A deep learning approach to automatic detection of early glaucoma from visual fields

Kucur, Şerife Seda; Holló, Gábor; Sznitman, Raphael

doi:10.1371/journal.pone.0206081

Cited by 64 publications

(45 citation statements)

References 18 publications

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“…In a total of 345 eyes (156 glaucoma and 189 non-glaucoma), Goldbuam et al reported that the Gaussian (MoG) model, among the machine learning models, yielded the highest performance (AUC: 0.923) in detecting glaucoma on 24-2 humphrey VF. With the advent of deep learning, Kucur et al utilized numerical total deviation plots of 2267 24-2 VF samples (201 subjects) and a customized CNN to train the deep learning system 24 , showing a precision score of 0.874 that is better than other conventional machine learning models. For glaucoma progression.…”

Section: Discussionmentioning

confidence: 99%

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Song

Chen

et al. 2020

npj Digit. Med.

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By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of ‘iGlaucoma’, a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets—200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834–0.877, with a sensitivity of 0.831–0.922 and a specificity of 0.676–0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953–0.979), 0.954 (0.930–0.977), and 0.873 (0.838–0.908), respectively. The ‘iGlaucoma’ is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input.

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Section: Discussionmentioning

confidence: 99%

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Song

Chen

et al. 2020

npj Digit. Med.

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“…Kucur et al . 19 developed a convolutional neural network (CNN), a kind of deep-learning architecture, to discriminate early glaucoma from normal glaucoma. They used two visual field examinations as input data, OCTOPUS 101 perimeter and Humphrey visual field 24-1.…”

Section: Discussionmentioning

confidence: 99%

Visual Field Prediction using Recurrent Neural Network

Park

Kim

Lee

2019

Sci Rep

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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 6 th 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 6 th 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.

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“…CNNs have also been shown to discriminate between controls and early glaucoma on visual fields with a higher accuracy than use of standard perimetry mean deviation (MD) or neural networks without convolutional features. 72 …”

Section: Introductionmentioning

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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A deep learning approach to automatic detection of early glaucoma from visual fields

Cited by 64 publications

References 18 publications

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Visual Field Prediction using Recurrent Neural Network

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

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