Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage

Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage

<i>Background and Objectives</i>: The present study evaluated the detection of diabetic retinopathy (DR) using an automated fundus camera focusing exclusively on retinal hemorrhage (RH) using a deep convolutional neural network, which is a machine-learning technology. <i>Materials...

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Bibliographic Details
Main Authors: Yoshihiro Tokuda, Hitoshi Tabuchi, Toshihiko Nagasawa, Mao Tanabe, Hodaka Deguchi, Yuki Yoshizumi, Zaigen Ohara, Hiroshi Takahashi
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Medicina
Subjects:
fundus ophthalmoscopy
diabetic retinopathy
retinal hemorrhage
deep learning
deep convolutional neural network
Online Access:https://www.mdpi.com/1648-9144/58/11/1681
Description
Summary:<i>Background and Objectives</i>: The present study evaluated the detection of diabetic retinopathy (DR) using an automated fundus camera focusing exclusively on retinal hemorrhage (RH) using a deep convolutional neural network, which is a machine-learning technology. <i>Materials and Methods</i>: This investigation was conducted via a prospective and observational study. The study included 89 fundus ophthalmoscopy images. Seventy images passed an image quality review and were graded as showing no apparent DR (<i>n</i> = 51), mild nonproliferative DR (NPDR; <i>n</i> = 16), moderate NPDR (<i>n</i> = 1), severe NPDR (<i>n</i> = 1), and proliferative DR (<i>n</i> = 1) by three retinal experts according to the International Clinical Diabetic Retinopathy Severity scale. The RH numbers and areas were automatically detected and the results of two tests—the detection of mild-or-worse NPDR and the detection of moderate-or-worse NPDR—were examined. <i>Results</i>: The detection of mild-or-worse DR showed a sensitivity of 0.812 (95% confidence interval: 0.680–0.945), specificity of 0.888, and area under the curve (AUC) of 0.884, whereas the detection of moderate-or-worse DR showed a sensitivity of 1.0, specificity of 1.0, and AUC of 1.0. <i>Conclusions</i>: Automated diagnosis using artificial intelligence focusing exclusively on RH could be used to diagnose DR requiring ophthalmologist intervention.
ISSN:1010-660X
1648-9144

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