Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images
Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive...
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| Veröffentlicht in: | Communications biology 2020-01, Vol.3 (1), p.15-15, Article 15 |
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| creator | Li, Zhongwen Guo, Chong Nie, Danyao Lin, Duoru Zhu, Yi Chen, Chuan Wu, Xiaohang Xu, Fabao Jin, Chenjin Zhang, Xiayin Xiao, Hui Zhang, Kai Zhao, Lanqin Yan, Pisong Lai, Weiyi Li, Jianyin Feng, Weibo Li, Yonghao Wei Ting, Daniel Shu Lin, Haotian |
| description | Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral.
Li et al. develop a cascaded deep learning system for automated retinal detachment and macular status detection based on ultra-widefield fundus (UWF) images. With reliable and comparable performance to an experienced opthamologist, this system can also provide guidance to patients regarding appropriate preoperative posturing to reduce RD progression. |
| doi_str_mv | 10.1038/s42003-019-0730-x |
| format | Article |
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Li et al. develop a cascaded deep learning system for automated retinal detachment and macular status detection based on ultra-widefield fundus (UWF) images. With reliable and comparable performance to an experienced opthamologist, this system can also provide guidance to patients regarding appropriate preoperative posturing to reduce RD progression.</description><identifier>ISSN: 2399-3642</identifier><identifier>EISSN: 2399-3642</identifier><identifier>DOI: 10.1038/s42003-019-0730-x</identifier><identifier>PMID: 31925315</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>692/308 ; 692/308/575 ; 692/699/3161/3175 ; Adolescent ; Adult ; Aged ; Aged, 80 and over ; Automation ; Biology ; Biomedical and Life Sciences ; Child ; Deep Learning ; Diagnostic Imaging - methods ; Female ; Humans ; Life Sciences ; Macula Lutea - diagnostic imaging ; Macula Lutea - pathology ; Male ; Middle Aged ; Reproducibility of Results ; Retina ; Retinal Detachment - diagnostic imaging ; Retinal Detachment - pathology ; ROC Curve ; Sensitivity and Specificity ; Therapeutic applications ; Workflow ; Young Adult</subject><ispartof>Communications biology, 2020-01, Vol.3 (1), p.15-15, Article 15</ispartof><rights>The Author(s) 2020</rights><rights>This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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-c470t-616085bcc3eee86b8babeadfce102e7e754b6fe4bd7ac2e46c1cae4b25f0f01b3</citedby><cites>FETCH-LOGICAL-c470t-616085bcc3eee86b8babeadfce102e7e754b6fe4bd7ac2e46c1cae4b25f0f01b3</cites><orcidid>0000-0003-4672-9721 ; 0000-0001-5701-0857 ; 0000-0001-9054-288X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6949241/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6949241/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,41096,42165,51551,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31925315$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Zhongwen</creatorcontrib><creatorcontrib>Guo, Chong</creatorcontrib><creatorcontrib>Nie, Danyao</creatorcontrib><creatorcontrib>Lin, Duoru</creatorcontrib><creatorcontrib>Zhu, Yi</creatorcontrib><creatorcontrib>Chen, Chuan</creatorcontrib><creatorcontrib>Wu, Xiaohang</creatorcontrib><creatorcontrib>Xu, Fabao</creatorcontrib><creatorcontrib>Jin, Chenjin</creatorcontrib><creatorcontrib>Zhang, Xiayin</creatorcontrib><creatorcontrib>Xiao, Hui</creatorcontrib><creatorcontrib>Zhang, Kai</creatorcontrib><creatorcontrib>Zhao, Lanqin</creatorcontrib><creatorcontrib>Yan, Pisong</creatorcontrib><creatorcontrib>Lai, Weiyi</creatorcontrib><creatorcontrib>Li, Jianyin</creatorcontrib><creatorcontrib>Feng, Weibo</creatorcontrib><creatorcontrib>Li, Yonghao</creatorcontrib><creatorcontrib>Wei Ting, Daniel Shu</creatorcontrib><creatorcontrib>Lin, Haotian</creatorcontrib><title>Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images</title><title>Communications biology</title><addtitle>Commun Biol</addtitle><addtitle>Commun Biol</addtitle><description>Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral.
Li et al. develop a cascaded deep learning system for automated retinal detachment and macular status detection based on ultra-widefield fundus (UWF) images. With reliable and comparable performance to an experienced opthamologist, this system can also provide guidance to patients regarding appropriate preoperative posturing to reduce RD progression.</description><subject>692/308</subject><subject>692/308/575</subject><subject>692/699/3161/3175</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Automation</subject><subject>Biology</subject><subject>Biomedical and Life Sciences</subject><subject>Child</subject><subject>Deep Learning</subject><subject>Diagnostic Imaging - methods</subject><subject>Female</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Macula Lutea - diagnostic imaging</subject><subject>Macula Lutea - pathology</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Reproducibility of Results</subject><subject>Retina</subject><subject>Retinal Detachment - diagnostic imaging</subject><subject>Retinal Detachment - 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methods</topic><topic>Female</topic><topic>Humans</topic><topic>Life Sciences</topic><topic>Macula Lutea - diagnostic imaging</topic><topic>Macula Lutea - pathology</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Reproducibility of Results</topic><topic>Retina</topic><topic>Retinal Detachment - diagnostic imaging</topic><topic>Retinal Detachment - pathology</topic><topic>ROC Curve</topic><topic>Sensitivity and Specificity</topic><topic>Therapeutic applications</topic><topic>Workflow</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Zhongwen</creatorcontrib><creatorcontrib>Guo, Chong</creatorcontrib><creatorcontrib>Nie, Danyao</creatorcontrib><creatorcontrib>Lin, Duoru</creatorcontrib><creatorcontrib>Zhu, Yi</creatorcontrib><creatorcontrib>Chen, Chuan</creatorcontrib><creatorcontrib>Wu, Xiaohang</creatorcontrib><creatorcontrib>Xu, Fabao</creatorcontrib><creatorcontrib>Jin, Chenjin</creatorcontrib><creatorcontrib>Zhang, Xiayin</creatorcontrib><creatorcontrib>Xiao, Hui</creatorcontrib><creatorcontrib>Zhang, Kai</creatorcontrib><creatorcontrib>Zhao, Lanqin</creatorcontrib><creatorcontrib>Yan, Pisong</creatorcontrib><creatorcontrib>Lai, Weiyi</creatorcontrib><creatorcontrib>Li, Jianyin</creatorcontrib><creatorcontrib>Feng, Weibo</creatorcontrib><creatorcontrib>Li, Yonghao</creatorcontrib><creatorcontrib>Wei Ting, Daniel Shu</creatorcontrib><creatorcontrib>Lin, Haotian</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Biological Sciences</collection><collection>ProQuest Science Journals</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Communications biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Zhongwen</au><au>Guo, Chong</au><au>Nie, Danyao</au><au>Lin, Duoru</au><au>Zhu, Yi</au><au>Chen, Chuan</au><au>Wu, Xiaohang</au><au>Xu, Fabao</au><au>Jin, Chenjin</au><au>Zhang, Xiayin</au><au>Xiao, Hui</au><au>Zhang, Kai</au><au>Zhao, Lanqin</au><au>Yan, Pisong</au><au>Lai, Weiyi</au><au>Li, Jianyin</au><au>Feng, Weibo</au><au>Li, Yonghao</au><au>Wei Ting, Daniel Shu</au><au>Lin, Haotian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images</atitle><jtitle>Communications biology</jtitle><stitle>Commun Biol</stitle><addtitle>Commun Biol</addtitle><date>2020-01-08</date><risdate>2020</risdate><volume>3</volume><issue>1</issue><spage>15</spage><epage>15</epage><pages>15-15</pages><artnum>15</artnum><issn>2399-3642</issn><eissn>2399-3642</eissn><abstract>Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral.
Li et al. develop a cascaded deep learning system for automated retinal detachment and macular status detection based on ultra-widefield fundus (UWF) images. With reliable and comparable performance to an experienced opthamologist, this system can also provide guidance to patients regarding appropriate preoperative posturing to reduce RD progression.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>31925315</pmid><doi>10.1038/s42003-019-0730-x</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-4672-9721</orcidid><orcidid>https://orcid.org/0000-0001-5701-0857</orcidid><orcidid>https://orcid.org/0000-0001-9054-288X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 692/308 692/308/575 692/699/3161/3175 Adolescent Adult Aged Aged, 80 and over Automation Biology Biomedical and Life Sciences Child Deep Learning Diagnostic Imaging - methods Female Humans Life Sciences Macula Lutea - diagnostic imaging Macula Lutea - pathology Male Middle Aged Reproducibility of Results Retina Retinal Detachment - diagnostic imaging Retinal Detachment - pathology ROC Curve Sensitivity and Specificity Therapeutic applications Workflow Young Adult |
| title | Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images |
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