A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography

Ryu, Gahyung; Lee, Kyung-Min; Park, Dong-Geun; Park, Sang Hyun; Sagong, Min

doi:10.1038/s41598-021-02479-6

Cited by 49 publications

(28 citation statements)

References 49 publications

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“…Similar results are also seen when using CNNs for DR severity grading; Ryu et al have recently developed a fully automated algorithm to classify DR stages with accuracy of 91-98%, sensitivity of 86-97%, and specificity of 94-99% (417,418). Application of these models can also establish biomarkers useful for diagnosis and treatment response; these could be structural (retinal layer measurement, hyperreflective foci) or vascular (areas of non-perfusion, vascular leakage, microaneurysm count, and neovascularization) (387).…”

Section: Artificial Intelligence and Diabetic Retinopathysupporting

confidence: 56%

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

et al. 2022

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The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

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Section: Artificial Intelligence and Diabetic Retinopathysupporting

confidence: 56%

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

et al. 2022

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“…Different DL-models assess OCTA image quality assessment [ 142 ], object segmentation [ 143 ], and quantification [ 144 ], with high accuracies. Ryu et al developed a convolutional neural network (CNN) model classification algorithm with a sensitivity of 86–97%, a specificity of 94–99%, and an accuracy of 91–98% to diagnose DR through OCTA [ 145 ]. Le et al’s DL classifier differentiated among healthy, no DR, and DR eyes with 83.76% sensitivity, 90.82% specificity, and an 87.27% accuracy [ 146 ] and Heisler et al achieved an accuracy of between 90% and 92% [ 147 ].…”

Section: Resultsmentioning

confidence: 99%

Optical Coherence Tomography Angiography in Diabetic Patients: A Systematic Review

et al. 2021

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Background: Diabetic retinopathy (DR) is the leading cause of legal blindness in the working population in developed countries. Optical coherence tomography (OCT) angiography (OCTA) has risen as an essential tool in the diagnosis and control of diabetic patients, with and without DR, allowing visualisation of the retinal and choroidal microvasculature, their qualitative and quantitative changes, the progression of vascular disease, quantification of ischaemic areas, and the detection of preclinical changes. The aim of this article is to analyse the current applications of OCTA and provide an updated overview of them in the evaluation of DR. Methods: A systematic literature search was performed in PubMed and Embase, including the keywords “OCTA” OR “OCT angiography” OR “optical coherence tomography angiography” AND “diabetes” OR “diabetes mellitus” OR “diabetic retinopathy” OR “diabetic maculopathy” OR “diabetic macular oedema” OR “diabetic macular ischaemia”. Of the 1456 studies initially identified, 107 studies were screened after duplication, and those articles that did not meet the selection criteria were removed. Finally, after looking for missing data, we included 135 studies in this review. Results: We present the common and distinctive findings in the analysed papers after the literature search including the diagnostic use of OCTA in diabetes mellitus (DM) patients. We describe previous findings in retinal vascularization, including microaneurysms, foveal avascular zone (FAZ) changes in both size and morphology, changes in vascular perfusion, the appearance of retinal microvascular abnormalities or new vessels, and diabetic macular oedema (DME) and the use of deep learning technology applied to this disease. Conclusion: OCTA findings enable the diagnosis and follow-up of DM patients, including those with no detectable lesions with other devices. The evaluation of retinal and choroidal plexuses using OCTA is a fundamental tool for the diagnosis and prognosis of DR.

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“…DL system from Google AI Healthcare identified image features to grade fundus lesions derived from 128,175 retinal images (labeled by 54 ophthalmologists) and discovered that these image features could quickly identify DR and identify signs of DR. Ting et al reported a clinically acceptable diagnostic performance with an AUC of 93.6%, sensitivity of 90.5%, and specificity of 91.6%, in detecting DR using a development dataset acquired from Singapore integrated DR Program and several external datasets from six different countries (13). In another study, investigators from Aalto University trained a DL model based on Inception-v3 and found that DL could accurately separate DR and macular edema (35) (38).…”

Section: Diabetic Retinopathymentioning

confidence: 99%

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases

Sheng

Chen

et al. 2022

Front. Public Health

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Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

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A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography

Cited by 49 publications

References 49 publications

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

Optical Coherence Tomography Angiography in Diabetic Patients: A Systematic Review

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases

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