Corpus ID: 56657796

Deep Learning to Assess Glaucoma Risk and Associated Features in Fundus Images

  title={Deep Learning to Assess Glaucoma Risk and Associated Features in Fundus Images},
  author={Sonia Phene and R. Carter Dunn and Naama Hammel and Yun Liu and Jonathan Krause and Naho Kitade and Mike Schaekermann and Rory Sayres and Derek J. Wu and Ashish Bora and Christopher Semturs and Anita Misra and Abigail E. Huang and Arielle Spitze and Felipe A. Medeiros and April Y. Maa and Monica Gandhi and Greg S Corrado and Lily H. Peng and Dale R. Webster},
Glaucoma is the leading cause of preventable, irreversible blindness world-wide. The disease can remain asymptomatic until severe, and an estimated 50%-90% of people with glaucoma remain undiagnosed. Thus, glaucoma screening is recommended for early detection and treatment. A cost-effective tool to detect glaucoma could expand healthcare access to a much larger patient population, but such a tool is currently unavailable. We trained a deep learning (DL) algorithm using a retrospective dataset… Expand
3 Citations
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
It is observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task, and the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results. Expand
Deep learning assisted detection of glaucomatous optic neuropathy and potential designs for a generalizable model
The ensemble model or a fine-tuned CNN classifier may be potential designs to build a generalizable deep learning model for glaucoma detection when large image databases are not available. Expand
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A deep learning system can detect referable GON with high sensitivity and specificity and coexistence of high or pathologic myopia is the most common cause resulting in false-negative results. Expand
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes. Expand
Development of a deep residual learning algorithm to screen for glaucoma from fundus photography
A deep residual learning algorithm was developed to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology, and its diagnostic accuracy was validated in the testing dataset, using the area under the receiver operating characteristic curve (AROC). Expand
Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
The results suggest that deep learning-based assessment of fundus images could be useful in clinical decision support systems and in the automation of large-scale glaucoma detection and screening programs. Expand
Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes
In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Expand
A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs.
This study demonstrated that a deep learning-based algorithm can identify glaucomatous discs at high accuracy level using monoscopic fundus images, highlighting the algorithm's potential application in large population-based disease screening or telemedicine programs. Expand
The Effectiveness of Teleglaucoma versus In-Patient Examination for Glaucoma Screening: A Systematic Review and Meta-Analysis
Teleglaucoma can accurately discriminate between screen test results with greater odds for positive cases, and is an effective screening tool for glaucomas specifically for remote and under-services communities. Expand
Do findings on routine examination identify patients at risk for primary open-angle glaucoma? The rational clinical examination systematic review.
Individual findings of increased CDR, CDR asymmetry, disc hemorrhage, and elevated IOP, as well as demographic risk factors of family history, black race, and advanced age are associated with increased risk for POAG, but their absence does not effectively rule out POAG. Expand
Primary open-angle glaucoma
Primary open-angle glaucoma (POAG) is the most common type and management of POAG includes topical drug therapies and surgery to reduce IOP, although new therapies targeting neuroprotection of RGCs and axonal regeneration are under development. Expand
Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy.
Deep learning algorithms can improve the accuracy of, and confidence in, DR diagnosis in an assisted read setting, and they also may increase grading time, although these effects may be ameliorated with experience. Expand