Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

Ting, Daniel Shu Wei; Cheung, Carol Y.; Lim, Gilbert; Tan, Gavin Siew Wei; Quang, Nguyễn Tiến; Gan, Alfred Tau Liang; Hamzah, Haslina; García-Franco, Renata; Yeo, Ian; Lee, Shu Yen; Wong, Edmund; Sabanayagam, Charumathi; Baskaran, Mani; Ibrahim, Farah; Tan, Ngiap Chuan; Finkelstein, Eric A.; Lamoureux, Ecosse L.; Wong, Ian Yu‐Hong; Bressler, Neil M.; Sivaprasad, Sobha; Varma, Rohit; Jonas, Jost B.; He, Mingguang; Cheng, Ching‐Yu; Cheung, Gemmy; Aung, Tin; Hsu, Wynne; Lee, Mong Li; Wong, Tien Yin

doi:10.1001/jama.2017.18152

Cited by 1,711 publications

(1,320 citation statements)

References 37 publications

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“…His group included 108 312 images (from 4686 patients) to train the MLC and a separate 1000 images (from 633 patients) to validate its capabilities. 38,[46][47][48] Ultimately, as the MLCs perform at a level similar to professional graders, it appears that an MLC created using a CNN could be a useful system for screening for AMD. The MLC achieved a sensitivity of 0.978, a specificity of 0.974 and a AUROC of 0.999 in its task of retinal disease classification.…”

Section: Assessment Of Age-related Macular Degenerationmentioning

confidence: 99%

Section: Gauging Glaucomamentioning

confidence: 99%

“…38 The focus of this study was to determine whether there was any additional benefit to maintaining the highresolution images and red, green, blue colour scheme when learning from the images. 38 These fundus photographs were taken from a multi-ethnic population, which may increase the external validity. This is roughly equivalent to the accuracy of human experts (quoted at around 80%), suggesting promise for this method.…”

Section: Gauging Glaucomamentioning

confidence: 99%

“…Many studies have looked at AI programs that can detect and even triage fundoscopic images with DR (Table 1). 3,38,61 These studies all used large datasets to create the MLCs. [58][59][60] This has culminated in a few significant AI MLCs being created that demonstrate classification results similar to trained graders and ophthalmologists.…”

Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

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Current state and future prospects of artificial intelligence in ophthalmology: a review

Hogarty

Mackey

Hewitt

2018

Clinical Exper Ophthalmology

144

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Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has broad application across many medical fields, it will have particular utility in ophthalmology and will dramatically change the diagnostic and treatment pathways for many eye conditions such as corneal ectasias, glaucoma, age-related macular degeneration and diabetic retinopathy. However, given that AI has primarily been driven as a computer science, its concepts and terminology are unfamiliar to many medical professionals. Important key terms such as machine learning and deep learning are often misunderstood and incorrectly used interchangeably. This article presents an overview of AI and new developments relevant to ophthalmology.

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Section: Assessment Of Age-related Macular Degenerationmentioning

confidence: 99%

Section: Gauging Glaucomamentioning

confidence: 99%

Section: Gauging Glaucomamentioning

confidence: 99%

Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

See 2 more Smart Citations

Current state and future prospects of artificial intelligence in ophthalmology: a review

Hogarty

Mackey

Hewitt

2018

Clinical Exper Ophthalmology

144

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“…Most recently, a deep-learning algorithm classified age-related macular degeneration and diabetic retinopathy from optical coherence tomography images of the retina 1 . Both of these eye conditions and others (such as glaucoma and macular oedema), have also been automatically assessed by deep learning trained on fundus images [2][3][4] (a retinal fundus image is a photograph of the internal surface at the back of the eye). In all these tests, the algorithms, which had been trained on hundreds of thousands of medically labelled images, performed on par with teams of human ophthalmologists and better than many individual experts when validated with thousands or hundreds of thousands of images.…”

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confidence: 99%

All eyes are on AI

2018

Nat Biomed Eng

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Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening

Wong

Bressler

2016

JAMA

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237

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Diabetes is a global public health disease projected to affect 642 million adults by 2040, with about 75% residing in lowand middle-income countries. Diabetic retinopathy (DR) affects 1 in 3 people with diabetes and remains the leading cause of blindness in working-aged adults. There are 3 broad strategic imperatives to prevent blindness caused by DR. Primary prevention requires preventing or delaying the onset of DR in those with diabetes by systems-level lifestyle modifications such as increasing physical activity or dietary modifications, pharmacological interventions for glycaemic and blood pressure control, and systematic screening for the onset of DR. Secondary prevention requires preventing the progression of DR in patients with DR by continuing systemic risk factor control, regular screening to monitor for the progression of mild DR to vision-threatening stages, and the development and implementation of evidence-based guidelines for managing DR. In this aspect, telemedicine-based DR screening incorporating artificial intelligence technology has the potential to facilitate more widespread and cost-effective screening, particularly in low-and middle-income countries. Tertiary prevention of DR blindness has been the main focus of the clinical ophthalmology community, classically based on laser photocoagulation treatment and ocular surgery but with an increasing use of anti-vascular endothelial growth factor (anti-VEGF) for vision-threatening DR. Evidence from serial epidemiological studies shows blindness due to DR has declined in high-income countries (e.g., the USA and UK) due to coordinated public health education efforts, increased awareness, early detection by DR screening, sustained systemic risk factor control, and the availability of effective tertiary level treatment. However, the progress made in reducing DR blindness in high-income countries may be overwhelmed by the increasing numbers of patients with diabetes and DR in low-and middle-income countries (e.g., China, India, Indonesia, etc.). Thus, to tackle DR at a global level, a paradigm shift in strategic focus from tertiary towards secondary and primary prevention measures with a multi-pronged whole-of-society approach at regional and national levels is urgently needed.

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Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

Cited by 1,711 publications

References 37 publications

Current state and future prospects of artificial intelligence in ophthalmology: a review

Current state and future prospects of artificial intelligence in ophthalmology: a review

All eyes are on AI

Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening

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