Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography.

Toshihiko Nagasawa,Hitoshi Tabuchi,Shoji Morita,Yuki Yoshizumi,Zaigen Ohara,Masanori Niki,Yoshinori Mitamura,Hiroki Masumoto,Heba A El Gendy

doi:10.1155/2021/6651175

Abstract

Purpose The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images. Method The study included 491 ultra-wide-field fundus ophthalmoscopy and optical coherence tomography angiography (OCTA) images that passed an image-quality review and were graded as no apparent DR (NDR; 169 images), mild nonproliferative DR (NPDR; 76 images), moderate NPDR (54 images), severe NPDR (90 images), and proliferative DR (PDR; 102 images) by three retinal experts by the International Clinical Diabetic Retinopathy Severity Scale. The findings of tests 1 and 2 to identify no apparent diabetic retinopathy (NDR) and PDR, respectively, were then assessed. For each verification, Optos, OCTA, and Optos OCTA imaging scans with DCNN were performed. Result The Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and DR showed mean areas under the curve (AUC) of 0.79, 0.883, and 0.847; sensitivity rates of 80.9%, 83.9%, and 78.6%; and specificity rates of 55%, 71.6%, and 69.8%, respectively. Meanwhile, the Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and PDR showed mean AUC of 0.981, 0.928, and 0.964; sensitivity rates of 90.2%, 74.5%, and 80.4%; and specificity rates of 97%, 97%, and 96.4%, respectively. Conclusion The combination of Optos and OCTA imaging with DCNN could detect DR at desirable levels of accuracy and may be useful in clinical practice and retinal screening. Although the combination of multiple imaging techniques might overcome their individual weaknesses and provide comprehensive imaging, artificial intelligence in classifying multimodal images has not always produced accurate results.

Highlights

Diabetic retinopathy (DR) has been one of the major causes of visual impairment and blindness
While diabetic eye care has been mainly reliant on the Journal of Ophthalmology number of ophthalmologists and necessary healthcare infrastructure [5], performing fundus examination, which is performed by ophthalmologists, for all patients with diabetes is unrealistic and expensive
Expenses associated with DR have been substantial, whereas the financial impact may be even more severe given that several patients with this complication live in developing countries [6, 7], many of which have an inadequate number of ophthalmologists [8]

Summary

Introduction

Diabetic retinopathy (DR) has been one of the major causes of visual impairment and blindness. Expenses associated with DR have been substantial, whereas the financial impact may be even more severe given that several patients with this complication live in developing countries [6, 7], many of which have an inadequate number of ophthalmologists [8]. Several recent studies have utilized state-of-the-art deep-learning (DL) algorithms for the automated detection of DR from a large number of fundus images [9,10,11,12]. Is AI system has allowed for specialty-level diagnostics to be applied in primary care settings [10, 13, 14], with studies expecting image diagnosis using AI to be a solution to the shortage of physicians and high medical expenses for specialists [15]. Several studies that examined the efficacy of automated detection have used standard fundus cameras that provide 30° or 50° images. The objective of the present study was to investigate the accuracy of AI using different composite images

Methods

Results

Conclusions

Conflicts of Interest