一项在挪威奥斯陆的少数族裔女性中比较基于人工智能的EyeArt®与眼科医生对糖尿病视网膜病变评估的试点成本分析研究。
A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway.
作者信息
Karabeg Mia, Petrovski Goran, Hertzberg Silvia Nw, Erke Maja Gran, Fosmark Dag Sigurd, Russell Greg, Moe Morten C, Volke Vallo, Raudonis Vidas, Verkauskiene Rasa, Sokolovska Jelizaveta, Haugen Inga-Britt Kjellevold, Petrovski Beata Eva
机构信息
Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway.
Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway.
出版信息
Int J Retina Vitreous. 2024 May 23;10(1):40. doi: 10.1186/s40942-024-00547-3.
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.
背景
糖尿病视网膜病变（DR）是全球劳动年龄人口中成人失明的主要原因，可通过早期检测来预防。建议定期进行眼部检查，这对于检测威胁视力的DR至关重要。需要利用人工智能（AI）来减轻医疗系统的负担。
目的
在挪威奥斯陆对患有糖尿病（DM）的少数族裔女性队列进行一项初步成本分析研究，该国该群体的糖尿病患病率最高，采用人工（眼科医生）和自主（AI）分级两种方式检测DR。据我们所知，这是挪威第一项在视网膜图像DR分级中使用AI的研究。
方法
2017年11月1日挪威奥斯陆的少数族裔妇女节，对33名18岁以上诊断为DM（1型糖尿病和2型糖尿病）的患者（66只眼）进行筛查。使用Eidon - 真彩色共聚焦扫描仪（美国CenterVue公司）进行视网膜成像，筛查完成后由一名眼科医生以及使用EyeArt自动DR检测系统2.1.0版（美国加利福尼亚州EyeArt公司EyeNuk）自动对DR进行分级。分级基于国际临床糖尿病视网膜病变（ICDR）严重程度量表[1]来检测是否存在可转诊的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。在检测DR方面，人工与基于AI的EyeArt分级系统之间发现了非常好的评分者间一致性：0.98（P < 0.01）。DR的患病率为18.6%（95%可信区间：11.4 - 25.8%），敏感性和特异性分别为100%（95%可信区间：100 - 100%和95%可信区间：100 - 100%）。与人工筛查相比，AI筛查每位患者的成本差异低143美元（成本节约），有利于AI。
结论
我们的结果表明，EyeArt AI系统在临床实践中是一种可靠、节省成本且有用的DR分级工具。
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