Visual Field Prediction using Recurrent Neural Network

Park, Keunheung; Kim, Jinmi; Lee, Jiwoong

doi:10.1038/s41598-019-44852-6

Cited by 58 publications

(57 citation statements)

References 30 publications

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“…Many articles compared deep learning with state‐of‐the‐art models on various datasets and achieved a robust performance 35–42 . It has been shown that deep learning are not only effective in fundus photographs for the diagnosis of glaucoma but also in Optical Coherence Tomography 43–48 and visual fields 49–52 . AI has been showed effective in other ophthalmic diseases, like age‐related macular degeneration and diabetic retinopathy 9,53 .…”

Section: Discussionmentioning

confidence: 99%

Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta‐analysis

Buisson

Navel

Labbé

et al. 2021

Clinical Exper Ophthalmology

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Background In this systematic review and meta‐analysis, we aimed to compare deep learning versus ophthalmologists in glaucoma diagnosis on fundus examinations. Method PubMed, Cochrane, Embase, http://clinicaltrials.gov and ScienceDirect databases were searched for studies reporting a comparison between the glaucoma diagnosis performance of deep learning and ophthalmologists on fundus examinations on the same datasets, until 10 December 2020. Studies had to report an area under the receiver operating characteristics (AUC) with SD or enough data to generate one. Results We included six studies in our meta‐analysis. There was no difference in AUC between ophthalmologists (AUC = 82.0, 95% confidence intervals [CI] 65.4–98.6) and deep learning (97.0, 89.4–104.5). There was also no difference using several pessimistic and optimistic variants of our meta‐analysis: the best (82.2, 60.0–104.3) or worst (77.7, 53.1–102.3) ophthalmologists versus the best (97.1, 89.5–104.7) or worst (97.1, 88.5–105.6) deep learning of each study. We did not retrieve any factors influencing those results. Conclusion Deep learning had similar performance compared to ophthalmologists in glaucoma diagnosis from fundus examinations. Further studies should evaluate deep learning in clinical situations.

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Section: Discussionmentioning

confidence: 99%

Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta‐analysis

Buisson

Navel

Labbé

et al. 2021

Clinical Exper Ophthalmology

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“…The authors found that at four years of followup, the model identified 35% of the eyes as progressing versus only 15% for MD. In another study, Park et al 90 used a recurrent neural network and showed that it achieved better prediction of future visual field observations compared to ordinary least squares linear regression. Wen et al 91 also attempted to set up a deep learning model to predict future visual field observations based on the first visual field test only.…”

Section: Glaucoma Progressionmentioning

confidence: 99%

A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression

Thompson

Jammal

Medeiros

2020

Trans. Vis. Sci. Tech.

126

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Because of recent advances in computing technology and the availability of large datasets, deep learning has risen to the forefront of artificial intelligence, with performances that often equal, or sometimes even exceed, those of human subjects on a variety of tasks, especially those related to image classification and pattern recognition. As one of the medical fields that is highly dependent on ancillary imaging tests, ophthalmology has been in a prime position to witness the application of deep learning algorithms that can help analyze the vast amount of data coming from those tests. In particular, glaucoma stands as one of the conditions where application of deep learning algorithms could potentially lead to better use of the vast amount of information coming from structural and functional tests evaluating the optic nerve and macula. The purpose of this article is to critically review recent applications of deep learning models in glaucoma, discussing their advantages but also focusing on the challenges inherent to the development of such models for screening, diagnosis and detection of progression. After a brief general overview of deep learning and how it compares to traditional machine learning classifiers, we discuss issues related to the training and validation of deep learning models and how they specifically apply to glaucoma. We then discuss specific scenarios where deep learning has been proposed for use in glaucoma, such as screening with fundus photography, and diagnosis and detection of glaucoma progression with optical coherence tomography and standard automated perimetry. Translational Relevance Deep learning algorithms have the potential to significantly improve diagnostic capabilities in glaucoma, but their application in clinical practice requires careful validation, with consideration of the target population, the reference standards used to build the models, and potential sources of bias.

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“…RNN is a good network for extracting information features related to dynamic systems at the hidden layer [10]. Recurrent Neural Network have been used for prediction in several studies [11], [12]. The difference of RNN with NN lies in the value of the input not only from outside the network but added from the hidden layer output value.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Hamdianah

Mahmudy

Widaryanto

2021

JITeCS

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Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10,8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10,3804.

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Visual Field Prediction using Recurrent Neural Network

Cited by 58 publications

References 30 publications

Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta‐analysis

Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta‐analysis

A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

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