A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway

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

A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway

Int J Retina Vitreous. 2024 May 23;10(1):40. doi: 10.1186/s40942-024-00547-3.

Authors

Mia Karabeg^{1

2}, Goran Petrovski^{1

2

3

4}, Silvia Nw Hertzberg^{1

2}, Maja Gran Erke², Dag Sigurd Fosmark², Greg Russell⁵, Morten C Moe^{1

2}, Vallo Volke⁶, Vidas Raudonis⁷, Rasa Verkauskiene⁸, Jelizaveta Sokolovska⁹, Inga-Britt Kjellevold Haugen¹⁰, Beata Eva Petrovski^{11

12

13}

Affiliations

¹ Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway.
² Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway.
³ Department of Ophthalmology, University of Split School of Medicine and University Hospital Centre, 21000, Split, Croatia.
⁴ UKLONetwork, University St. Kliment Ohridski-Bitola, 7000, Bitola, Macedonia.
⁵ Clinical Development, Eyenuk Inc, Woodland Hills, CA, USA.
⁶ Faculty of Medicine, Tartu University, 50411, Tartu, Estonia.
⁷ Automation Department, Kaunas University of Technology, 51368, Kaunas, Lithuania.
⁸ Institute of Endocrinology, Lithuanian University of Health Sciences, 50161, Kaunas, Lithuania.
⁹ Faculty of Medicine, University of Latvia, Jelgavas street 3, LV1004, Riga, Latvia.
¹⁰ Norwegian Association of the Blind and Partially Sighted, Oslo, Norway.
¹¹ Center for Eye Research and Innovative Diagnostics, Department of Ophthalmology, Institute for Clinical Medicine, University of Oslo, Kirkeveien 166, 0450, Oslo, Norway. [email protected].
¹² Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway. [email protected].
¹³ Institute of Endocrinology, Lithuanian University of Health Sciences, 50161, Kaunas, Lithuania. [email protected].

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.

Keywords: Artificial intelligence; Automated grading; Cost-analysis; Diabetic retinopathy; Manual grading; Minority women; Norway; Screening.