&lt;p&gt;The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System&lt;/p&gt;

Almazroa, Ahmed; Woodward, Maria A.; Newman-Casey, Paula Anne; Shah, Manjool; Elam, Angela R.; Kamat, Shivani; Karvonen‐Gutierrez, Carrie; Wood, Sarah Dougherty; Kumar, Navasuja; Moroi, Sayoko E.

doi:10.2147/opth.s273659

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Researchers have estimated that 50% of people in the US remain undiagnosed. Thus, if current screening methods are continued, 2.1 million US citizens will remain undiagnosed by 2030 [ 4 ]. Telemedicine innovations can overcome glaucoma screening shortcomings and may facilitate the detection of early-stage cases.…”

Section: Introductionmentioning

confidence: 99%

Peripapillary atrophy classification using CNN deep learning for glaucoma screening

Almansour

Alawad²,

Aljouie³

et al. 2022

PLoS ONE

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Glaucoma is the second leading cause of blindness worldwide, and peripapillary atrophy (PPA) is a morphological symptom associated with it. Therefore, it is necessary to clinically detect PPA for glaucoma diagnosis. This study was aimed at developing a detection method for PPA using fundus images with deep learning algorithms to be used by ophthalmologists or optometrists for screening purposes. The model was developed based on localization for the region of interest (ROI) using a mask region-based convolutional neural networks R-CNN and a classification network for the presence of PPA using CNN deep learning algorithms. A total of 2,472 images, obtained from five public sources and one Saudi-based resource (King Abdullah International Medical Research Center in Riyadh, Saudi Arabia), were used to train and test the model. First the images from public sources were analyzed, followed by those from local sources, and finally, images from both sources were analyzed together. In testing the classification model, the area under the curve’s (AUC) scores of 0.83, 0.89, and 0.87 were obtained for the local, public, and combined sets, respectively. The developed model will assist in diagnosing glaucoma in screening programs; however, more research is needed on segmenting the PPA boundaries for more detailed PPA detection, which can be combined with optic disc and cup boundaries to calculate the cup-to-disc ratio.

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Section: Introductionmentioning

confidence: 99%

Peripapillary atrophy classification using CNN deep learning for glaucoma screening

Almansour

Alawad²,

Aljouie³

et al. 2022

PLoS ONE

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Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review

Alawad¹,

Aljouie²,

Alamri³

et al. 2022

OPTH

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Background Globally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs. Methods A systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed. Results Eight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation. Conclusion We provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.

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<p>The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System</p>

Cited by 2 publications

References 20 publications

Peripapillary atrophy classification using CNN deep learning for glaucoma screening

Peripapillary atrophy classification using CNN deep learning for glaucoma screening

Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review

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