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Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage
Background and Objectives: 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. Materials and Methods: This investigat...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9692355/ https://www.ncbi.nlm.nih.gov/pubmed/36422220 http://dx.doi.org/10.3390/medicina58111681 |
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author | Tokuda, Yoshihiro Tabuchi, Hitoshi Nagasawa, Toshihiko Tanabe, Mao Deguchi, Hodaka Yoshizumi, Yuki Ohara, Zaigen Takahashi, Hiroshi |
author_facet | Tokuda, Yoshihiro Tabuchi, Hitoshi Nagasawa, Toshihiko Tanabe, Mao Deguchi, Hodaka Yoshizumi, Yuki Ohara, Zaigen Takahashi, Hiroshi |
author_sort | Tokuda, Yoshihiro |
collection | PubMed |
description | Background and Objectives: 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. Materials and Methods: 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 (n = 51), mild nonproliferative DR (NPDR; n = 16), moderate NPDR (n = 1), severe NPDR (n = 1), and proliferative DR (n = 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. Results: 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. Conclusions: Automated diagnosis using artificial intelligence focusing exclusively on RH could be used to diagnose DR requiring ophthalmologist intervention. |
format | Online Article Text |
id | pubmed-9692355 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96923552022-11-26 Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage Tokuda, Yoshihiro Tabuchi, Hitoshi Nagasawa, Toshihiko Tanabe, Mao Deguchi, Hodaka Yoshizumi, Yuki Ohara, Zaigen Takahashi, Hiroshi Medicina (Kaunas) Article Background and Objectives: 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. Materials and Methods: 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 (n = 51), mild nonproliferative DR (NPDR; n = 16), moderate NPDR (n = 1), severe NPDR (n = 1), and proliferative DR (n = 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. Results: 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. Conclusions: Automated diagnosis using artificial intelligence focusing exclusively on RH could be used to diagnose DR requiring ophthalmologist intervention. MDPI 2022-11-20 /pmc/articles/PMC9692355/ /pubmed/36422220 http://dx.doi.org/10.3390/medicina58111681 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tokuda, Yoshihiro Tabuchi, Hitoshi Nagasawa, Toshihiko Tanabe, Mao Deguchi, Hodaka Yoshizumi, Yuki Ohara, Zaigen Takahashi, Hiroshi Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage |
title | Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage |
title_full | Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage |
title_fullStr | Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage |
title_full_unstemmed | Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage |
title_short | Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage |
title_sort | automatic diagnosis of diabetic retinopathy stage focusing exclusively on retinal hemorrhage |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9692355/ https://www.ncbi.nlm.nih.gov/pubmed/36422220 http://dx.doi.org/10.3390/medicina58111681 |
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