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Visual field prediction using a deep bidirectional gated recurrent unit network model.


ABSTRACT: Although deep learning architecture has been used to process sequential data, only a few studies have explored the usefulness of deep learning algorithms to detect glaucoma progression. Here, we proposed a bidirectional gated recurrent unit (Bi-GRU) algorithm to predict visual field loss. In total, 5413 eyes from 3321 patients were included in the training set, whereas 1272 eyes from 1272 patients were included in the test set. Data from five consecutive visual field examinations were used as input; the sixth visual field examinations were compared with predictions by the Bi-GRU. The performance of Bi-GRU was compared with the performances of conventional linear regression (LR) and long short-term memory (LSTM) algorithms. Overall prediction error was significantly lower for Bi-GRU than for LR and LSTM algorithms. In pointwise prediction, Bi-GRU showed the lowest prediction error among the three models in most test locations. Furthermore, Bi-GRU was the least affected model in terms of worsening reliability indices and glaucoma severity. Accurate prediction of visual field loss using the Bi-GRU algorithm may facilitate decision-making regarding the treatment of patients with glaucoma.

SUBMITTER: Kim H 

PROVIDER: S-EPMC10333213 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Visual field prediction using a deep bidirectional gated recurrent unit network model.

Kim Hwayeong H   Lee Jiwoong J   Moon Sangwoo S   Kim Sangil S   Kim Taehyeong T   Jin Sang Wook SW   Kim Jung Lim JL   Shin Jonghoon J   Lee Seung Uk SU   Jang Geunsoo G   Hu Yuanmeng Y   Park Jeong Rye JR  

Scientific reports 20230710 1


Although deep learning architecture has been used to process sequential data, only a few studies have explored the usefulness of deep learning algorithms to detect glaucoma progression. Here, we proposed a bidirectional gated recurrent unit (Bi-GRU) algorithm to predict visual field loss. In total, 5413 eyes from 3321 patients were included in the training set, whereas 1272 eyes from 1272 patients were included in the test set. Data from five consecutive visual field examinations were used as in  ...[more]

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