Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs

AbstractPurposeTo assess the accuracy of deep learning models to predict glaucoma development from fundus photographs several years prior to disease onset.DesignA deep learning model for prediction of glaucomatous optic neuropathy or visual field abnormality from color fundus photographs.ParticipantsWe retrospectively included 66,721 fundus photographs from 3,272 eyes of 1,636 subjects to develop deep leaning models.MethodFundus photographs and visual fields were carefully examined by two independent readers from the optic disc and visual field reading centers of the ocular hypertension treatment study (OHTS). When an abnormality was detected by the readers, subject was recalled for re-testing to confirm the abnormality and further confirmation by an endpoint committee. Using OHTS data, deep learning models were trained and tested using 85% of the fundus photographs and further validated (re-tested) on the remaining (held-out) 15% of the fundus photographs.Main Outcome MeasuresAccuracy and area under the receiver-operating characteristic curve (AUC).ResultsThe AUC of the deep learning model in predicting glaucoma development 4-7 years prior to disease onset was 0.77 (95% confidence interval 0.75, 0.79). The accuracy of the model in predicting glaucoma development about 1-3 years prior to disease onset was 0.88 (0.86, 0.91). The accuracy of the model in detecting glaucoma after onset was 0.95 (0.94, 0.96).ConclusionsDeep learning models can predict glaucoma development prior to disease onset with reasonable accuracy. Eyes with visual field abnormality but not glaucomatous optic neuropathy had a higher tendency to be missed by deep learning algorithms.

Download Full-text

Considerations When Using a Deep Learning System to Diagnose Glaucomatous Optic Neuropathy

JAMA Ophthalmology ◽

10.1001/jamaophthalmol.2020.0023 ◽

2020 ◽

Vol 138 (4) ◽

pp. 419

Author(s):

Lin Sun

Keyword(s):

Deep Learning ◽

Optic Neuropathy ◽

Learning System ◽

Glaucomatous Optic Neuropathy

Download Full-text

Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2020-317327 ◽

2020 ◽

pp. bjophthalmol-2020-317327

Author(s):

Zhongwen Li ◽

Chong Guo ◽

Duoru Lin ◽

Danyao Nie ◽

Yi Zhu ◽

...

Keyword(s):

Deep Learning ◽

Optic Neuropathy ◽

Network Architecture ◽

Characteristic Curve ◽

False Negative ◽

Learning System ◽

Glaucomatous Optic Neuropathy ◽

Pathological Myopia ◽

Data Set ◽

Negative Results

Background/AimsTo develop a deep learning system for automated glaucomatous optic neuropathy (GON) detection using ultra-widefield fundus (UWF) images.MethodsWe trained, validated and externally evaluated a deep learning system for GON detection based on 22 972 UWF images from 10 590 subjects that were collected at 4 different institutions in China and Japan. The InceptionResNetV2 neural network architecture was used to develop the system. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were used to assess the performance of detecting GON by the system. The data set from the Zhongshan Ophthalmic Center (ZOC) was selected to compare the performance of the system to that of ophthalmologists who mainly conducted UWF image analysis in clinics.ResultsThe system for GON detection achieved AUCs of 0.983–0.999 with sensitivities of 97.5–98.2% and specificities of 94.3–98.4% in four independent data sets. The most common reasons for false-negative results were confounding optic disc characteristics caused by high myopia or pathological myopia (n=39 (53%)). The leading cause for false-positive results was having other fundus lesions (n=401 (96%)). The performance of the system in the ZOC data set was comparable to that of an experienced ophthalmologist (p>0.05).ConclusionOur deep learning system can accurately detect GON from UWF images in an automated fashion. It may be used as a screening tool to improve the accessibility of screening and promote the early diagnosis and management of glaucoma.

Download Full-text

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

Scientific Reports ◽

10.1038/s41598-018-35044-9 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 55

Author(s):

Mark Christopher ◽

Akram Belghith ◽

Christopher Bowd ◽

James A. Proudfoot ◽

Michael H. Goldbaum ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Optic Neuropathy ◽

Glaucomatous Optic Neuropathy ◽

Fundus Photographs ◽

Learning Architectures

Download Full-text

Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs

Graefe s Archive for Clinical and Experimental Ophthalmology ◽

10.1007/s00417-020-04609-8 ◽

2020 ◽

Vol 258 (4) ◽

pp. 851-867 ◽

Cited By ~ 1

Author(s):

Feng Li ◽

Lei Yan ◽

Yuguang Wang ◽

Jianxun Shi ◽

Hua Chen ◽

...

Keyword(s):

Deep Learning ◽

Optic Neuropathy ◽

Automated Detection ◽

Glaucomatous Optic Neuropathy ◽

Fundus Photographs

Download Full-text

Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems

American Journal of Ophthalmology ◽

10.1016/j.ajo.2020.03.035 ◽

2020 ◽

Vol 216 ◽

pp. 140-146

Author(s):

Hee Kyung Yang ◽

Young Jae Kim ◽

Jae Yun Sung ◽

Dong Hyun Kim ◽

Kwang Gi Kim ◽

...

Keyword(s):

Deep Learning ◽

Optic Neuropathy ◽

Learning Systems ◽

Glaucomatous Optic Neuropathy

Download Full-text

Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2020-316290 ◽

2021 ◽

pp. bjophthalmol-2020-316290

Author(s):

Bing Li ◽

Huan Chen ◽

Bilei Zhang ◽

Mingzhen Yuan ◽

Xuemin Jin ◽

...

Keyword(s):

Deep Learning ◽

Learning System ◽

Age Related Macular Degeneration ◽

Fundus Photography ◽

Glaucomatous Optic Neuropathy ◽

Test Set ◽

Receiver Operating Characteristic Curves ◽

Age Related ◽

Test Sets ◽

External Test

AimTo explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography.MethodsDiagnostic performance of a DLS was tested on the detection of normal fundus and 12 major fundus diseases including referable diabetic retinopathy, pathologic myopic retinal degeneration, retinal vein occlusion, retinitis pigmentosa, retinal detachment, wet and dry age-related macular degeneration, epiretinal membrane, macula hole, possible glaucomatous optic neuropathy, papilledema and optic nerve atrophy. The DLS was developed with 56 738 images and tested with 8176 images from one internal test set and two external test sets. The comparison with human doctors was also conducted.ResultsThe area under the receiver operating characteristic curves of the DLS on the internal test set and the two external test sets were 0.950 (95% CI 0.942 to 0.957) to 0.996 (95% CI 0.994 to 0.998), 0.931 (95% CI 0.923 to 0.939) to 1.000 (95% CI 0.999 to 1.000) and 0.934 (95% CI 0.929 to 0.938) to 1.000 (95% CI 0.999 to 1.000), with sensitivities of 80.4% (95% CI 79.1% to 81.6%) to 97.3% (95% CI 96.7% to 97.8%), 64.6% (95% CI 63.0% to 66.1%) to 100% (95% CI 100% to 100%) and 68.0% (95% CI 67.1% to 68.9%) to 100% (95% CI 100% to 100%), respectively, and specificities of 89.7% (95% CI 88.8% to 90.7%) to 98.1% (95%CI 97.7% to 98.6%), 78.7% (95% CI 77.4% to 80.0%) to 99.6% (95% CI 99.4% to 99.8%) and 88.1% (95% CI 87.4% to 88.7%) to 98.7% (95% CI 98.5% to 99.0%), respectively. When compared with human doctors, the DLS obtained a higher diagnostic sensitivity but lower specificity.ConclusionThe proposed DLS is effective in diagnosing normal fundus and 12 major fundus diseases, and thus has much potential for fundus diseases screening in the real world.

Download Full-text