Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs

Chakrabarty, Lipi; Joshi, Gopal Datt; Chakravarty, Arunava; Raman, Ganesh V; Krishnadas, Subbaiah Ramasamy; Sivaswamy, Jayanthi

doi:10.1097/ijg.0000000000000354

Cited by 29 publications

(26 citation statements)

References 23 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, structural variations such as tilted nerves, PPA and poor image acquisition further complicate automated fundus photo processing . Nonetheless, efforts to automate glaucoma assessment of fundus photos have made significant progress …”

Section: Alternatives To Optic Nerve Analysis By Physiciansmentioning

confidence: 99%

“…56 Nonetheless, efforts to automate glaucoma assessment of fundus photos have made significant progress. 57 Fundus photograph processing of the optic nerve generally consists of two parts: localization and segmentation. Localization, as its name suggests, is the process of determining the location of the optic nerve.…”

Section: Alternatives To Optic Nerve Analysis By Physiciansmentioning

confidence: 99%

See 1 more Smart Citation

Evolution of optic nerve photography for glaucoma screening: a review

Myers

Fudemberg

Lee

2018

Clinical Exper Ophthalmology

View full text Add to dashboard Cite

Visual evaluation of the optic nerve has been one of the earliest and most widely used methods to evaluate patients for glaucoma. Photography has proven very useful for documentation of the nerve's appearance at a given time, allowing more detailed scrutiny then, and later comparison for change. Photography serves as the basis for real-time or non-simultaneous review in telemedicine and screening events allowing fundus and optic nerve evaluation by experts elsewhere. Expert evaluation of disc photographs has shown diagnostic performance similar to other methods of optic nerve evaluation for glaucoma. Newer technology has made optic nerve photography simpler, cheaper and more portable creating opportunities for broader utilization in screening in underserved populations by non-physicians. Recent investigations suggest that non-physicians or software algorithms for disc photograph evaluation have promise to allow more screening to be done with fewer experts.

show abstract

Section: Alternatives To Optic Nerve Analysis By Physiciansmentioning

confidence: 99%

Section: Alternatives To Optic Nerve Analysis By Physiciansmentioning

confidence: 99%

Evolution of optic nerve photography for glaucoma screening: a review

Myers

Fudemberg

Lee

2018

Clinical Exper Ophthalmology

View full text Add to dashboard Cite

show abstract

“…15 Initially, studies using machine learning to identify glaucomatous optic nerve damage based on fundus photographs were published. 35,36,37 These were followed by studies that used deep learning technology with much larger databases compared to the earlier machine learning studies. 20,38,39 In another study using a database of 125,189 fundus photographs, Ting et al 20 reported a sensitivity of 96.4% and specificity of 87.2%.…”

Section: Artificial Intelligence and Glaucomamentioning

confidence: 99%

Artificial Intelligence and Ophthalmology

Keskinbora¹,

Güven²

2020

tjo

View full text Add to dashboard Cite

What is Artificial Intelligence? Artificial intelligence, described simply, is the ability of a computer to mimic the intellectual intelligence unique to humans. This type of intelligence includes qualities such as the ability to link events to specific causes, make generalizations, and learn from experience. 1 As a general public notion, the term is used to describe devices that can provide a reason for a certain phenomenon, develop strategies, make judgments about situations, and have the ability to learn. However, there are ongoing controversies regarding the level and reliability of this intelligence. 2 Many different theories on how to evaluate machine intelligence have been proposed. The most famous of these is the Turing test, which was put forward in 1950 by Alan Turing, an English mathematician, computer scientist, and cryptologist. Artificial intelligence is advancing rapidly and making its way into all areas of our lives. This review discusses developments and potential practices regarding the use of artificial intelligence in the field of ophthalmology, and the related topic of medical ethics. Various artificial intelligence applications related to the diagnosis of eye diseases were researched in books, journals, search engines, print and social media. Resources were cross-checked to verify the information. Artificial intelligence algorithms, some of which were approved by the US Food and Drug Administration, have been adopted in the field of ophthalmology, especially in diagnostic studies. Studies are being conducted that prove that artificial intelligence algorithms can be used in the field of ophthalmology, especially in diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. Some of these algorithms have come to the approval stage. The current point in artificial intelligence studies shows that this technology has advanced considerably and shows promise for future work. It is believed that artificial intelligence applications will be effective in identifying patients with preventable vision loss and directing them to physicians, especially in developing countries where there are fewer trained professionals and physicians are difficult to reach. When we consider the possibility that some future artificial intelligence systems may be candidates for moral/ethical status, certain ethical issues arise. Questions about moral/ethical status are important in some areas of applied ethics. Although it is accepted that current intelligence systems do not have moral/ethical status, it has yet to be determined what the exact the characteristics that confer moral/ethical status are or will be.

show abstract

“…In the right eye, sectors N and T are on the right and left sides, and vice versa for the left eye. The thickest and most clearly visible parts of the RNFL structure are located in sectors I and S [ 2 ]. An overview of the retinal texture, which shows the differences between areas with and without (loss) RNFL and also the sector partition on retinal fundus image of the right eye, is shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation

et al. 2018

View full text Add to dashboard Cite

ObjectivesThe retinal nerve fiber layer (RNFL) is a site of glaucomatous optic neuropathy whose early changes need to be detected because glaucoma is one of the most common causes of blindness. This paper proposes an automated RNFL detection method based on the texture feature by forming a co-occurrence matrix and a backpropagation neural network as the classifier.MethodsWe propose two texture features, namely, correlation and autocorrelation based on a co-occurrence matrix. Those features are selected by using a correlation feature selection method. Then the backpropagation neural network is applied as the classifier to implement RNFL detection in a retinal fundus image.ResultsWe used 40 retinal fundus images as testing data and 160 sub-images (80 showing a normal RNFL and 80 showing RNFL loss) as training data to evaluate the performance of our proposed method. Overall, this work achieved an accuracy of 94.52%.ConclusionsOur results demonstrated that the proposed method achieved a high accuracy, which indicates good performance.

show abstract

Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs

Abstract: An automated system was presented for glaucoma detection from color fundus photographs. The overall evaluation results indicated that the presented system was comparable in performance to glaucoma classification by a manual grader solely based on fundus image examination.

Cited by 29 publications

References 23 publications

Evolution of optic nerve photography for glaucoma screening: a review

Evolution of optic nerve photography for glaucoma screening: a review

Artificial Intelligence and Ophthalmology

Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation

Contact Info

Product

Resources

About