Deep Learning Models for Automated Diagnosis of Retinopathy of Prematurity in Preterm Infants

Retinopathy of prematurity (ROP) is a disease that can cause blindness in premature infants. It is characterized by immature vascular growth of the retinal blood vessels. However, early detection and treatment of ROP can significantly improve the visual acuity of high-risk patients. Thus, early diag...

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Veröffentlicht in:Electronics (Basel) 2020-09, Vol.9 (9), p.1444
Hauptverfasser: Huang, Yo-Ping, Vadloori, Spandana, Chu, Hung-Chi, Kang, Eugene Yu-Chuan, Wu, Wei-Chi, Kusaka, Shunji, Fukushima, Yoko
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container_end_page
container_issue 9
container_start_page 1444
container_title Electronics (Basel)
container_volume 9
creator Huang, Yo-Ping
Vadloori, Spandana
Chu, Hung-Chi
Kang, Eugene Yu-Chuan
Wu, Wei-Chi
Kusaka, Shunji
Fukushima, Yoko
description Retinopathy of prematurity (ROP) is a disease that can cause blindness in premature infants. It is characterized by immature vascular growth of the retinal blood vessels. However, early detection and treatment of ROP can significantly improve the visual acuity of high-risk patients. Thus, early diagnosis of ROP is crucial in preventing visual impairment. However, several patients refrain from treatment owing to the lack of medical expertise in diagnosing the disease; this is especially problematic considering that the number of ROP cases is on the rise. To this end, we applied transfer learning to five deep neural network architectures for identifying ROP in preterm infants. Our results showed that the VGG19 model outperformed the other models in determining whether a preterm infant has ROP, with 96% accuracy, 96.6% sensitivity, and 95.2% specificity. We also classified the severity of the disease; the VGG19 model showed 98.82% accuracy in predicting the severity of the disease with a sensitivity and specificity of 100% and 98.41%, respectively. We performed 5-fold cross-validation on the datasets to validate the reliability of the VGG19 model and found that the VGG19 model exhibited high accuracy in predicting ROP. These findings could help promote the development of computer-aided diagnosis.
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It is characterized by immature vascular growth of the retinal blood vessels. However, early detection and treatment of ROP can significantly improve the visual acuity of high-risk patients. Thus, early diagnosis of ROP is crucial in preventing visual impairment. However, several patients refrain from treatment owing to the lack of medical expertise in diagnosing the disease; this is especially problematic considering that the number of ROP cases is on the rise. To this end, we applied transfer learning to five deep neural network architectures for identifying ROP in preterm infants. Our results showed that the VGG19 model outperformed the other models in determining whether a preterm infant has ROP, with 96% accuracy, 96.6% sensitivity, and 95.2% specificity. We also classified the severity of the disease; the VGG19 model showed 98.82% accuracy in predicting the severity of the disease with a sensitivity and specificity of 100% and 98.41%, respectively. We performed 5-fold cross-validation on the datasets to validate the reliability of the VGG19 model and found that the VGG19 model exhibited high accuracy in predicting ROP. 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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 (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c413t-29557dc4f37e2f7fe8ad97943813e45ab4887e8ee62952f1155b6b13325aab583</citedby><cites>FETCH-LOGICAL-c413t-29557dc4f37e2f7fe8ad97943813e45ab4887e8ee62952f1155b6b13325aab583</cites><orcidid>0000-0003-0429-2007 ; 0000-0001-6814-6530 ; 0000-0002-1732-981X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Huang, Yo-Ping</creatorcontrib><creatorcontrib>Vadloori, Spandana</creatorcontrib><creatorcontrib>Chu, Hung-Chi</creatorcontrib><creatorcontrib>Kang, Eugene Yu-Chuan</creatorcontrib><creatorcontrib>Wu, Wei-Chi</creatorcontrib><creatorcontrib>Kusaka, Shunji</creatorcontrib><creatorcontrib>Fukushima, Yoko</creatorcontrib><title>Deep Learning Models for Automated Diagnosis of Retinopathy of Prematurity in Preterm Infants</title><title>Electronics (Basel)</title><description>Retinopathy of prematurity (ROP) is a disease that can cause blindness in premature infants. 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subjects Applied research
Artificial intelligence
Artificial neural networks
Automation
Blindness
Blood vessels
Brain cancer
Classification
Computer aided medical diagnosis
Computer architecture
Datasets
Deep learning
Diabetic retinopathy
Diagnosis
Diagnostic imaging
Eye diseases
Glaucoma
Health services
Infants
Machine learning
Medical personnel
Methods
Model accuracy
Neural networks
Newborn babies
Premature birth
Retinal detachment
Retinopathy of prematurity
Sensitivity
Telemedicine
Visual acuity
Visual impairment
title Deep Learning Models for Automated Diagnosis of Retinopathy of Prematurity in Preterm Infants
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