Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs

Liu, Xinle; Ali, Tayyeba K.; Singh, Preeti; Shah, Ami; McKinney, Scott Mayer; Ruamviboonsuk, Paisan; Turner, Angus; Keane, Pearse A.; Chotcomwongse, Peranut; Nganthavee, Variya; Chia, Mark A.; Huemer, Josef; Cuadros, Jorge; Raman, Rajiv; Corrado, Greg S.; Peng, Lily; Webster, Dale R.; Hammel, Naama; Varadarajan, Avinash V.; Liu, Yun; Chopra, Reena; Bavishi, Pinal

doi:10.1016/j.oret.2021.12.021

Cited by 32 publications

(14 citation statements)

References 38 publications

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“…The center is staffed by two full-time ophthalmologists and a range of other health staff including Aboriginal Health Workers and optometrists. Our team is currently engaged in several AI projects focusing on DR screening, detecting macular edema, ( 18 ) and analyzing optical coherence tomography angiography linking systemic risk factors.…”

Section: Beyond Telemedicine: the Role Of Ai In Rural Eye Carementioning

confidence: 99%

Benefits of Integrating Telemedicine and Artificial Intelligence Into Outreach Eye Care: Stepwise Approach and Future Directions

Chia

Turner

2022

Front. Med.

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Telemedicine has traditionally been applied within remote settings to overcome geographical barriers to healthcare access, providing an alternate means of connecting patients to specialist services. The coronavirus 2019 pandemic has rapidly expanded the use of telemedicine into metropolitan areas and enhanced global telemedicine capabilities. Through our experience of delivering real-time telemedicine over the past decade within a large outreach eye service, we have identified key themes for successful implementation which may be relevant to services facing common challenges. We present our journey toward establishing a comprehensive teleophthalmology model built on the principles of collaborative care, with a focus on delivering practical lessons for service design. Artificial intelligence is an emerging technology that has shown potential to further address resource limitations. We explore the applications of artificial intelligence and the need for targeted research within underserved settings in order to meet growing healthcare demands. Based on our rural telemedicine experience, we make the case that similar models may be adapted to urban settings with the aim of reducing surgical waitlists and improving efficiency.

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Section: Beyond Telemedicine: the Role Of Ai In Rural Eye Carementioning

confidence: 99%

Benefits of Integrating Telemedicine and Artificial Intelligence Into Outreach Eye Care: Stepwise Approach and Future Directions

Chia

Turner

2022

Front. Med.

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“…In addition, the model was validated in an international multicenter study and proved to have an accuracy exceeding that of experts and generalization ability to multiple international populations. 36 Further, a study by Arcadu et al 37 showed that DL could not only identify fundus color photographs with macular thickness > 250 µm but also had the potential to predict specific values of fovea macular thickness from fundus photographs. Regarding hardware, Schramm et al 38 achieved 3-dimensional (3D) retinal imaging based on light field technology, and they successfully estimated the depth of the retina in one-shot imaging with an accuracy comparable to OCT measurement.…”

Section: Application Of Ai In Dme Screeningmentioning

confidence: 99%

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

Zhang

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Diabetic macular edema (DME) is the primary cause of central vision impairment in patients with diabetes and the leading cause of preventable blindness in working-age people. With the advent of optical coherence tomography and antivascular endothelial growth factor (anti-VEGF) therapy, the diagnosis, evaluation, and treatment of DME were greatly revolutionized in the last decade. However, there is tremendous heterogeneity among DME patients, and 30%–50% of DME patients do not respond well to anti-VEGF agents. In addition, there is no evidence-based and universally accepted administration regimen. The identification of DME patients not responding to anti-VEGF agents and the determination of the optimal administration interval are the 2 major challenges of DME, which are difficult to achieve with the coarse granularity of conventional health care modality. Therefore, more and more retina specialists have pointed out the necessity of introducing precision medicine into the management of DME and have conducted related studies in recent years. One of the most frontier methods is the targeted extraction of individualized disease features from optical coherence tomography images based on artificial intelligence technology, which provides precise evaluation and risk classification of DME. This review aims to provide an overview of the progress of artificial intelligence-enabled precision medicine in automated screening, precise evaluation, prognosis prediction, and follow-up monitoring of DME. Further, the challenges ahead of real-world applications and the future development of precision medicine in DME will be discussed.

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“…AI has the potential to detect disease in early (even asymptomatic) stages, classify it, predict the disease course, and thus guide treatment in select eyes (404)(405)(406). Tools can match or even outperform physicians and can make access to screening broader and less expensive; algorithms and devices are already clinically available (IDx-DR by Digital Diagnostics, Coralville, IA, United States; SELENA+ by EyRIS, Singapore) and have been authorized for use in multiple fundus cameras (407)(408)(409)(410)(411)(412). Training models for AI has been steadily increasing for diagnosing DR from fundus pictures, with accuracy, sensitivity, and specificity improving over time (reaching 95.7, 97.5, and 98%, respectively) (412)(413)(414)(415)(416).…”

Section: Artificial Intelligence and Diabetic Retinopathymentioning

confidence: 99%

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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Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs

Cited by 32 publications

References 38 publications

Benefits of Integrating Telemedicine and Artificial Intelligence Into Outreach Eye Care: Stepwise Approach and Future Directions

Benefits of Integrating Telemedicine and Artificial Intelligence Into Outreach Eye Care: Stepwise Approach and Future Directions

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

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