Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography

Sandhu, Harpal S.; ElTanboly, Ahmed; Shalaby, Ahmed; Keynton, Robert; Schaal, Schlomit; El-Baz, Ayman

doi:10.1167/iovs.17-23677

Cited by 42 publications

(23 citation statements)

References 32 publications

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“…17,18,19,20 Thanks to deep learning applications, it is possible to develop software with databases containing over 100,000 data points. 21,22 There are examples of studies using machine learning methods with fundus photographs, machine learning with OCT, and deep learning methods with OCT. 19,23,24,25,26,27 Some of these studies have reported nearly 100% sensitivity or specificity rates. 28,29…”

Section: Artificial Intelligence and Diabetic Retinopathymentioning

confidence: 99%

Artificial Intelligence and Ophthalmology

Keskinbora¹,

Güven²

2020

tjo

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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.

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Section: Artificial Intelligence and Diabetic Retinopathymentioning

confidence: 99%

Artificial Intelligence and Ophthalmology

Keskinbora¹,

Güven²

2020

tjo

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“…Hence, accurate and early diagnosis of this disease can prevent the development of blindness. Detection of DR is done by examination of fundus and optical coherence tomography (OCT) images [1, 2].…”

Section: Introductionmentioning

confidence: 99%

“…Finally they used deep fusion classification network (DFCN) to classify normal or diabetic regions. Sandhu et al [2] presented a novel CAD system that segments the retina into 12 layers and then some global features such as curvature, reflectivity, and thickness measured. Finally, a two-stage, deep network is used to classify normal and abnormal areas.…”

Section: Introductionmentioning

confidence: 99%

Microaneurysm detection in fundus images using a two-step convolutional neural network

et al. 2019

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Background and objectives Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since microaneurysms (MA) are one of the main symptoms of the disease, distinguishing this complication within the fundus images facilitates early DR detection. In this paper, an automatic analysis of retinal images using convolutional neural network (CNN) is presented. Methods Our method incorporates a novel technique utilizing a two-stage process with two online datasets which results in accurate detection while solving the imbalance data problem and decreasing training time in comparison with previous studies. We have implemented our proposed CNNs using the Keras library. Results In order to evaluate our proposed method, an experiment was conducted on two standard publicly available datasets, i.e., Retinopathy Online Challenge dataset and E-Ophtha-MA dataset. Our results demonstrated a promising sensitivity value of about 0.8 for an average of >6 false positives per image, which is competitive with state of the art approaches. Conclusion Our method indicates significant improvement in MA-detection using retinal fundus images for monitoring diabetic retinopathy.

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“…Macular nonperfusion is a risk factor for disease progression, but patients are usually asymptomatic until advanced stages of disease ( 7 ). It is estimated that 38% of diabetics suffer from diabetic retinopathy ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

Optical Coherence Tomography Angiography Contributions in Classification of Nonproliferative Diabetic Retinopathy

et al. 2020

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Introduction: To show the importance of optical coherence tomography (OCT) angiography imaging of superficial and deep capillary network in patients with non-proliferative diabetic retinopathy (NPDR), and to show the correlation between blood glucose level and changes in the foveal microvasculature. Methods: A cross-sectional study was performed on eyes with NPDR and healthy subjects using a highspeed 840-nm-wavelength spectral-domain optical coherence tomography instrument (RTVue XR Avanti; Optovue, Inc, Fremont, California, USA). Blood flow was detected using the split-spectrum amplitude-decorrelation angiography algorithm. A fully automated microstructural analysis of the foveal avascular zone (FAZ) and avascular surfaces was performed. Quantitative values from diabetic patients were compared with those of healthy subjects. Data about blood status in diabetic patients and healthy subjects were taken from patients’ medical history. Results: Size of both, FAZ and vascular dropout are significantly different between healthy patients and patients with NPDR. OCT angiography detected enlargement and distortion of the foveal avascular zone, retinal capillary dropout, and a higher number of vascular loops and microaneurysms. Sizes of FAZ and vascular dropout increase with the duration of disease. Central macular thickness (CMT) is not significantly different between healthy patients and patients with NPDR. A study has proven a positive correlation between the size of FAZ and the size of vascular dropout in superficial vascular plexus in patients that have DM over 10 years. Conclusion: A qualitative and quantitative OCT angiography approach to retinal vascular status can offer objective data on monitoring patients with non-proliferative diabetic retinopathy as well as indicate the progression of the disease.

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Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography

Cited by 42 publications

References 32 publications

Artificial Intelligence and Ophthalmology

Artificial Intelligence and Ophthalmology

Microaneurysm detection in fundus images using a two-step convolutional neural network

Optical Coherence Tomography Angiography Contributions in Classification of Nonproliferative Diabetic Retinopathy

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