{"title":"创新利用超宽视野眼底图像和深度学习算法筛查高风险后极性白内障。","authors":"Elsa L C Mai, Bing-Hong Chen, Tai-Yuan Su","doi":"10.1097/j.jcrs.0000000000001419","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To test a cataract shadow projection theory and validate it by developing a deep learning algorithm that enables automatic and stable posterior polar cataract (PPC) screening using fundus images.</p><p><strong>Setting: </strong>Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei, Taiwan.</p><p><strong>Design: </strong>Retrospective chart review.</p><p><strong>Methods: </strong>A deep learning algorithm to automatically detect PPC was developed based on the cataract shadow projection theory. Retrospective data (n = 546) with ultra-wide field fundus images were collected, and various model architectures and fields of view were tested for optimization.</p><p><strong>Results: </strong>The final model achieved 80% overall accuracy, with 88.2% sensitivity and 93.4% specificity in PPC screening on a clinical validation dataset (n = 103).</p><p><strong>Conclusions: </strong>This study established a significant relationship between PPC and the projected shadow, which may help surgeons to identify potential PPC risks preoperatively and reduce the incidence of posterior capsular rupture during cataract surgery.</p>","PeriodicalId":15214,"journal":{"name":"Journal of cataract and refractive surgery","volume":" ","pages":"618-623"},"PeriodicalIF":2.6000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11146186/pdf/","citationCount":"0","resultStr":"{\"title\":\"Innovative utilization of ultra-wide field fundus images and deep learning algorithms for screening high-risk posterior polar cataract.\",\"authors\":\"Elsa L C Mai, Bing-Hong Chen, Tai-Yuan Su\",\"doi\":\"10.1097/j.jcrs.0000000000001419\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To test a cataract shadow projection theory and validate it by developing a deep learning algorithm that enables automatic and stable posterior polar cataract (PPC) screening using fundus images.</p><p><strong>Setting: </strong>Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei, Taiwan.</p><p><strong>Design: </strong>Retrospective chart review.</p><p><strong>Methods: </strong>A deep learning algorithm to automatically detect PPC was developed based on the cataract shadow projection theory. Retrospective data (n = 546) with ultra-wide field fundus images were collected, and various model architectures and fields of view were tested for optimization.</p><p><strong>Results: </strong>The final model achieved 80% overall accuracy, with 88.2% sensitivity and 93.4% specificity in PPC screening on a clinical validation dataset (n = 103).</p><p><strong>Conclusions: </strong>This study established a significant relationship between PPC and the projected shadow, which may help surgeons to identify potential PPC risks preoperatively and reduce the incidence of posterior capsular rupture during cataract surgery.</p>\",\"PeriodicalId\":15214,\"journal\":{\"name\":\"Journal of cataract and refractive surgery\",\"volume\":\" \",\"pages\":\"618-623\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11146186/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of cataract and refractive surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/j.jcrs.0000000000001419\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of cataract and refractive surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/j.jcrs.0000000000001419","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Innovative utilization of ultra-wide field fundus images and deep learning algorithms for screening high-risk posterior polar cataract.
Purpose: To test a cataract shadow projection theory and validate it by developing a deep learning algorithm that enables automatic and stable posterior polar cataract (PPC) screening using fundus images.
Setting: Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei, Taiwan.
Design: Retrospective chart review.
Methods: A deep learning algorithm to automatically detect PPC was developed based on the cataract shadow projection theory. Retrospective data (n = 546) with ultra-wide field fundus images were collected, and various model architectures and fields of view were tested for optimization.
Results: The final model achieved 80% overall accuracy, with 88.2% sensitivity and 93.4% specificity in PPC screening on a clinical validation dataset (n = 103).
Conclusions: This study established a significant relationship between PPC and the projected shadow, which may help surgeons to identify potential PPC risks preoperatively and reduce the incidence of posterior capsular rupture during cataract surgery.
期刊介绍：
The Journal of Cataract & Refractive Surgery (JCRS), a preeminent peer-reviewed monthly ophthalmology publication, is the official journal of the American Society of Cataract and Refractive Surgery (ASCRS) and the European Society of Cataract and Refractive Surgeons (ESCRS).
JCRS publishes high quality articles on all aspects of anterior segment surgery. In addition to original clinical studies, the journal features a consultation section, practical techniques, important cases, and reviews as well as basic science articles.
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