Mia Karabeg, Goran Petrovski, Silvia Nw Hertzberg, Maja Gran Erke, Dag Sigurd Fosmark, Greg Russell, Morten C Moe, Vallo Volke, Vidas Raudonis, Rasa Verkauskiene, Jelizaveta Sokolovska, Inga-Britt Kjellevold Haugen, Beata Eva Petrovski
{"title":"一项试点成本分析研究，比较了基于人工智能的 EyeArt® 和眼科医生对挪威奥斯陆少数民族妇女糖尿病视网膜病变的评估。","authors":"Mia Karabeg, Goran Petrovski, Silvia Nw Hertzberg, Maja Gran Erke, Dag Sigurd Fosmark, Greg Russell, Morten C Moe, Vallo Volke, Vidas Raudonis, Rasa Verkauskiene, Jelizaveta Sokolovska, Inga-Britt Kjellevold Haugen, Beata Eva Petrovski","doi":"10.1186/s40942-024-00547-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Diabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed.</p><p><strong>Purpose: </strong>To perform a pilot cost-analysis study for detecting DR in a cohort of minority women with DM in Oslo, Norway, that have the highest prevalence of diabetes mellitus (DM) in the country, using both manual (ophthalmologist) and autonomous (AI) grading. This is the first study in Norway, as far as we know, that uses AI in DR- grading of retinal images.</p><p><strong>Methods: </strong>On Minority Women's Day, November 1, 2017, in Oslo, Norway, 33 patients (66 eyes) over 18 years of age diagnosed with DM (T1D and T2D) were screened. The Eidon - True Color Confocal Scanner (CenterVue, United States) was used for retinal imaging and graded for DR after screening had been completed, by an ophthalmologist and automatically, using EyeArt Automated DR Detection System, version 2.1.0 (EyeArt, EyeNuk, CA, USA). The gradings were based on the International Clinical Diabetic Retinopathy (ICDR) severity scale [1] detecting the presence or absence of referable DR. Cost-minimization analyses were performed for both grading methods.</p><p><strong>Results: </strong>33 women (64 eyes) were eligible for the analysis. A very good inter-rater agreement was found: 0.98 (P < 0.01), between the human and AI-based EyeArt grading system for detecting DR. The prevalence of DR was 18.6% (95% CI: 11.4-25.8%), and the sensitivity and specificity were 100% (95% CI: 100-100% and 95% CI: 100-100%), respectively. The cost difference for AI screening compared to human screening was $143 lower per patient (cost-saving) in favour of AI.</p><p><strong>Conclusion: </strong>Our results indicate that The EyeArt AI system is both a reliable, cost-saving, and useful tool for DR grading in clinical practice.</p>","PeriodicalId":14289,"journal":{"name":"International Journal of Retina and Vitreous","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11112837/pdf/","citationCount":"0","resultStr":"{\"title\":\"A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway.\",\"authors\":\"Mia Karabeg, Goran Petrovski, Silvia Nw Hertzberg, Maja Gran Erke, Dag Sigurd Fosmark, Greg Russell, Morten C Moe, Vallo Volke, Vidas Raudonis, Rasa Verkauskiene, Jelizaveta Sokolovska, Inga-Britt Kjellevold Haugen, Beata Eva Petrovski\",\"doi\":\"10.1186/s40942-024-00547-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Diabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed.</p><p><strong>Purpose: </strong>To perform a pilot cost-analysis study for detecting DR in a cohort of minority women with DM in Oslo, Norway, that have the highest prevalence of diabetes mellitus (DM) in the country, using both manual (ophthalmologist) and autonomous (AI) grading. This is the first study in Norway, as far as we know, that uses AI in DR- grading of retinal images.</p><p><strong>Methods: </strong>On Minority Women's Day, November 1, 2017, in Oslo, Norway, 33 patients (66 eyes) over 18 years of age diagnosed with DM (T1D and T2D) were screened. The Eidon - True Color Confocal Scanner (CenterVue, United States) was used for retinal imaging and graded for DR after screening had been completed, by an ophthalmologist and automatically, using EyeArt Automated DR Detection System, version 2.1.0 (EyeArt, EyeNuk, CA, USA). The gradings were based on the International Clinical Diabetic Retinopathy (ICDR) severity scale [1] detecting the presence or absence of referable DR. Cost-minimization analyses were performed for both grading methods.</p><p><strong>Results: </strong>33 women (64 eyes) were eligible for the analysis. A very good inter-rater agreement was found: 0.98 (P < 0.01), between the human and AI-based EyeArt grading system for detecting DR. The prevalence of DR was 18.6% (95% CI: 11.4-25.8%), and the sensitivity and specificity were 100% (95% CI: 100-100% and 95% CI: 100-100%), respectively. The cost difference for AI screening compared to human screening was $143 lower per patient (cost-saving) in favour of AI.</p><p><strong>Conclusion: </strong>Our results indicate that The EyeArt AI system is both a reliable, cost-saving, and useful tool for DR grading in clinical practice.</p>\",\"PeriodicalId\":14289,\"journal\":{\"name\":\"International Journal of Retina and Vitreous\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11112837/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Retina and Vitreous\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s40942-024-00547-3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Retina and Vitreous","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40942-024-00547-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景：糖尿病视网膜病变（DR）是导致全球劳动适龄人口成人失明的主要原因，而早期发现是可以预防的。建议定期进行眼科检查，这对发现危及视力的糖尿病视网膜病变至关重要。目的：在挪威奥斯陆开展一项成本分析试点研究，利用人工（眼科医生）和自主（人工智能）分级，对该国糖尿病（DM）发病率最高的少数民族女性糖尿病患者队列进行检测。据我们所知，这是挪威第一项在DR视网膜图像分级中使用人工智能的研究：2017年11月1日少数民族妇女节当天，挪威奥斯陆市对33名18岁以上确诊患有糖尿病（T1D和T2D）的患者（66只眼睛）进行了筛查。在筛查结束后，由眼科医生使用EyeArt自动DR检测系统2.1.0版（EyeArt, EyeNuk, CA, USA）自动进行DR分级。分级基于国际临床糖尿病视网膜病变（ICDR）严重程度量表[1]，检测是否存在可转诊的 DR。两种分级方法都进行了成本最小化分析：33名女性（64只眼睛）符合分析条件。结果：33 名女性（64 只眼睛）符合分析条件，评分者之间的一致性非常好：0.98（P我们的研究结果表明，EyeArt AI 系统是临床实践中用于 DR 分级的可靠、节约成本且有用的工具。
A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway.
Background: Diabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed.
Purpose: To perform a pilot cost-analysis study for detecting DR in a cohort of minority women with DM in Oslo, Norway, that have the highest prevalence of diabetes mellitus (DM) in the country, using both manual (ophthalmologist) and autonomous (AI) grading. This is the first study in Norway, as far as we know, that uses AI in DR- grading of retinal images.
Methods: On Minority Women's Day, November 1, 2017, in Oslo, Norway, 33 patients (66 eyes) over 18 years of age diagnosed with DM (T1D and T2D) were screened. The Eidon - True Color Confocal Scanner (CenterVue, United States) was used for retinal imaging and graded for DR after screening had been completed, by an ophthalmologist and automatically, using EyeArt Automated DR Detection System, version 2.1.0 (EyeArt, EyeNuk, CA, USA). The gradings were based on the International Clinical Diabetic Retinopathy (ICDR) severity scale [1] detecting the presence or absence of referable DR. Cost-minimization analyses were performed for both grading methods.
Results: 33 women (64 eyes) were eligible for the analysis. A very good inter-rater agreement was found: 0.98 (P < 0.01), between the human and AI-based EyeArt grading system for detecting DR. The prevalence of DR was 18.6% (95% CI: 11.4-25.8%), and the sensitivity and specificity were 100% (95% CI: 100-100% and 95% CI: 100-100%), respectively. The cost difference for AI screening compared to human screening was $143 lower per patient (cost-saving) in favour of AI.
Conclusion: Our results indicate that The EyeArt AI system is both a reliable, cost-saving, and useful tool for DR grading in clinical practice.
期刊介绍：
International Journal of Retina and Vitreous focuses on the ophthalmic subspecialty of vitreoretinal disorders. The journal presents original articles on new approaches to diagnosis, outcomes of clinical trials, innovations in pharmacological therapy and surgical techniques, as well as basic science advances that impact clinical practice. Topical areas include, but are not limited to: -Imaging of the retina, choroid and vitreous -Innovations in optical coherence tomography (OCT) -Small-gauge vitrectomy, retinal detachment, chromovitrectomy -Electroretinography (ERG), microperimetry, other functional tests -Intraocular tumors -Retinal pharmacotherapy & drug delivery -Diabetic retinopathy & other vascular diseases -Age-related macular degeneration (AMD) & other macular entities
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
