Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review

Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera

BACKGROUND

Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with diabetes is increasing and telemedicine, mobile handheld devices and automated solutions may alleviate the burden for healthcare. We compared the performance of 21 artificial intelligence (AI) algorithms for referable DR screening in datasets taken by handheld Optomed Aurora fundus camera in a real-world setting.

PATIENTS AND METHODS

Prospective study of 156 patients (312 eyes) attending DR screening and follow-up. Both papilla- and macula-centred 50° fundus images were taken from each eye. DR was graded by experienced ophthalmologists and 21 AI algorithms.

RESULTS

Most eyes, 183 out of 312 (58.7%), had no DR and mild NPDR was noted in 21 (6.7%) of the eyes. Moderate NPDR was detected in 66 (21.2%) of the eyes, severe NPDR in 1 (0.3%), and PDR in 41 (13.1%) composing a group of 34.6% of eyes with referable DR. The AI algorithms achieved a mean agreement of 79.4% for referable DR, but the results varied from 49.4% to 92.3%. The mean sensitivity for referable DR was 77.5% (95% CI 69.1-85.8) and specificity 80.6% (95% CI 72.1-89.2). The rate for images ungradable by AI varied from 0% to 28.2% (mean 1.9%). Nineteen out of 21 (90.5%) AI algorithms resulted in grading for DR at least in 98% of the images.

CONCLUSIONS

Fundus images captured with Optomed Aurora were suitable for DR screening. The performance of the AI algorithms varied considerably emphasizing the need for external validation of screening algorithms in real-world settings before their clinical application.

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.

"Spaceflight-to-Eye Clinic": Terrestrial advances in ophthalmic healthcare delivery from space-based innovations

The phrase "Bench-to-Bedside" is a well-known phrase in medicine, highlighting scientific discoveries that directly translate to impacting patient care. Key examples of translational research include identification of key molecular targets in diseases and development of diagnostic laboratory tests for earlier disease detection. Bridging these scientific advances to the bedside/clinic has played a meaningful impact in numerous patient lives. The spaceflight environment poses a unique opportunity to also make this impact; the nature of harsh extraterrestrial conditions and medically austere and remote environments push for cutting-edge technology innovation. Many of these novel technologies built for the spaceflight environment also have numerous benefits for human health on Earth. In this manuscript, we focus on "Spaceflight-to-Eye Clinic" and discuss technologies built for the spaceflight environment that eventually helped to optimize ophthalmic health on Earth (e.g., LADAR for satellite docking now utilized in eye-tracking technology for LASIK). We also discuss current technology research for spaceflight associated neuro-ocular syndrome (SANS) that may also be applied to terrestrial ophthalmic health. Ultimately, various advances made to enable to the future of space exploration have also advanced the ophthalmic health of individuals on Earth.

Benefit-Risk Assessment of ChatGPT Applications in the Field of Diabetes and Metabolic Illnesses: A Qualitative Study

Background

The use of ChatGPT and artificial intelligence (AI) in the management of metabolic and endocrine disorders presents both significant opportunities and notable risks.

Objectives

To investigate the benefits and risks associated with the application of ChatGPT in managing diabetes and metabolic illnesses by exploring the perspectives of endocrinologists and diabetologists.

Methods and materials

The study employed a qualitative research approach. A semi-structured in-depth interview guide was developed. A convenience sample of 25 endocrinologists and diabetologists was enrolled and interviewed. All interviews were audiotaped and verbatim transcribed; then, thematic analysis was used to determine the themes in the data.

Results

The findings of the thematic analysis resulted in 19 codes and 9 major themes regarding the benefits of implementing AI and ChatGPT in managing diabetes and metabolic illnesses. Moreover, the extracted risks of implementing AI and ChatGPT in managing diabetes and metabolic illnesses were categorized into 7 themes and 14 codes. The benefits of heightened diagnostic precision, tailored treatment, and efficient resource utilization have potential to improve patient results. Concurrently, the identification of potential challenges, such as data security concerns and the need for AI that can be explained, enables stakeholders to proactively tackle these issues.

Conclusions

Regulatory frameworks must evolve to keep pace with the rapid adoption of AI in healthcare. Sustained attention to ethical considerations, including obtaining patient consent, safeguarding data privacy, ensuring accountability, and promoting fairness, remains critical. Despite its potential impact on the human aspect of healthcare, AI will remain an integral component of patient-centered care. Striking a balance between AI-assisted decision-making and human expertise is essential to uphold trust and provide comprehensive patient care.

Present and future screening programs for diabetic retinopathy: a narrative review

Diabetes is a prevalent global concern, with an estimated 12% of the global adult population affected by 2045. Diabetic retinopathy (DR), a sight-threatening complication, has spurred diverse screening approaches worldwide due to advances in DR knowledge, rapid technological developments in retinal imaging and variations in healthcare resources.Many high income countries have fully implemented or are on the verge of completing a national Diabetic Eye Screening Programme (DESP). Although there have been some improvements in DR screening in Africa, Asia, and American countries further progress is needed. In low-income countries, only one out of 29, partially implemented a DESP, while 21 out of 50 lower-middle-income countries have started the DR policy cycle. Among upper-middle-income countries, a third of 59 nations have advanced in DR agenda-setting, with five having a comprehensive national DESP and 11 in the early stages of implementation.Many nations use 2-4 fields fundus images, proven effective with 80-98% sensitivity and 86-100% specificity compared to the traditional seven-field evaluation for DR. A cell phone based screening with a hand held retinal camera presents a potential low-cost alternative as imaging device. While this method in low-resource settings may not entirely match the sensitivity and specificity of seven-field stereoscopic photography, positive outcomes are observed.Individualized DR screening intervals are the standard in many high-resource nations. In countries that lacks a national DESP and resources, screening are more sporadic, i.e. screening intervals are not evidence-based and often less frequently, which can lead to late recognition of treatment required DR.The rising global prevalence of DR poses an economic challenge to nationwide screening programs AI-algorithms have showed high sensitivity and specificity for detection of DR and could provide a promising solution for the future screening burden.In summary, this narrative review enlightens on the epidemiology of DR and the necessity for effective DR screening programs. Worldwide evolution in existing approaches for DR screening has showed promising results but has also revealed limitations. Technological advancements, such as handheld imaging devices, tele ophthalmology and artificial intelligence enhance cost-effectiveness, but also the accessibility of DR screening in countries with low resources or where distance to or a shortage of ophthalmologists exists.

[Global ophthalmology : Update]

The aim of global ophthalmology is to maximize vision, ocular health and functional ability, thereby contributing to overall health and well-being, social inclusion and quality of life of every individual worldwide. Currently, an estimated 1.1 billion people live with visual impairment, 90% of which can be prevented or cured through largely cost-effective interventions. At the same time, 90% of people affected live in regions with insufficient eye health coverage. This challenge drove the World Health Organization (WHO) and a group of nongovernmental organizations to launch "VISION 2020: the Right to Sight", a global campaign which recently concluded after 20 years. The achievements, challenges and lessons learned were identified and incorporated into the current campaign "2030 IN SIGHT".

Artificial intelligence-supported diabetic retinopathy screening in Tanzania: rationale and design of a randomised controlled trial

INTRODUCTION

Globally, diabetic retinopathy (DR) is a major cause of blindness. Sub-Saharan Africa is projected to see the largest proportionate increase in the number of people living with diabetes over the next two decades. Screening for DR is recommended to prevent sight loss; however, in many low and middle-income countries, because of a lack of specialist eye care staff, current screening services for DR are not optimal. The use of artificial intelligence (AI) for DR screening, which automates the grading of retinal photographs and provides a point-of-screening result, offers an innovative potential solution to improve DR screening in Tanzania.

METHODS AND ANALYSIS

We will test the hypothesis that AI-supported DR screening increases the proportion of persons with true referable DR who attend the central ophthalmology clinic following referral after screening in a single-masked, parallel group, individually randomised controlled trial. Participants (2364) will be randomised (1:1 ratio) to either AI-supported or the standard of care DR screening pathway. Participants allocated to the AI-supported screening pathway will receive their result followed by point-of-screening counselling immediately after retinal image capture. Participants in the standard of care arm will receive their result and counselling by phone once the retinal images have been graded in the usual way (typically after 2-4 weeks). The primary outcome is the proportion of persons with true referable DR attending the central ophthalmology clinic within 8 weeks of screening. Secondary outcomes, by trial arm, include the proportion of persons attending the central ophthalmology clinic out of all those referred, sensitivity and specificity, number of false positive referrals, acceptability and fidelity of AI-supported screening.

ETHICS AND DISSEMINATION

The London School of Hygiene & Tropical Medicine, Kilimanjaro Christian Medical Centre and Tanzanian National Institute of Medical Research ethics committees have approved the trial. The results will be submitted to peer-reviewed journals for publication.

TRIAL REGISTRATION NUMBER

ISRCTN18317152.

Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program

Purpose

We examine the rate of and reasons for follow-up in an Artificial Intelligence (AI)-based workflow for diabetic retinopathy (DR) screening relative to two human-based workflows.

Patients and Methods

A DR screening program initiated September 2019 between one institution and its affiliated primary care and endocrinology clinics screened 2243 adult patients with type 1 or 2 diabetes without a diagnosis of DR in the previous year in the San Francisco Bay Area. For patients who screened positive for more-than-mild-DR (MTMDR), rates of follow-up were calculated under a store-and-forward human-based DR workflow ("Human Workflow"), an AI-based workflow involving IDx-DR ("AI Workflow"), and a two-step hybrid workflow ("AI-Human Hybrid Workflow"). The AI Workflow provided results within 48 hours, whereas the other workflows took up to 7 days. Patients were surveyed by phone about follow-up decisions.

Results

Under the AI Workflow, 279 patients screened positive for MTMDR. Of these, 69.2% followed up with an ophthalmologist within 90 days. Altogether 70.5% (N=48) of patients who followed up chose their location based on primary care referral. Among the subset of patients that were seen in person at the university eye institute under the Human Workflow and AI-Human Hybrid Workflow, 12.0% (N=14/117) and 11.7% (N=12/103) of patients with a referrable screening result followed up compared to 35.5% of patients under the AI Workflow (N=99/279; χ2df=2 = 36.70, p < 0.00000001).

Conclusion

Ophthalmology follow-up after a positive DR screening result is approximately three-fold higher under the AI Workflow than either the Human Workflow or AI-Human Hybrid Workflow. Improved follow-up behavior may be due to the decreased time to screening result.