdf1312fc4483a90537b7a7d8d096 |
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