Abstract
The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology. A training dataset consisted of 1,364 color fundus photographs with glaucomatous indications and 1,768 color fundus photographs without glaucomatous features. A testing dataset consisted of 60 eyes of 60 glaucoma patients and 50 eyes of 50 normal subjects. Using the training dataset, a deep learning algorithm known as Deep Residual Learning for Image Recognition (ResNet) was developed to discriminate glaucoma, and its diagnostic accuracy was validated in the testing dataset, using the area under the receiver operating characteristic curve (AROC). The Deep Residual Learning for Image Recognition was constructed using the training dataset and validated using the testing dataset. The presence of glaucoma in the testing dataset was also confirmed by three Residents in Ophthalmology. The deep learning algorithm achieved significantly higher diagnostic performance compared to Residents in Ophthalmology; with ResNet, the AROC from all testing data was 96.5 (95% confidence interval [CI]: 93.5 to 99.6)% while the AROCs obtained by the three Residents were between 72.6% and 91.2%.
Highlights
Bilateral blindness was estimated to be present in 9.4 million people with glaucoma in 2010, and this number is expected to rise to 11.2 million people in 20201
A recent study suggested the usefulness of applying a deep learning method to diagnose glaucoma[8,9], it used a simple convolutional neural network (CNN), whereas more powerful deep learning methods, such as the Deep Residual Learning for Image Recognition (ResNet)[10], have become available
The potential impact of the deep residual learning algorithm for screening, and the early detection of glaucoma and prevention of blindness, cannot be overstated; fundus photography is commonly used at non-ophthalmological facilities, such as opticians, screening centers and internal medicine clinics
Summary
Bilateral blindness was estimated to be present in 9.4 million people with glaucoma in 2010, and this number is expected to rise to 11.2 million people in 20201. A considerable limitation of these ‘high-tech’ imaging devices is that they are usually available only at specialist eye clinics or hospitals; glaucoma sufferers – who have not visited these facilities – can persist without a diagnosis for many years. The purpose of the current study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photographs, and to validate its diagnostic performance using an independent dataset. Tilting of the ONH and thinning of the RNFL is associated with myopia[15,16] These changes make the detection of glaucoma a challenging task in myopic patients. The potential impact of the deep residual learning algorithm for screening, and the early detection of glaucoma and prevention of blindness, cannot be overstated; fundus photography is commonly used at non-ophthalmological facilities, such as opticians, screening centers and internal medicine clinics
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