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Karabeg M, Petrovski G, Hertzberg SN, Erke MG, Fosmark DS, Russell G, Moe MC, Volke V, Raudonis V, Verkauskiene R, Sokolovska J, Haugen IBK, Petrovski BE. 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; 10:40. [PMID: 38783384 PMCID: PMC11112837 DOI: 10.1186/s40942-024-00547-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 03/17/2024] [Indexed: 05/25/2024] Open
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.
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Affiliation(s)
- Mia Karabeg
- 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
| | - Goran Petrovski
- 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
- Department of Ophthalmology, University of Split School of Medicine and University Hospital Centre, 21000, Split, Croatia
- UKLONetwork, University St. Kliment Ohridski-Bitola, 7000, Bitola, Macedonia
| | - Silvia Nw Hertzberg
- 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
| | - Maja Gran Erke
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Dag Sigurd Fosmark
- Department of Ophthalmology, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway
| | - Greg Russell
- Clinical Development, Eyenuk Inc, Woodland Hills, CA, USA
| | - Morten C Moe
- 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
| | - Vallo Volke
- Faculty of Medicine, Tartu University, 50411, Tartu, Estonia
| | - Vidas Raudonis
- Automation Department, Kaunas University of Technology, 51368, Kaunas, Lithuania
| | - Rasa Verkauskiene
- Institute of Endocrinology, Lithuanian University of Health Sciences, 50161, Kaunas, Lithuania
| | | | | | - Beata Eva Petrovski
- 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.
- Institute of Endocrinology, Lithuanian University of Health Sciences, 50161, Kaunas, Lithuania.
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Stopyra W, Cooke DL, Grzybowski A. A Review of Intraocular Lens Power Calculation Formulas Based on Artificial Intelligence. J Clin Med 2024; 13:498. [PMID: 38256632 PMCID: PMC10816994 DOI: 10.3390/jcm13020498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/01/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
PURPOSE The proper selection of an intraocular lens power calculation formula is an essential aspect of cataract surgery. This study evaluated the accuracy of artificial intelligence-based formulas. DESIGN Systematic review. METHODS This review comprises articles evaluating the exactness of artificial intelligence-based formulas published from 2017 to July 2023. The papers were identified by a literature search of various databases (Pubmed/MEDLINE, Google Scholar, Crossref, Cochrane Library, Web of Science, and SciELO) using the terms "IOL formulas", "FullMonte", "Ladas", "Hill-RBF", "PEARL-DGS", "Kane", "Karmona", "Hoffer QST", and "Nallasamy". In total, 25 peer-reviewed articles in English with the maximum sample and the largest number of compared formulas were examined. RESULTS The scores of the mean absolute error and percentage of patients within ±0.5 D and ±1.0 D were used to estimate the exactness of the formulas. In most studies the Kane formula obtained the smallest mean absolute error and the highest percentage of patients within ±0.5 D and ±1.0 D. Second place was typically achieved by the PEARL DGS formula. The limitations of the studies were also discussed. CONCLUSIONS Kane seems to be the most accurate artificial intelligence-based formula. PEARL DGS also gives very good results. Hoffer QST, Karmona, and Nallasamy are the newest, and need further evaluation.
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Affiliation(s)
- Wiktor Stopyra
- MW-Med Eye Centre, 31-416 Krakow, Poland;
- Department of Medicine, University of Applied Sciences, 34-400 Nowy Targ, Poland
| | - David L. Cooke
- Great Lakes Eye Care, Saint Joseph, MI 49085, USA;
- Department of Neurology and Ophthalmology, College of Osteopathic Medicine, Michigan State University, East Lansing, MI 48824, USA
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, 61-553 Poznan, Poland
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