ABSTRACT
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.
ABSTRACT
PURPOSE: The aim of this study was to estimate the 1-year costs associated with treating diabetic macular oedema (DME) patients using current intravitreal anti-vascular endothelial growth factor (anti-VEGF) biologics compared with the dexamethasone implant. METHODS: We conducted a descriptive cost-evaluation analysis using data from Oslo University Hospital and literature to compare three different intravitreal drugs for DME: bevacizumab, aflibercept and dexamethasone. Stratification of patients into 'Naive' or 'Switch' group was based on treatment history. We estimated the costs from healthcare and 'extended' healthcare perspectives. Sensitivity analysis evaluated the impact of various parameters. RESULTS: The average injections per patient per year for the Naive group (bevacizumab), Switch group (aflibercept) and dexamethasone were 9.5, 9.1 and 3.0 respectively. From a healthcare perspective, the 1-year costs for the Naive group were 15% lower (bevacizumab, 3619), and for the Switch group, 23% higher (aflibercept, 5226) compared with dexamethasone (4252). The 'extended' healthcare perspective showed the cost per patient per year for bevacizumab remained nominally lower in the Naive group, while dexamethasone remained lower for the Switch group (5116 for dexamethasone, compared to 4987 for bevacizumab and 6537 for aflibercept). CONCLUSIONS: From a primary healthcare perspective, the dexamethasone as a first-line DME treatment may increase economic costs in settings where bevacizumab is used off-label. Treating resistant DMEwith dexamethasone may reduce the costs and treatment burden compared with switching to aflibercept.