Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness

Current Status, Hotspots, and Prospects of Artificial Intelligence in Ophthalmology: A Bibliometric Analysis (2003-2023)

PURPOSE

Artificial intelligence (AI) has gained significant attention in ophthalmology. This paper reviews, classifies, and summarizes the research literature in this field and aims to provide readers with a detailed understanding of the current status and future directions, laying a solid foundation for further research and decision-making.

METHODS

Literature was retrieved from the Web of Science database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package Bibliometrix.

RESULTS

The study included 3,377 publications from 4,035 institutions in 98 countries. China and the United States had the most publications. Sun Yat-sen University is a leading institution. Translational Vision Science & Technology"published the most articles, while "Ophthalmology" had the most co-citations. Among 13,145 researchers, Ting DSW had the most publications and citations. Keywords included "Deep learning," "Diabetic retinopathy," "Machine learning," and others.

CONCLUSION

The study highlights the promising prospects of AI in ophthalmology. Automated eye disease screening, particularly its core technology of retinal image segmentation and recognition, has become a research hotspot. AI is also expanding to complex areas like surgical assistance, predictive models. Multimodal AI, Generative Adversarial Networks, and ChatGPT have driven further technological innovation. However, implementing AI in ophthalmology also faces many challenges, including technical, regulatory, and ethical issues, and others. As these challenges are overcome, we anticipate more innovative applications, paving the way for more effective and safer eye disease treatments.

Implementation Mapping of the Collaborative University of California Teleophthalmology Initiative (CUTI): A Qualitative Study Using the Exploration, Preparation, Implementation, and Sustainment (EPIS) Framework

Background This study aimed to investigate the rationale, barriers, and facilitators of teleretinal camera implementation in primary care and endocrinology clinics for diabetic retinopathy (DR) screening across University of California (UC) health systems utilizing the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework. Methodology Institutional representatives from UC Los Angeles, San Diego, San Francisco, and Davis participated in a series of focus group meetings to elicit implementation facilitators and barriers for teleophthalmology programs within their campuses. Site representatives also completed a survey regarding their program's performance over the calendar year 2022 in the following areas: DR screening camera sites, payment sources and coding, screening workflows including clinical, information technology (IT), reading, results, pathologic findings, and follow-up, including patient outreach for abnormal results. Focus group and survey results were mapped to the EPIS framework to gain insights into the implementation process of these programs and identify areas for optimization. Results Four UC campuses with 20 active camera sites screened 7,450 patients in the calendar year 2022. The average DR screening rate across the four campuses was 55%. Variations between sources of payment, turn-around time, image-grading structure, image-report characteristics, IT infrastructure, and patient outreach strategies were identified between sites. Closing gaps in IT integration between data systems, ensuring the financial sustainability of the program, and optimizing patient outreach remain primary challenges across sites and serve as good opportunities for cross-institutional learning. Conclusions Despite the potential for long-term cost savings and improving access to care, numerous obstacles continue to hinder the widespread implementation of teleretinal DR screening. Implementation science approaches can identify strategies for addressing these challenges and optimizing implementation.

American Society of Retina Specialists Artificial Intelligence Task Force Report

Since the Artificial Intelligence Committee of the American Society of Retina Specialists developed the initial task force report in 2020, the artificial intelligence (AI) field has seen further adoption of US Food and Drug Administration-approved AI platforms and significant development of AI for various retinal conditions. With expansion of this technology comes further areas of challenges, including the data sources used in AI, the democracy of AI, commercialization, bias, and the need for provider education on the technology of AI. The overall focus of this committee report is to explore these recent issues as they relate to the continued development of AI and its integration into ophthalmology and retinal practice.

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

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.

A Review of Intraocular Lens Power Calculation Formulas Based on Artificial Intelligence

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.