Economic evaluation of combined population-based screening for multiple blindness-causing eye diseases in China: a cost-effectiveness analysis

Liu, Hanruo; Li, Ruyue; Zhang, Yue; Zhang, Kaiwen; Yusufu, Mayinuer; Liu, Yanting; Mou, Dapeng; Chen, Xiaoniao; Tian, Jingkui; Li, Huiqi; Fan, Sujie; Tang, Jianjun; Wang, Ke

doi:10.1016/s2214-109x(22)00554-x

Cited by 36 publications

(33 citation statements)

References 26 publications

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“…In countries with high income (HIC), cost of human graders is higher, so it is easier for AI algorithm to price lower [ 38 , 39 ]. Studies from Thailand and China, considered to be low- and middle-income countries (LMIC), found AI to be more cost-effective [ 40 , 41 ]. However, other studies from China [ 42 ] and Brazil [ 43 ] have claimed the opposite, arguing that factors like “years without blindness” and better compliance with patient referrals could contribute to AI being more cost-effective than humans [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

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

Karabeg,

Petrovski,

Hertzberg

et al. 2024

Int J Retin Vitr

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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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Section: Discussionmentioning

confidence: 99%

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

Karabeg,

Petrovski,

Hertzberg

et al. 2024

Int J Retin Vitr

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“…To depict these strategies, a decision tree will be developed (see online supplemental figure 1). The classification of health states for each blinding eye disease and the transitions between these states will be established based on relevant literature on health economic analyses of eye disease screening from reputable journals 9 11 12…”

Section: Methods and Analysismentioning

confidence: 99%

“…Lin et al 7 constructed the Comprehensive Artificial intelligence Retinal Expert system, capable of identifying 14 retinal abnormalities, including DR, AMD, glaucomatous optic neuropathy (GON) and pathological myopia (PM). Additionally, several studies have conducted cost-effectiveness analyses of community screening for eye diseases such as cataract, glaucoma, DR, AMD and PM 8–12. These AI models and systems have demonstrated promise as low-cost diagnostic tools that could aid in triage situations where access to eye care is limited.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the accuracy of the Ophthalmologist Robot for multiple blindness-causing eye diseases: a multicentre, prospective study protocol

Li,

Tan,

Xie

et al. 2024

BMJ Open

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IntroductionEarly eye screening and treatment can reduce the incidence of blindness by detecting and addressing eye diseases at an early stage. The Ophthalmologist Robot is an automated device that can simultaneously capture ocular surface and fundus images without the need for ophthalmologists, making it highly suitable for primary application. However, the accuracy of the device’s screening capabilities requires further validation. This study aims to evaluate and compare the screening accuracies of ophthalmologists and deep learning models using images captured by the Ophthalmologist Robot, in order to identify a screening method that is both highly accurate and cost-effective. Our findings may provide valuable insights into the potential applications of remote eye screening.Methods and analysisThis is a multicentre, prospective study that will recruit approximately 1578 participants from 3 hospitals. All participants will undergo ocular surface and fundus images taken by the Ophthalmologist Robot. Additionally, 695 participants will have their ocular surface imaged with a slit lamp. Relevant information from outpatient medical records will be collected. The primary objective is to evaluate the accuracy of ophthalmologists’ screening for multiple blindness-causing eye diseases using device images through receiver operating characteristic curve analysis. The targeted diseases include keratitis, corneal scar, cataract, diabetic retinopathy, age-related macular degeneration, glaucomatous optic neuropathy and pathological myopia. The secondary objective is to assess the accuracy of deep learning models in disease screening. Furthermore, the study aims to compare the consistency between the Ophthalmologist Robot and the slit lamp in screening for keratitis and corneal scar using the Kappa test. Additionally, the cost-effectiveness of three eye screening methods, based on non-telemedicine screening, ophthalmologist-telemedicine screening and artificial intelligence-telemedicine screening, will be assessed by constructing Markov models.Ethics and disseminationThe study has obtained approval from the ethics committee of the Ophthalmology and Optometry Hospital of Wenzhou Medical University (reference: 2023-026 K-21-01). This work will be disseminated by peer-review publications, abstract presentations at national and international conferences and data sharing with other researchers.Trial registration numberChiCTR2300070082.

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“…The development of diagnostic products for eye diseases assisted by artificial intelligence (AI) has matured in recent years 5 , with a growing number of such products receiving medical device approval and entering the market. Research indicates that the adoption of AI for eye disease screening can significantly alleviate the dependency on ophthalmologists and improve the cost-efficiency of screenings 6 – 8 . However, during community eye disease screening, the inability to perform pupil dilation, coupled with inferior image capture conditions, personnel, and environment compared with clinical settings, results in compromised image quality.…”

Section: Introductionmentioning

confidence: 99%

RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis

Zhang,

Lin,

Cheng

et al. 2024

npj Digit. Med.

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Visual impairments and blindness are major public health concerns globally. Effective eye disease screening aided by artificial intelligence (AI) is a promising countermeasure, although it is challenged by practical constraints such as poor image quality in community screening. The recently developed ophthalmic foundation model RETFound has shown higher accuracy in retinal image recognition tasks. This study developed an RETFound-enhanced deep learning (DL) model for multiple-eye disease screening using real-world images from community screenings. Our results revealed that our DL model improved the sensitivity and specificity by over 15% compared with commercial models. Our model also shows better generalisation ability than AI models developed using traditional processes. Additionally, decision curve analysis underscores the higher net benefit of employing our model in both urban and rural settings in China. These findings indicate that the RETFound-enhanced DL model can achieve a higher net benefit in community-based screening, advocating its adoption in low- and middle-income countries to address global eye health challenges.

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Economic evaluation of combined population-based screening for multiple blindness-causing eye diseases in China: a cost-effectiveness analysis

Cited by 36 publications

References 26 publications

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

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

Evaluating the accuracy of the Ophthalmologist Robot for multiple blindness-causing eye diseases: a multicentre, prospective study protocol

RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis

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