Validation of automated screening for referable diabetic retinopathy with the IDx‐DR device in the Hoorn Diabetes Care System

Heijden, Amber A. van der; Abràmoff, Michael D.; Verbraak, Frank D.; Hecke, Manon V. van; Liem, Albert T.A.; Nijpels, Giel

doi:10.1111/aos.13613

Cited by 224 publications

(141 citation statements)

References 29 publications

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“…Multiple diagnostic algorithms for the detection of DR are now commercially available for which the performance has been independently evaluated (9)(10)(11)(12). One of these, the IDx-DR-EU-2.1 device, has been enhanced with deep learning.…”

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confidence: 99%

Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting

et al. 2019

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OBJECTIVETo determine the diagnostic accuracy in a real-world primary care setting of a deep learning-enhanced device for automated detection of diabetic retinopathy (DR). RESEARCH DESIGN AND METHODSRetinal images of people with type 2 diabetes visiting a primary care screening program were graded by a hybrid deep learning-enhanced device (IDx-DR-EU-2.1; IDx, Amsterdam, the Netherlands), and its classification of retinopathy (vision-threatening [vt]DR, more than mild [mtm]DR, and mild or more [mom]DR) was compared with a reference standard. This reference standard consisted of grading according to the International Clinical Classification of DR by the Rotterdam Study reading center. We determined the diagnostic accuracy of the hybrid deep learningenhanced device (IDx-DR-EU-2.1) against the reference standard. RESULTSA total of 1,616 people with type 2 diabetes were imaged. The hybrid deep learning-enhanced device's sensitivity/specificity against the reference standard was, respectively, for vtDR 100% (95% CI 77.1-100)/97.8% (95% CI 96.8-98.5) and for mtmDR 79.4% (95% CI 66.5-87.9)/93.8% (95% CI 92.1-94.9). CONCLUSIONSThe hybrid deep learning-enhanced device had high diagnostic accuracy for the detection of both vtDR (although the number of vtDR cases was low) and mtmDR in a primary care setting against an independent reading center. This allows its' safe use in a primary care setting.With the growing prevalence of diabetes, the prevalence of diabetic retinopathy (DR) is rising as well. Screening for DR has proven to be effective in the prevention of visual loss and blindness from DR (1). National health authorities (2) and most professional organizations (3) recommend regular DR screening programs, which are usually integrated within regular diabetes care (4). Automated medical diagnosis has achieved parity with or even superiority to clinical experts' diagnosis for an increasing number of clinical tasks, including detection of DR (5-7), and can help to improve health care efficiency, affordability, and accessibility of DR screening. Moreover, automated diagnosis reduced the diagnostic variability that was common in expert review of medical images (8).

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confidence: 99%

Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting

et al. 2019

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“…Group 1 can be retested in 12 months while group 2 is referred to an eye care specialist. A recent study demonstrated IDx-DR to have similar sensitivity in detecting diabetic retinopathy to human experts [47].…”

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confidence: 97%

Innovative Diagnostic Tools for Ophthalmology in Low-Income Countries

Singh¹,

Kabbara²,

Conway³

et al. 2019

Novel Diagnostic Methods in Ophthalmology

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Globally, there are almost 300 million people blind and visually impaired and over 90% live developing countries. The gross disparity in access to ophthalmologists limits the ability to accurately diagnose potentially blinding conditions like cataract, glaucoma, trachoma, uncorrected refractive error and limits timely initiation of medical and surgical treatment. Since 85% of blindness is preventable, bridging this chasm for care is even more critical in preventing needless blindness. Many low-income countries must rely on community health workers, physician assistants, and cataract surgeons for primary eye care. Ophthalmology in lowincome countries (LIC) is further challenging due to complexities brought from tropical climates, frail electric grids, poor road and water infrastructure, limited diagnostic capability and limited treatment options. Vision 2020 set the goal of eliminating preventable blindness by 2020 despite formidable obstacles. Innovative technologies are emerging to test visual acuity, correct refractive error quickly and inexpensively, capture retinal images with portable tools, train cataract surgeons using simulators, capitalize on mHealth, access ophthalmic information remotely. These advancements are allowing nonspecialized ophthalmic practitioners to provide low-cost, high impact eye care in resource-limited regions around the world.

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“…Similar virtual clinics have been successfully established for low-risk glaucoma screening, and had relatively low clinically significant false negative rate of 4% when reviewed by an ophthalmologist "face to face" audit [14]. The application of AI to home-based monitoring is being validated in screening for DR, and is gaining Food and Drug Administration (FDA) approval [15]. Furthermore, promising technologies such as automated whole-eye binocular OCT may soon enable ophthalmologists to remotely acquire increasingly detailed imaging along with pupillometry, perimetry, motility, and visual acuity [16].…”

Section: Developments and Future Potentialmentioning

confidence: 99%

A renaissance of teleophthalmology through artificial intelligence

Korot

Wood

Weiner

et al. 2019

Eye

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Background While teleophthalmology is not a novel technique or method of care, its application is set to undergo a transformation due to the implementation of artificial intelligence (AI). The field of AI has recently experienced significant advancements in image recognition due to a technique called deep learning, and is increasingly being investigated in ophthalmic image segmentation, analysis, and clinical decision making [1-5].

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Validation of automated screening for referable diabetic retinopathy with the IDx‐DR device in the Hoorn Diabetes Care System

Cited by 224 publications

References 29 publications

Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting

Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting

Innovative Diagnostic Tools for Ophthalmology in Low-Income Countries

A renaissance of teleophthalmology through artificial intelligence

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