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

Abstract

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).

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

“…Finally, some other layers convert these segments into the classification/recognition of images. All these layers learn features from the input data using learning procedures and without expert intervention [135][136][137][138].…”

“…Each neuron outcome is then mixed to maintain overlapping among input areas to better represent the original image information. This procedure is pursued for all layers until desirable results are achieved [135][136][137][138][139][140][141][142]. [145] CNN model Detection of exudates -- [113] Multiscale and CNN Detection of fovea and OD -AC: 97%…”

<p>Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma</p>

“…18 However, using such algorithms in real-world situations remains a challenge, as shown by the decreased accuracy (around 80%) achieved in prospective studies. 19 This decrease could be due to inconsistent image quality and other aspects, such as comorbidity. Dataset quality assessment is normally taken for granted, with researchers using trainings and certifications for photographers 17,18 or manually "cleaning" the datasets; 19 some authors have even used quality filters on algorithms before training the algorithms.…”

“…19 This decrease could be due to inconsistent image quality and other aspects, such as comorbidity. Dataset quality assessment is normally taken for granted, with researchers using trainings and certifications for photographers 17,18 or manually "cleaning" the datasets; 19 some authors have even used quality filters on algorithms before training the algorithms. 20 Optretina is a telemedicine platform which performs general screening for retinal diseases using nonmydriatic cameras and human evaluation by a retinal specialist ophthalmologist.…”

From the eye into the foot?

“…Retinal imaging, combined with the power of artificial intelligence (AI) -based algorithms, might soon be able to make personalized risk stratification feasible on a population basis. Deep learning-based retinal fundus cameras are already being exploited in the field of diabetes and screening for DR. Four recent studies have validated these AI-based devices for DR screening in real-life populations with multiple ethnicities, demonstrating high accuracy, sensitivity and specificity compared with various reference standards [18][19][20][21].…”