glaucoma screening
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Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 434
Author(s):  
Marriam Nawaz ◽  
Tahira Nazir ◽  
Ali Javed ◽  
Usman Tariq ◽  
Hwan-Seung Yong ◽  
...  

Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.


Author(s):  
Hitendra Garg ◽  
Neeraj Gupta ◽  
Rohit Agrawal ◽  
Shivendra Shivani ◽  
Bhisham Sharma
Keyword(s):  

2021 ◽  
Vol Volume 15 ◽  
pp. 4855-4863
Author(s):  
Patrick C Staropoli ◽  
Richard K Lee ◽  
Zachary A Kroeger ◽  
Karina Somohano ◽  
Matthew Feldman ◽  
...  

Cureus ◽  
2021 ◽  
Author(s):  
Karen Allison ◽  
Deepkumar Patel ◽  
Caren Besharim
Keyword(s):  

Author(s):  
Guangmin Sun ◽  
Zhongxiang Zhang ◽  
Junjie Zhang ◽  
Meilong Zhu ◽  
Xiao-rong Zhu ◽  
...  

AbstractAutomatic segmentation of optic disc (OD) and optic cup (OC) is an essential task for analysing colour fundus images. In clinical practice, accurate OD and OC segmentation assist ophthalmologists in diagnosing glaucoma. In this paper, we propose a unified convolutional neural network, named ResFPN-Net, which learns the boundary feature and the inner relation between OD and OC for automatic segmentation. The proposed ResFPN-Net is mainly composed of multi-scale feature extractor, multi-scale segmentation transition and attention pyramid architecture. The multi-scale feature extractor achieved the feature encoding of fundus images and captured the boundary representations. The multi-scale segmentation transition is employed to retain the features of different scales. Moreover, an attention pyramid architecture is proposed to learn rich representations and the mutual connection in the OD and OC. To verify the effectiveness of the proposed method, we conducted extensive experiments on two public datasets. On the Drishti-GS database, we achieved a Dice coefficient of 97.59%, 89.87%, the accuracy of 99.21%, 98.77%, and the Averaged Hausdorff distance of 0.099, 0.882 on the OD and OC segmentation, respectively. We achieved a Dice coefficient of 96.41%, 83.91%, the accuracy of 99.30%, 99.24%, and the Averaged Hausdorff distance of 0.166, 1.210 on the RIM-ONE database for OD and OC segmentation, respectively. Comprehensive results show that the proposed method outperforms other competitive OD and OC segmentation methods and appears more adaptable in cross-dataset scenarios. The introduced multi-scale loss function achieved significantly lower training loss and higher accuracy compared with other loss functions. Furthermore, the proposed method is further validated in OC to OD ratio calculation task and achieved the best MAE of 0.0499 and 0.0630 on the Drishti-GS and RIM-ONE datasets, respectively. Finally, we evaluated the effectiveness of the glaucoma screening on Drishti-GS and RIM-ONE datasets, achieving the AUC of 0.8947 and 0.7964. These results proved that the proposed ResFPN-Net is effective in analysing fundus images for glaucoma screening and can be applied in other relative biomedical image segmentation applications.


Author(s):  
Jamie Prince ◽  
Atalie Thompson ◽  
Jean-Claude Mwanza ◽  
Sue Tolleson-Rinehart ◽  
Donald L. Budenz

2021 ◽  
Author(s):  
Lisika Gawas ◽  
Avik Kumar Roy ◽  
Aparna Rao

Abstract Purpose: To compare the glaucoma assessment skills among general ophthalmologists in their referral patients over a span of 5 years.Methods: Details of consecutive new glaucoma patients seen in the glaucoma services of a tertiary eye care institute in 2013 and 2018 were collected from the hospital database. Details of each patient were obtained from the electronic medical records which included the clinical presentation, baseline intraocular pressure (IOP), type and severity of glaucoma, referral details, gonioscopy, HVF (humphrey visual field) data and number of medications. Results: Of 28,886 medical records screened, 211 & 568 new glaucoma patients were retrieved in 2013 & 2018 respectively. The patients presenting in 2018 were younger (58.1±15.4 years) at presentation than in 2013 (65.6±15.2 years); p<0.01 and also had higher baseline IOP (IOP ≥40mm Hg was found in 9.5% in 2018 versus 2.4% in 2013; p<0.01). The percentage of eyes with presenting visual acuity worse than 20/400 or 20/600 was higher in the patients presenting in 2018, (22.2% vs. 15.1%); p=0.03. Though primary glaucoma predominated in both periods, number of eyes referred to as disc suspects showed an increase in 2018 (4.7% to 14.4%; p<0.01). Among 195 & 517 referrals in 2013 and 2018 respectively, the documentation of clinical findings were dismally poor in both the groups in terms of absent gonioscopy (99% vs. 98.2%, p=0.4), absent disc details (89.6% vs. 91%, p=0.5) or absent visual field analysis (79.1% vs. 74.8%, p=0.2). However, the missing of IOP values was significantly better in the latter year (77.3% vs. 57.2%; p<0.01) Conclusion: The increase in number of new glaucoma patients and referrals did not show a corresponding improvement in documentation of findings except IOP recording among general ophthalmologists. Hence we need to re-emphasize on the training of general ophthalmologists on basic glaucoma evaluation to improve their referral ability.


2021 ◽  
pp. 1-11
Author(s):  
Olusola Olawoye ◽  
Augusto Azuara-Blanco ◽  
Ving Fai Chan ◽  
Prabhath Piyasena ◽  
Grainne E. Crealey ◽  
...  

2021 ◽  
Vol 10 (16) ◽  
pp. 3452
Author(s):  
Sze H. Wong ◽  
James C. Tsai

Telehealth has become a viable option for glaucoma screening and glaucoma monitoring due to advances in technology. The ability to measure intraocular pressure without an anesthetic and to take optic nerve photographs without pharmacologic pupillary dilation using portable equipment have allowed glaucoma screening programs to generate enough data for assessment. At home, patients can perform visual acuity testing, web-based visual field testing, rebound tonometry, and video visits with the physician to monitor for glaucomatous progression. Artificial intelligence will enhance the accuracy of data interpretation and inspire confidence in popularizing telehealth for glaucoma.


2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Natasha Nayak Kolomeyer ◽  
L. Jay Katz ◽  
Lisa A. Hark ◽  
Madison Wahl ◽  
Prateek Gajwani ◽  
...  

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