背景:糖尿病视网膜病变(DR)是全球工作年龄人群中成人失明的主要原因,这可以通过早期检测来预防。建议定期进行眼科检查,这对于检测威胁视力的DR至关重要。需要使用人工智能(AI)来减轻医疗保健系统的负担。
目的:为了进行一项成本分析试验研究,以检测奥斯陆患有DM的少数族裔女性人群中的DR,挪威,全国糖尿病(DM)患病率最高,使用手动(眼科医生)和自主(AI)分级。这是挪威的第一项研究,据我们所知,使用人工智能对视网膜图像进行DR分级。
方法:在少数民族妇女节,2017年11月1日,在奥斯陆,挪威,筛查了33例(66只眼)18岁以上被诊断为DM(T1D和T2D)的患者。Eidon-真彩色共聚焦扫描仪(CenterVue,美国)用于视网膜成像,并在筛查完成后对DR进行分级,由眼科医生自动,使用EyeArt自动DR检测系统,版本2.1.0(EyeArt,EyeNuk,CA,美国)。分级基于国际临床糖尿病视网膜病变(ICDR)严重程度量表[1],检测是否存在可参考的DR。对两种分级方法都进行了成本最小化分析。
结果:33名女性(64只眼)符合分析条件。评分者之间的一致性很好:0.98(P<0.01),在人类和基于AI的EyeArt分级系统之间,用于检测DR.DR的患病率为18.6%(95%CI:11.4-25.8%),敏感性和特异性为100%(95%CI:100-100%,95%CI:100-100%),分别。与人类筛查相比,AI筛查的成本差异为每位患者143美元(节省成本),有利于AI。
结论:我们的结果表明,EyeArtAI系统既是可靠的,节约成本,和临床实践中DR分级的有用工具。
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.
OBJECTIVE: 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.
CONCLUSIONS: Our results indicate that The EyeArt AI system is both a reliable, cost-saving, and useful tool for DR grading in clinical practice.