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

Machine learning model for age-related macular degeneration based on heavy metals: The National Health and Nutrition Examination Survey 2005 to 2008

Age-related macular degeneration (AMD) is the leading cause of blindness in older people in developed countries. It has been suggested that heavy metal exposure may be associated with the development of AMD, but most studies have focused on the effects of a single metal with traditional methods. In this study, we analyzed the relationship between 13 urinary heavy metal concentrations and AMD using NHANES data between 2005 and 2008. We constructed and compared 11 machine learning models to identify the best model for predicting AMD risk. We further interpreted the models by Permutation Feature Importance (PFI), Partial Dependence Plot (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis. 216 AMD patients out of 2380 participants. The random forest (RF) model performed optimally in predicting the risk of AMD, with an AUC value of 0.970. PFI analyses revealed that age and urinary cadmium (Cd) were the main factors influencing the risk of AMD. SHAP analyses further confirmed the significance of Cd concentration in predicting the risk of AMD, and we revealed a significant interaction with significant interaction of race. Our study firstly explored the relationship between heavy metal exposure levels and AMD based on machine learning techniques, found that urinary Cd concentration had the greatest impact on AMD, and revealed the superior predictive performance of machine learning methods. Furthermore, our study provided a new perspective for early screening and intervention of AMD.

Cost-effectiveness and cost-utility of community-based blinding fundus diseases screening with artificial intelligence: A modelling study from Shanghai, China

BACKGROUND

With application of artificial intelligence (AI) in the disease screening, process reengineering occurred simultaneously. Whether process reengineering deserves special emphasis in AI implementation in the community-based blinding fundus diseases screening is not clear.

METHOD

Cost-effectiveness and cost-utility analyses were performed employing decision-analytic Markov models. A hypothetical cohort of community residents was followed in the model over a period of 30 1-year Markov cycles, starting from the age of 60. The simulated cohort was based on work data of the Shanghai Digital Eye Disease Screening program (SDEDS). Three scenarios were compared: centralized screening with manual grading-based telemedicine systems (Scenario 1), centralized screening with an AI-assisted screening system (Scenario 2), and process reengineered screening with an AI-assisted screening system (Scenario 3). The main outcomes were incremental cost-effectiveness ratio (ICER) and incremental cost-utility ratio (ICUR).

RESULTS

Compared with Scenario 1, Scenario 2 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $ 490010.62 per 10,000 people screened, with an ICER of $2619.98 per year of blindness avoided and an ICUR of $4589.13 per QALY. Compared with Scenario 1, Scenario 3 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $242313.23 per 10,000 people screened, with an ICER of $1295.60 per year of blindness avoided and an ICUR of $2269.35 per QALY. Although Scenario 2 and 3 could be considered cost-effective, the screening cost of Scenario 3 was 27.6 % and the total cost was 1.1 % lower, with the same expected effectiveness and utility. The probabilistic sensitivity analyses show that Scenario 3 dominated 69.1 % and 70.3 % of simulations under one and three times the local GDP per capita thresholds.

CONCLUSIONS

AI can improve the cost-effectiveness and cost-utility of screenings, especially when process reengineering is performed. Therefore, process reengineering is strongly recommended when AI is implemented.

Effect of supplementation with lutein, zeaxanthin, and omega-3 fatty acids on macular pigment and visual function in young adults with long-term use of digital devices: study protocol for a randomized double-blind placebo-controlled study

Background

Growing evidence emphasizes the importance of xanthophyll carotenoids and omega-3 fatty acids in eye health. However, the beneficial effects of such supplementation have not been thoroughly discussed among adults with high screen exposure. Current trial evidence on lutein bioavailability is contradictory, and the interactions of dietary intervention with host-related factors remain elusive. This study aims to investigate the comparative effectiveness of supplementation with macular xanthophylls and omega-3 fatty acids on macular pigment optical density (MPOD) and visual function, access the bioavailability of free lutein and lutein ester, and explore the complex interplay between genetic variations, intestinal microbiota, and the dietary intervention in Chinese adults with long-term exposure to digital devices.

Methods

The Lutein, Zeaxanthin, and Omega-3 (LZO) clinical trial is a 24-week multicenter, randomized, double-blind, placebo-controlled trial of 600 participants recruited from research centers, universities, and communities. Individuals are eligible to participate if they are aged over 18 years and use digital devices for over 8 h daily in the last 2 years, and will be randomized to six arms. A total of three visits will be scheduled at baseline, 12 and 24 weeks. The primary outcome is the change in MPOD over the 24-week intervention. The secondary outcomes are changes in visual function (visual acuity, best-corrected visual acuity, contrast and glare sensitivity, critical flicker fusion, reaction time, visuognosis persistence, symptoms and signs of dry eye, retinal thickness, and optical quality), and changes in serum lutein and zeaxanthin concentrations, and erythrocyte membrane omega-3 fatty acids. Genetic variations will be determined using genome-wide genotyping at baseline. 16S rRNA gene sequencing will be utilized to assess microbiome compositional changes before and after intervention.

Discussion

The trial is anticipated to establish early interventions to prevent photochemical ocular damage and delay the onset of vision impairment in young adults with long-term repeated exposure to screen-based electronic devices, and provide valuable insights for the development of precision nutrition strategies for maintaining eye health.

Clinical trial registration

www.clinicaltrials.in.th, Identifier, TCTR20220904002.

Cost-effectiveness analysis of bevacizumab for cerebral radiation necrosis treatment based on real-world utility value in China

BACKGROUND

Bevacizumab shows superior efficacy in cerebral radiation necrosis (CRN) therapy, but its economic burden remains heavy due to the high drug price. This study aims to evaluate the cost-effectiveness of bevacizumab for CRN treatment from the Chinese payers' perspective.

METHODS

A decision tree model was developed to compare the costs and health outcomes of bevacizumab and corticosteroids for CRN therapy. Efficacy and safety data were derived from the NCT01621880 trial, which compared the effectiveness and safety of bevacizumab monotherapy with corticosteroids for CRN in nasopharyngeal cancer patients, and demonstrated that bevacizumab invoked a significantly higher response than corticosteroids (65.5% vs. 31.5%, P < 0.001) with no significant differences in adverse events between two groups. The utility value of the "non-recurrence" status was derived from real-world data. Costs and other utility values were collected from an authoritative Chinese network database and published literature. The primary outcomes were total costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER). The uncertainty of the model was evaluated via one-way and probabilistic sensitivity analyses.

RESULTS

Bevacizumab treatment added 0.12 (0.48 vs. 0.36) QALYs compared to corticosteroid therapy, along with incremental costs of $ 2010 ($ 4260 vs. $ 2160). The resultant ICER was $ 16,866/QALY, which was lower than the willingness-to-pay threshold of $ 38,223/QALY in China. The price of bevacizumab, body weight, and the utility value of recurrence status were the key influential parameters for ICER. Probabilistic sensitivity analysis revealed that the probability of bevacizumab being cost-effectiveness was 84.9%.

CONCLUSION

Compared with corticosteroids, bevacizumab is an economical option for CRN treatment in China.

Global tendency and research trends of minimally invasive surgery for glaucoma from 1992 to 2023: A visual bibliometric analysis

Background

Minimally invasive glaucoma surgery has become a popular research topic over the past decade. However, no published studies have provided a systematic overview for this field. A bibliometric analysis is urgently required to characterise current international trends and provide an intuitive description of past and emerging trends.

Methods

This study analysed minimally invasive glaucoma surgery-related studies by searching the Web of Science for relevant articles published between 1992 and 2023. All the retrieved titles and abstracts were screened for eligibility, and only articles and reviews written in English were included in the analysis. CiteSpace (version 6.1.6), VOSviewer (version 1.6.19), and the bibliometric package in RStudio were used to construct and visualise the results.

Results

A total of 1533 publications were included in the analysis with 26072 citations. A total of 4482 authors from 1191 organizations in 57 countries and regions published papers in 139 journals. After 2010, the number of publications increased significantly, with the highest annual productivity occurring in 2022 (n = 229, 15 %). Most of these studies were published in ophthalmology journals. The journal "Ophthalmology" ranked first with 30 papers and 5275 citations. Among the 10 most productive countries, the United States had the largest share of publications (n = 423, 36 %) and Switzerland had the highest proportion of multiple-country publications (70 %). Neodymium was the first keyword discovered, appearing in 1992 and continuing for 21 years. Kahook dual-blade, progression, gonioscopy-assisted transluminal trabeculotomy, efficacy, minimally invasive glaucoma surgery, cataract extraction, and primary open-angle glaucoma were the most recent keywords since 2020.

Conclusions

This was the first bibliometric analysis of minimally invasive glaucoma surgery and provides an overview of the developments in this field. Our results identified outstanding studies, countries, institutions, journals, and authors in the field to point the way forward for scientific research and clinical applications of minimally invasive glaucoma surgery.

Cost-effectiveness of incorporating self-imaging optical coherence tomography into fundus photography-based diabetic retinopathy screening

Diabetic macular edema (DME) has emerged as the foremost cause of vision loss in the population with diabetes. Early detection of DME is paramount, yet the prevailing screening, relying on two-dimensional and labor-intensive fundus photography (FP), results in frequent unwarranted referrals and overlooked diagnoses. Self-imaging optical coherence tomography (SI-OCT), offering fully automated, three-dimensional macular imaging, holds the potential to enhance DR screening. We conducted an observational study within a cohort of 1822 participants with diabetes, who received comprehensive assessments, including visual acuity testing, FP, and SI-OCT examinations. We compared the performance of three screening strategies: the conventional FP-based strategy, a combination strategy of FP and SI-OCT, and a simulated combination strategy of FP and manual SD-OCT. Additionally, we undertook a cost-effectiveness analysis utilizing Markov models to evaluate the costs and benefits of the three strategies for referable DR. We found that the FP + SI-OCT strategy demonstrated superior sensitivity (87.69% vs 61.53%) and specificity (98.29% vs 92.47%) in detecting DME when compared to the FP-based strategy. Importantly, the FP + SI-OCT strategy outperformed the FP-based strategy, with an incremental cost-effectiveness ratio (ICER) of $8016 per quality-adjusted life year (QALY), while the FP + SD-OCT strategy was less cost-effective, with an ICER of $45,754/QALY. Our results were robust to extensive sensitivity analyses, with the FP + SI-OCT strategy standing as the dominant choice in 69.36% of simulations conducted at the current willingness-to-pay threshold. In summary, incorporating SI-OCT into FP-based screening offers substantial enhancements in sensitivity, specificity for detecting DME, and most notably, cost-effectiveness for DR screening.

The AI revolution in glaucoma: Bridging challenges with opportunities

Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.

A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children

The increasing prevalence of myopia worldwide presents a significant public health challenge. A key strategy to combat myopia is with early detection and prediction in children as such examination allows for effective intervention using readily accessible imaging technique. To this end, we introduced DeepMyopia, an artificial intelligence (AI)-enabled decision support system to detect and predict myopia onset and facilitate targeted interventions for children at risk using routine retinal fundus images. Based on deep learning architecture, DeepMyopia had been trained and internally validated on a large cohort of retinal fundus images (n = 1,638,315) and then externally tested on datasets from seven sites in China (n = 22,060). Our results demonstrated robustness of DeepMyopia, with AUCs of 0.908, 0.813, and 0.810 for 1-, 2-, and 3-year myopia onset prediction with the internal test set, and AUCs of 0.796, 0.808, and 0.767 with the external test set. DeepMyopia also effectively stratified children into low- and high-risk groups (p < 0.001) in both test sets. In an emulated randomized controlled trial (eRCT) on the Shanghai outdoor cohort (n = 3303) where DeepMyopia showed effectiveness in myopia prevention compared to NonCyc-based model, with an adjusted relative reduction (ARR) of -17.8%, 95% CI: -29.4%, -6.4%. DeepMyopia-assisted interventions attained quality-adjusted life years (QALYs) of 0.75 (95% CI: 0.53, 1.04) per person and avoided blindness years of 13.54 (95% CI: 9.57, 18.83) per 1 million persons compared to natural lifestyle with no active intervention. Our findings demonstrated DeepMyopia as a reliable and efficient AI-based decision support system for intervention guidance for children.

Habitual coffee and tea consumption and risk of cataract: A prospective cohort study from the UK Biobank

BACKGROUND

To study the association of habitual coffee and tea consumption with the risk of cataract.

METHODS

This prospective cohort study enrolled UK Biobank participants between 2006 and 2010, and prospectively followed them up for cataract diagnosis. We examined the associations of self-reported intake of tea and coffee and the calculated combined caffeine intake, with the risk of incident cataract. Cox proportional hazards models were analyzed after adjusting for age, sex, race, diabetes, Townsend Index, income, education, smoking and alcohol status.

RESULTS

A total of 444,787 UK Biobank participants aged from 37 to 73 years old who had no cataract at baseline were included. Coffee intake of 2-3 cups/day (HR 0.973, 95% CI 0.949-0.998) or tea intake of 4-6 cups/day (HR 0.962, 95% CI 0.934-0.990) or combination caffeine intake of 160.0-235.0 mg/day (HR 0.950, 95% CI 0.925-0.976) were linked with the lowest risk of incident cataract. Cox models with restricted cubic splines showed J-shaped associations of coffee, tea, and combined caffeine intake with the risk of cataract (all p for nonlinear <0.001).

CONCLUSIONS

Moderate habitual consumption of coffee and tea is associated with a lower risk of cataract. To maximize the protective effect against cataract, it is advisable to control total caffeine intake from coffee and tea within a range of 160.0-235.0 mg/day.

Cost-Utility Analysis of Rosuvastatin (20 mg) to Prevent Cardiovascular Diseases in Iran

Background

Cardiovascular diseases are a main cause of disease burden in developing and developed countries. This study aimed to evaluate the cost-utility of rosuvastatin 20 mg in contrast with no intervention for the prevention of cardiovascular disease in Iran.

Materials and Methods

The costs and utility of rosuvastatin 20 mg were compared to nonintervention in patients with cardiovascular disease for the whole lifetime horizon in this study using the Markov model. Cost and utility data were taken from literature. After estimating the incremental cost-effectiveness ratio, a sensitivity analysis was performed using TreeAge Pro 2011 software to cope with uncertainty.

Results

Based on finding, the expected cost and quality-adjusted life years (QALYs) of using rosuvastatin 20 mg were $300 and 12, and the values for no intervention were $56 and $10, respectively. Given the threshold of $20800, using rosuvastatin 20 mg was cost-effective compared to no intervention and the incremental cost was $122 per QALY. The results showed that the highest costs were related to admission to the coronary care unit (CCU) ward. Moreover, among the costs of paraclinical services, the highest were those of echocardiography. Furthermore, Troponin accounted for most of the cost of laboratory tests.

Conclusion

It is recommended that policymakers consider using rosuvastatin 20 mg by cardiologists while designing clinical guidelines for the diagnosis of patients with cardiovascular diseases. Because of the high cost of cardiovascular diseases in Iran, it is suggested that policymakers should consider cost control strategies to impose lower costs on patients.

FaceMotionPreserve: a generative approach for facial de-identification and medical information preservation

Telemedicine and video-based diagnosis have raised significant concerns regarding the protection of facial privacy. Effective de-identification methods require the preservation of diagnostic information related to normal and pathological facial movements, which play a crucial role in the diagnosis of various movement, neurological, and psychiatric disorders. In this work, we have developed FaceMotionPreserve , a deep generative model-based approach that transforms patients' facial identities while preserving facial dynamics with a novel face dynamic similarity module to enhance facial landmark consistency. We collected test videos from patients with Parkinson's disease recruited via telemedicine for evaluation of model performance and clinical applicability. The performance of FaceMotionPreserve was quantitatively evaluated based on neurologist diagnostic consistency, critical facial behavior fidelity, and correlation of general facial dynamics. In addition, we further validated the robustness and advancements of our model in preserving medical information with clinical examination videos from a different cohort of patients. FaceMotionPreserve is applicable to real-time integration, safeguarding facial privacy while retaining crucial medical information associated with facial movements to address concerns in telemedicine, and facilitating safer and more collaborative medical data sharing.

A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality

Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the health economics of AI in this field. We examine studies from the PubMed, Google Scholar, and Web of Science databases that employed quantitative analysis, retrieved up to July 2023. Most of the studies indicate that AI leads to cost savings and improved efficiency in ophthalmology. On the other hand, some studies suggest that using AI in healthcare may raise costs for patients, especially when taking into account factors such as labor costs, infrastructure, and patient adherence. Future research should cover a wider range of ophthalmic diseases beyond common eye conditions. Moreover, conducting extensive health economic research, designed to collect data relevant to its own context, is imperative.

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.

Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.

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

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.

Trends in the Prevalence of Common Retinal and Optic Nerve Diseases in China: An Artificial Intelligence Based National Screening

Purpose

Retinal and optic nerve diseases have become the primary cause of irreversible vision loss and blindness. However, there is still a lack of thorough evaluation regarding their prevalence in China.

Methods

This artificial intelligence-based national screening study applied a previously developed deep learning algorithm, named the Retinal Artificial Intelligence Diagnosis System (RAIDS). De-identified personal medical records from January 2019 to December 2021 were extracted from 65 examination centers in 19 provinces of China. Crude prevalence and age-sex-adjusted prevalence were calculated by mapping to the standard population in the seventh national census.

Results

In 2021, adjusted referral possible glaucoma (63.29, 95% confidence interval [CI] = 57.12-68.90 cases per 1000), epiretinal macular membrane (21.84, 95% CI = 15.64-29.22), age-related macular degeneration (13.93, 95% CI = 11.09-17.17), and diabetic retinopathy (11.33, 95% CI = 8.89-13.77) ranked the highest among 10 diseases. Female participants had significantly higher adjusted prevalence of pathologic myopia, yet a lower adjusted prevalence of diabetic retinopathy, referral possible glaucoma, and hypertensive retinopathy than male participants. From 2019 to 2021, the adjusted prevalence of retinal vein occlusion (0.99, 95% CI = 0.73-1.26 to 1.88, 95% CI = 1.42-2.44), macular hole (0.59, 95% CI = 0.41-0.82 to 1.12, 95% CI = 0.76-1.51), and hypertensive retinopathy (0.53, 95% CI = 0.40-0.67 to 0.77, 95% CI = 0.60-0.95) significantly increased. The prevalence of diabetic retinopathy in participants under 50 years old significant increased.

Conclusions

Retinal and optic nerve diseases are an important public health concern in China. Further well-conceived epidemiological studies are required to validate the observed increased prevalence of diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, and macular hole nationwide.

Translational Relevance

This artificial intelligence system can be a potential tool to monitor the prevalence of major retinal and optic nerve diseases over a wide geographic area.

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

INTRODUCTION

Early 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 ANALYSIS

This 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 DISSEMINATION

The 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 NUMBER

ChiCTR2300070082.

Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case

Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI's sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI's sensitivity.

A deep learning system for predicting time to progression of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

[Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis]

This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first part of the review provides information how AI methods improve the effectiveness of glaucoma screening, presents the technologies using deep learning, including neural networks, for the analysis of big data obtained by methods of ocular imaging (fundus imaging, optical coherence tomography of the anterior and posterior eye segments, digital gonioscopy, ultrasound biomicroscopy, etc.), including a multimodal approach. The results found in the reviewed literature are contradictory, indicating that improvement of the AI models requires further research and a standardized approach. The use of neural networks for timely detection of glaucoma based on multimodal imaging will reduce the risk of blindness associated with glaucoma.

Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis

Background

We aimed to evaluate the cost-effectiveness of an artificial intelligence-(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non-Indigenous and Indigenous people living with diabetes in Australia.

Methods

We performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decision-analytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non-Indigenous and 65,160 Indigenous Australians living with diabetes aged ≥20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI-based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit-cost ratio (BCR), and net monetary benefits (NMB). A Willingness-to-pay (WTP) threshold of AU$50,000 per quality-adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study.

Findings

With the status quo, the non-Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost-saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost-saving for the Indigenous population. Notably, universal AI-based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m.

Interpretation

Our findings suggest that implementing AI-based DR screening in primary care is highly effective and cost-saving in both Indigenous and non-Indigenous populations.

Funding

This project received grant funding from the Australian Government: the National Critical Research Infrastructure Initiative, Medical Research Future Fund (MRFAI00035) and the NHMRC Investigator Grant (APP1175405). The contents of the published material are solely the responsibility of the Administering Institution, a participating institution or individual authors and do not reflect the views of the NHMRC. This work was supported by the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. W.H. is supported by the Melbourne Research Scholarship established by the University of Melbourne. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Quality assessment of non-mydriatic fundus photographs for glaucoma screening in primary healthcare centres: a real-world study

BACKGROUND

This study assessed the quality distribution of non-mydriatic fundus photographs (NMFPs) in real-world glaucoma screening and analysed its influencing factors.

METHODS

This cross-sectional study was conducted in primary healthcare centres in the Yinzhou District, China, from 17 March to 3 December 2021. The quality distribution of bilateral NMFPs was assessed by the Digital Reading Department of the Eye Hospital of Wenzhou Medical University. Generalised estimating equations and logistic regression models identified factors affecting image quality.

RESULTS

A total of 17 232 photographs of 8616 subjects were assessed. Of these, 11.9% of images were reliable for the right eyes, while only 4.6% were reliable for the left eyes; 93.6% of images were readable in the right eyes, while 90.3% were readable in the left eyes. In adjusted models, older age was associated with decreased odds of image readability (adjusted OR (aOR)=1.07, 95% CI 1.06~1.08, p<0.001). A larger absolute value of spherical equivalent significantly decreased the odds of image readability (all p<0.001). Media opacity and worse visual acuity had a significantly lower likelihood of achieving readable NMFPs (aOR=1.52, 95% CI 1.31~1.75; aOR=1.70, 95% CI 1.42~2.02, respectively, all p<0.001). Astigmatism axes within 31°~60° and 121°~150° had lower odds of image readability (aOR=1.35, 95% CI 1.11~1.63, p<0.01) than astigmatism axes within 180°±30°.

CONCLUSIONS

The image readability of NMFPs in large-scale glaucoma screening for individuals 50 years and older is comparable with relevant studies, but image reliability is unsatisfactory. Addressing the associated factors may be vital when implementing ophthalmological telemedicine in underserviced areas.

TRIAL REGISTRATION NUMBER

ChiCTR2200059277.

Identifying, Understanding, and Addressing Disparities in Glaucoma Care in the United States

Glaucoma is the leading cause of irreversible blindness worldwide, currently affecting around 80 million people. Glaucoma prevalence is rapidly rising in the United States due to an aging population. Despite recent advances in the diagnosis and treatment of glaucoma, significant disparities persist in disease detection, management, and outcomes among the diverse patient populations of the United States. Research on disparities is critical to identifying, understanding, and addressing societal and healthcare inequalities. Disparities research is especially important and impactful in the context of irreversible diseases such as glaucoma, where earlier detection and intervention are the primary approach to improving patient outcomes. In this article, we first review recent studies identifying disparities in glaucoma care that affect patient populations based on race, age, and gender. We then review studies elucidating and furthering our understanding of modifiable factors that contribute to these inequities, including socioeconomic status (particularly age and education), insurance product, and geographic region. Finally, we present work proposing potential strategies addressing disparities in glaucoma care, including teleophthalmology and artificial intelligence. We also discuss the presence of non-modifiable factors that contribute to differences in glaucoma burden and can confound the detection of glaucoma disparities.

Translational Relevance

By recognizing underlying causes and proposing potential solutions, healthcare providers, policymakers, and other stakeholders can work collaboratively to reduce the burden of glaucoma and improve visual health and clinical outcomes in vulnerable patient populations.

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

Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial intelligence (AI) algorithms have been developed to autonomously screen for DR from fundus photography without human input. Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable DR compared with human graders; however, many questions still remain. In this narrative review on AI in DR screening, we discuss key concepts in AI algorithm development as a background for understanding the algorithms. We present the AI algorithms that have been prospectively validated against human graders and demonstrate the variability of reference standards and cohort demographics. We review the limited head-to-head validation studies where investigators attempt to directly compare the available algorithms. Next, we discuss the literature regarding cost-effectiveness, equity and bias, and medicolegal considerations, all of which play a role in the implementation of these AI algorithms in clinical practice. Lastly, we highlight ongoing efforts to bridge gaps in AI model data sets to pursue equitable development and delivery.

Antioxidant nanoemulsion loaded with latanoprost enables highly effective glaucoma treatment

Glaucoma is the third leading cause of blindness worldwide and is primarily characterized by elevated intraocular pressure (IOP). Common risk factors such as age, myopia, ocular trauma, and hypertension all increase the risk of elevated IOP. Prolonged high IOP not only causes physiological discomfort like headaches, but also directly damages retinal cells and leads to retinal ischemia, oxidative imbalance, and accumulation of reactive oxygen species (ROS) in the retina. This oxidative stress causes the oxidation of proteins and unsaturated lipids, leading to peroxide formation and exacerbating retinal damage. While current clinical treatments primarily target reducing IOP through medication or surgery, there are currently no effective methods to mitigate the retinal cell damage associated with glaucoma. To address this gap, we developed a novel nanoemulsion to co-delivery latanoprost and α-tocopherol (referred to as LA@VNE later) that prolongs ocular retention and enhances retinal permeability through localized administration. By encapsulating latanoprost, an IOP-lowering drug, and α-tocopherol, a potent antioxidant, we effectively reduced ROS accumulation (>1.5-fold in vitro and 2.5-fold in vivo), retinal ganglion cell (RGC) apoptosis (>9 fold), and inflammatory cell infiltration (>1.6 fold). Our approach showed strong biocompatibility and significant potential for clinical translation, providing a promising platform for the treatment of glaucoma.

Personalized Medicine in Ophthalmic Diseases: Challenges and Opportunities

Personalized medicine is a broadly used term to encompass approaches used to tailor healthcare to the needs of individual patients [...].