Teleophthalmology and Artificial Intelligence As Game Changers in Ophthalmic Care After the COVID-19 Pandemic

Teleophthalmology at a primary and tertiary eye care network from India: environmental and economic impact

OBJECTIVE

To evaluate the environmental and economic impact of teleophthalmological services provided by a primary (rural) and tertiary (urban) eyecare network in India.

METHODS

This prospective study utilised a random sampling method, and administered an environmental and economic impact assessment questionnaire. The study included 324 (primary: 173; tertiary: 151) patients who received teleconsultations from July to September 2022. The primary network (rural) used a colour-coded triage system (Green: eye conditions managed by teleconsult alone; yellow: semi-urgent referral within 1 week to a month, red: urgent referral within a day to a week). The tertiary network (urban) included new and follow-up patients. The environmental impact was assessed by estimating the potential CO2 emissions saved by avoiding travel for various transport modes. Economic impact measured by the potential cost savings from direct (travel) and indirect (food and wages lost) expenses spent by yellow and red referrals (primary) and the first-visit expenses of follow-up (tertiary) patients.

RESULTS

The primary rural network saved 2.89 kg CO2/person and 80 km/person. The tertiary urban network saved 176.6 kg CO2/person and 1666 km/person. The potential cost savings on travel expenses were INR 19,970 (USD 250) for the primary (average: INR 370 (USD 4.6) per patient) and INR 758,870 (USD 9486) for the tertiary network (average: INR 8339 (USD 104) per patient). Indirect cost savings (food and wages) were of INR 29,100 (USD 364) for the primary and INR 347,800 (USD 4347) for the tertiary network.

CONCLUSION

Teleophthalmology offers substantial environmental and economic benefits in rural and urban eyecare systems.

Unveiling the Potential: A Comprehensive Review of Artificial Intelligence Applications in Ophthalmology and Future Prospects

Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in the field of ophthalmology. This comprehensive review examines the current applications of AI in ophthalmology, highlighting its significant contributions to diagnostic accuracy, treatment efficacy, and patient care. AI technologies, such as deep learning algorithms, have demonstrated exceptional performance in the early detection and diagnosis of various eye conditions, including diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Additionally, AI has enhanced the analysis of ophthalmic imaging techniques like optical coherence tomography (OCT) and fundus photography, facilitating more precise disease monitoring and management. The review also explores AI's role in surgical assistance, predictive analytics, and personalized treatment plans, showcasing its potential to revolutionize clinical practice and improve patient outcomes. Despite these advancements, challenges such as data privacy, regulatory hurdles, and ethical considerations remain. The review underscores the need for continued research and collaboration among clinicians, researchers, technology developers, and policymakers to address these challenges and fully harness the potential of AI in improving eye health worldwide. By integrating AI with teleophthalmology and developing AI-driven wearable devices, the future of ophthalmic care promises enhanced accessibility, efficiency, and efficacy, ultimately reducing the global burden of visual impairment and blindness.

Smartphone Eye Examination: Artificial Intelligence and Telemedicine

Background: The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in particular, it contributes greatly to screening diseases with high rates of blindness. Eye examination using smartphones essentially represents a cheap and safe method, thus contributing to public policies on population screening. This review aims to provide an update on the use of this resource and its future prospects, especially as a screening and ophthalmic diagnostic tool. Methods: In this review, we surveyed major published advances in retinal and anterior segment analysis using AI. We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for published literature without a deadline. We included studies that compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting prevalent diseases with an accurate or commonly employed reference standard. Results: There are few databases with complete metadata, providing demographic data, and few databases with sufficient images involving current or new therapies. It should be taken into consideration that these are databases containing images captured using different systems and formats, with information often being excluded without essential detailing of the reasons for exclusion, which further distances them from real-life conditions. The safety, portability, low cost, and reproducibility of smartphone eye images are discussed in several studies, with encouraging results. Conclusions: The high level of agreement between conventional and a smartphone method shows a powerful arsenal for screening and early diagnosis of the main causes of blindness, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration. In addition to streamlining the medical workflow and bringing benefits for public health policies, smartphone eye examination can make safe and quality assessment available to the population.

Examining Global Crises: Extracting Insights From the COVID-19 Pandemic and Natural Disasters to Develop a Robust Emergency Diabetic Retinopathy Strategy for Puerto Rico

In this critical analysis, we investigate the profound impact of natural disasters and pandemics on the care and adherence to treating diabetic retinopathy, a severe complication of diabetes requiring continuous monitoring and treatment to prevent vision loss. Our study also sheds light on the social and economic context of Puerto Rico, emphasizing recent emergency events that have exacerbated existing public health challenges. Through a comprehensive review of relevant literature from PubMed, Google Scholar, and the George Washington University Himmelfarb Health Sciences Library database, we identified 31 pertinent articles out of 45 evaluated, focusing on the effects of these crises on healthcare delivery, diabetic retinopathy screening, and treatment. The evidence strongly indicates that during such emergencies, barriers to healthcare escalate, leading to significant treatment delays and a reduction in diabetic retinopathy screening and diagnosis, ultimately resulting in deteriorated visual outcomes. Thus, our review underscores the urgent need for the development of effective emergency plans tailored specifically to diabetic retinopathy, particularly in Puerto Rico, where diabetes prevalence and its complications are notably higher. Such plans should not only incorporate established emergency measures but also harness emerging technological advances in the field of ophthalmology to ensure optimal preparedness for future pandemics and natural disasters.

The application of artificial intelligence in glaucoma diagnosis and prediction

Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.

Evaluating the Efficacy of Teleophthalmology in Delivering Ophthalmic Care to Underserved Populations: A Literature Review

Technological advancement has brought commendable changes in medicine, advancing diagnosis, treatment, and interventions. Telemedicine has been adopted by various subspecialties including ophthalmology. Over the years, teleophthalmology has been implemented in various countries, and continuous progress is being made in this area. In underserved populations, due to socioeconomic factors, there is little or no access to healthcare facilities, and people are at higher risk of eye diseases and vision impairment. Transportation is the major hurdle for these people in obtaining access to eye care in the main hospitals. There is a dire need for accessible eye care for such populations, and teleophthalmology is the ray of hope for providing eye care facilities to underserved people. Numerous studies have reported the advantages of teleophthalmology for rural populations such as being cost-effective, timesaving, reliable, efficient, and satisfactory for patients. Although it is being practiced in urban populations, for rural populations, its benefits amplify. However, there are certain obstacles as well, such as the cost of equipment, lack of steady electricity and internet supply in rural areas, and the attitude of people in certain regions toward acceptance of teleophthalmology. In this review, we have discussed in detail eye health in rural populations, teleophthalmology, and its effectiveness in rural populations of different countries.

Impact of the COVID-19 Pandemic on the Metabolic Control of Diabetic Patients in Diabetic Retinopathy and Its Screening

(1) Background: Diabetic retinopathy (DR) is a complication of diabetes mellitus (DM), screening programs of which have been affected by the COVID-19 pandemic. The aim of the present study was to determine the impact of the COVID-19 pandemic on the screening of diabetes patients in our healthcare area (HCA). (2) Methods: We carried out a retrospective study of patients with DM who had attended the DR screening program between January 2015 and June 2022. We studied attendance, DM metabolic control and DR incidence. (3) Results: Screening for DR decreased in the first few months of the pandemic. The incidence of mild and moderate DR remained stable throughout the study, and we observed little increase in severe DR, proliferative DR and neovascular glaucoma during 2021 and 2022. (4) Conclusions: The current study shows that during the COVID-19 pandemic, screening program attendance decreased during the year 2020, which then recovered in 2021. Regarding the most severe forms of DR, a slight increase in cases was observed, beginning in the year 2021. Nevertheless, we aimed to improve the telemedicine systems, since the conditions of a significant proportion of the studied patients worsened during the pandemic; these patients are likely those who were already poorly monitored.

Cost-Utility Analysis of Screening for Diabetic Retinopathy in China

Background. Diabetic retinopathy (DR) has been primarily indicated to cause vision impairment and blindness, while no studies have focused on the cost-utility of telemedicine-based and community screening programs for DR in China, especially in rural and urban areas, respectively.Methods. We developed a Markov model to calculate the cost-utility of screening programs for DR in DM patients in rural and urban settings from the societal perspective. The incremental cost-utility ratio (ICUR) was calculated for the assessment.Results. In the rural setting, the community screening program obtained 1 QALY with a cost of $4179 (95% CI 3859 to 5343), and the telemedicine screening program had an ICUR of $2323 (95% CI 1023 to 3903) compared with no screening, both of which satisfied the criterion of a significantly cost-effective health intervention. Likewise, community screening programs in urban areas generated an ICUR of $3812 (95% CI 2906 to 4167) per QALY gained, with telemedicine screening at an ICUR of $2437 (95% CI 1242 to 3520) compared with no screening, and both were also cost-effective. By further comparison, compared to community screening programs, telemedicine screening yielded an ICUR of 1212 (95% CI 896 to 1590) per incremental QALY gained in rural setting and 1141 (95% CI 859 to 1403) in urban setting, which both meet the criterion for a significantly cost-effective health intervention.Conclusions. Both telemedicine and community screening for DR in rural and urban settings were cost-effective in China, and telemedicine screening programs were more cost-effective.