Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

Implementation of anterior segment ophthalmic telemedicine

PURPOSE OF REVIEW

The growing push to integrate telemedicine into ophthalmic practices requires physicians to have a thorough understanding of ophthalmic telemedicine's applications, limitations, and recent advances in order to provide well tolerated and appropriate clinical care. This review aims to provide an overview of recent advancements in the use of ophthalmic telemedicine for anterior segment eye examinations.

RECENT FINDINGS

Virtual care for anterior segment evaluation relies on appropriate technology, novel workflows, and appropriate clinical case selection. Recent advances, particularly in the wake of the COVID-19 pandemic, have highlighted the utility of home-based assessments for visual acuity, external evaluation, tonometry, and refraction. Additionally, innovative workflows incorporating office-based testing into virtual care, termed 'hybrid telemedicine', enable high-quality ophthalmic testing to inform clinical decision-making.

SUMMARY

Novel digital tools and workflows enable high-quality anterior segment evaluation and management for select ophthalmic concerns. This review highlights the clinical tools and workflows necessary to enable anterior segment telehealth.

Rate and Predictors of Misclassification of Active Diabetic Macular Edema as Detected by an Automated Retinal Image Analysis System

INTRODUCTION

The aim of this work is to estimate the sensitivity, specificity, and misclassification rate of an automated retinal image analysis system (ARIAS) in diagnosing active diabetic macular edema (DME) and to identify factors associated with true and false positives.

METHODS

We conducted a cross-sectional study of prospectively enrolled patients with diabetes mellitus (DM) referred to a tertiary medical retina center for screening or management of DME. All patients underwent two-field fundus photography (macula- and disc-centered) with a true-color confocal camera; images were processed by EyeArt V.2.1.0 (Woodland Hills, CA, USA). Active DME was defined as the presence of intraretinal or subretinal fluid on spectral-domain optical coherence tomography (SD-OCT). Sensitivity and specificity and their 95% confidence intervals (CIs) were calculated. Variables associated with true (i.e., DME labeled as present by ARIAS + fluid on SD-OCT) and false positives (i.e., DME labeled as present by ARIAS + no fluid on SD-OCT) of active DME were explored.

RESULTS

A total of 298 eyes were included; 92 eyes (31%) had active DME. ARIAS sensitivity and specificity were 82.61% (95% CI 72.37-89.60) and 84.47% (95% CI 78.34-89.10). The misclassification rate was 16%. Factors associated with true positives included younger age (p = 0.01), shorter DM duration (p = 0.006), presence of hard exudates (p = 0.005), and microaneurysms (p = 0.002). Factors associated with false positives included longer DM duration (p = 0.01), worse diabetic retinopathy severity (p = 0.008), history of inactivated DME (p < 0.001), and presence of hard exudates (p < 0.001), microaneurysms (p < 0.001), or epiretinal membrane (p = 0.06).

CONCLUSIONS

The sensitivity of ARIAS was diminished in older patients and those without DME-related fundus lesions, while the specificity was reduced in cases with a history of inactivated DME. ARIAS performed well in screening for naïve DME but is not effective in surveillance inactivated DME.

Artificial intelligence in medical science: a review

Artificial intelligence (AI) is a technique to make intelligent machines, mainly by using smart computer programs. It is based on a statistical analysis of data or machine learning. Using machine learning, software algorithms are designed according to the desired application. These techniques are found to have the potential for advancement in the medical field by generating new and significant perceptions from the data generated using various types of healthcare tests. Artificial intelligence (AI) in medicine is of two types: virtual and physical. The virtual part decides the treatment using electronic health record systems using various sensors whereas the physical part assists robots to perform surgeries, implants, replacement of various organs, elderly care, etc. Using AI, a machine can examine various kinds of health care test reports in one go which could save the time, money, and increase the chances of the patient to be treated without any hassles. At present, artificial intelligence (AI) is used while deciding the treatment, and medications using various tools which could analyze X-rays, CT scans, MRIs, and any other data. During the COVID pandemic, there was a huge/massive demand for AI-supported technologies and many of those were created during that time. This study is focused on various applications of AI in healthcare.

Processing of clinical notes for efficient diagnosis with feedback attention-based BiLSTM

Predicting a patient's future health state through the analysis of their clinical records is an emerging area in the field of intelligent medicine. It has the potential to assist healthcare professionals in prescribing treatments safely, making more accurate diagnoses, and improving patient care. However, clinical notes have been underutilized due to their complexity, high dimensionality, and sparsity. Nevertheless, these clinical records hold significant promise for enhancing clinical decision. To tackle these problems, a novel feedback attention-based bidirectional long short-term memory (FABiLSTM) model has been proposed for more effective diagnosis using clinical records. This model incorporates PubMedBERT for filtering irrelevant information, enhances global vector word embeddings for numerical representations and K-means clustering, and performs to explore term frequency and inverse document frequency intricacies. The proposed approach excels in capturing information, aiding accurate disease prediction. The predictive capability is further enhanced with the help of a billiards-inspired optimization algorithm. The effectiveness of the FABiLSTM method has been assessed with the MIMIC-III dataset, yielding impressive results in accuracy, precision, F1 score, and recall score of 98.52%, 98%, 98.2%, and 98.2% individually. These results reveal ways in which the proposed technique excels in comparison with current practices.

Detecting the corneal neovascularisation area using artificial intelligence

AIMS

To create and assess the performance of an artificial intelligence-based image analysis tool for the measurement and quantification of the corneal neovascularisation (CoNV) area.

METHODS

Slit lamp images of patients with CoNV were exported from the electronic medical records and included in the study. An experienced ophthalmologist made manual annotations of the CoNV areas, which were then used to create, train and evaluate an automated image analysis tool that uses deep learning to segment and detect CoNV areas. A pretrained neural network (U-Net) was used and fine-tuned on the annotated images. Sixfold cross-validation was used to evaluate the performance of the algorithm on each subset of 20 images. The main metric for our evaluation was intersection over union (IoU).

RESULTS

The slit lamp images of 120 eyes of 120 patients with CoNV were included in the analysis. Detections of the total corneal area achieved IoU between 90.0% and 95.5% in each fold and those of the non-vascularised area achieved IoU between 76.6% and 82.2%. The specificity for the detection was between 96.4% and 98.6% for the total corneal area and 96.6% and 98.0% for the non-vascularised area.

CONCLUSION

The proposed algorithm showed a high accuracy compared with the measurement made by an ophthalmologist. The study suggests that an automated tool using artificial intelligence may be used for the calculation of the CoNV area from the slit-lamp images of patients with CoNV.

Evaluating the precision of an online visual acuity test tool

OBJECTIVE

The aim of this study was to assess the precision of a web-based tool in measuring visual acuity (VA) in ophthalmic patients, comparing it to the traditional in-clinic evaluation using a Snellen chart, considered the gold standard.

METHODS

We conducted a prospective and in-clinic validation comparing the Eyecare Visual Acuity Test® to the standard Snellen chart, with patients undergoing both tests sequentially. Patients wore their standard spectacles as needed for both tests. Inclusion criteria involved individuals above 18 years with VA equal to or better than +1 logMar (20/200) in each eye. VA measurements were converted from Snellen to logMAR, and statistical analyses included Bland-Altman and descriptive statistics.

RESULTS

The study, encompassing 322 patients and 644 eyes, compared Eyecare Visual Acuity Test® to conventional methods, revealing a statistically insignificant mean difference (0.01 logMAR, P = 0.1517). Bland-Altman analysis showed a narrow 95% limit of agreement (0.22 to -0.23 logMAR), indicating concordance, supported by a significant Pearson correlation (r = 0.61, P < 0.001) between the two assessments.

CONCLUSION

The Eyecare Visual Acuity Test® demonstrates accuracy and reliability, with the potential to facilitate home monitoring, triage, and remote consultation. In future research, it is important to validate the Eyecare Visual Acuity Test® accuracy across varied age cohorts, including pediatric and geriatric populations, as well as among individuals presenting with specific comorbidities like cataract, uveitis, keratoconus, age-related macular disease, and amblyopia.

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.

Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy

Purpose: To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography by two double masked retina specialists. Methods: Two-hundred one patients (mean age 65 ± 13 years) with type 1 diabetes mellitus or type 2 diabetes mellitus were included. All patients were undergoing a retinography and spectral domain optical coherence tomography (SD-OCT, DRI 3D OCT-2000, Topcon) of the macula. The retinal photographs were graded using two validated AI DR screening software (Eye Art TM and IDx-DR) designed to identify more than mild DR. Results: Retinal images of 201 patients were graded. DR (more than mild DR) was detected by the ophthalmologists in 38 (18.9%) patients and by the AI-algorithms in 36 patients (with 30 eyes diagnosed by both algorithms). Ungradable patients by the AI software were 13 (6.5%) and 16 (8%) for the Eye Art and IDx-DR, respectively. Both AI software strategies showed a high sensitivity and specificity for detecting any more than mild DR without showing any statistically significant difference between them. Conclusions: The comparison between the diagnosis provided by artificial intelligence based automated software and the reference clinical diagnosis showed that they can work at a level of sensitivity that is similar to that achieved by experts.

Service Quality and Residents' Preferences for Facilitated Self-Service Fundus Disease Screening: Cross-Sectional Study

BACKGROUND

Fundus photography is the most important examination in eye disease screening. A facilitated self-service eye screening pattern based on the fully automatic fundus camera was developed in 2022 in Shanghai, China; it may help solve the problem of insufficient human resources in primary health care institutions. However, the service quality and residents' preference for this new pattern are unclear.

OBJECTIVE

This study aimed to compare the service quality and residents' preferences between facilitated self-service eye screening and traditional manual screening and to explore the relationships between the screening service's quality and residents' preferences.

METHODS

We conducted a cross-sectional study in Shanghai, China. Residents who underwent facilitated self-service fundus disease screening at one of the screening sites were assigned to the exposure group; those who were screened with a traditional fundus camera operated by an optometrist at an adjacent site comprised the control group. The primary outcome was the screening service quality, including effectiveness (image quality and screening efficiency), physiological discomfort, safety, convenience, and trustworthiness. The secondary outcome was the participants' preferences. Differences in service quality and the participants' preferences between the 2 groups were compared using chi-square tests separately. Subgroup analyses for exploring the relationships between the screening service's quality and residents' preference were conducted using generalized logit models.

RESULTS

A total of 358 residents enrolled; among them, 176 (49.16%) were included in the exposure group and the remaining 182 (50.84%) in the control group. Residents' basic characteristics were balanced between the 2 groups. There was no significant difference in service quality between the 2 groups (image quality pass rate: P=.79; average screening time: P=.57; no physiological discomfort rate: P=.92; safety rate: P=.78; convenience rate: P=.95; trustworthiness rate: P=.20). However, the proportion of participants who were willing to use the same technology for their next screening was significantly lower in the exposure group than in the control group (P<.001). Subgroup analyses suggest that distrust in the facilitated self-service eye screening might increase the probability of refusal to undergo screening (P=.02).

CONCLUSIONS

This study confirms that the facilitated self-service fundus disease screening pattern could achieve good service quality. However, it was difficult to reverse residents' preferences for manual screening in a short period, especially when the original manual service was already excellent. Therefore, the digital transformation of health care must be cautious. We suggest that attention be paid to the residents' individual needs. More efficient man-machine collaboration and personalized health management solutions based on large language models are both needed.

Advancements and turning point of artificial intelligence in ophthalmology: A comprehensive analysis of research trends and collaborative networks

Artificial intelligence (AI) has emerged as a transformative force with great potential in various fields, including healthcare. In recent years, AI has garnered significant attention due to its potential to revolutionise ophthalmology, leading to advancements in patient care such as disease detection, diagnosis, treatment and monitoring of disease progression. This study presents a comprehensive analysis of the research trends and collaborative networks at the intersection of AI and ophthalmology. In this study, we conducted an extensive search of the Web of Science Core Collection to identify articles related to 'artificial intelligence' in ophthalmology published from 1968 to 2023. We performed co-occurrence keywords and co-authorship network analyses using VOSviewer software to explore the relationships between keywords and country collaboration. We found a remarkable surge in articles applying AI in ophthalmology after 2017, marking a turning point in the integration of AI within the medical field. The primary application of AI shifted towards the diagnosis of ocular disease, which was particularly evident through keywords such as glaucoma, diabetic retinopathy and age-related macular degeneration. Analysis of the collaboration networks of countries revealed a global expansion of ophthalmology-related AI research. This study provides valuable insights into the evolving landscape of AI integration in ophthalmology, indicating its growing potential for enhancing disease detection, diagnosis, treatment planning and monitoring of disease progression. In order to translate AI technologies into clinical practice effectively, it is imperative to comprehend the evolving research trends and advancements at the intersection of AI and ophthalmology.

Multinational External Validation of Autonomous Retinopathy of Prematurity Screening

Importance

Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening.

Objective

To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP.

Design, Setting, and Participants

This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023.

Exposures

An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine.

Main Outcomes and Measures

The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels.

Results

The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis.

Conclusions and Relevance

Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.

Achieving net-zero in the dry eye disease care pathway

Climate change is a threat to human health and wellbeing across the world. In recent years, there has been a surge in awareness of this crisis, leading to many countries and organisations setting "net-zero" targets. This entails minimising carbon emissions and neutralising remaining emissions by removing carbon from the atmosphere. At the 2022 United Nations Climate Change Conference (COP27), commitments to transition away from fossil fuels and augment climate targets were underwhelming. It is therefore imperative for public and private sector organisations to demonstrate successful implementation of net-zero and set a precedent for the global political consensus. As a top 10 world employer, the United Kingdom National Health Service (NHS) has pledged to reach net-zero by 2045. The NHS has already taken positive steps forward, but its scale and complexity as a health system means stakeholders in each of its services must highlight the specifications for further progress. Dry eye disease is a chronic illness with an estimated global prevalence of 29.5% and an environmentally damaging care pathway. Moreover, environmental damage is a known aggravator of dry eye disease. Worldwide management of this illness generates copious amounts of non-recyclable waste, utilises inefficient supply chains and involves recurrent follow-up appointments and prescriptions. By mapping the dry eye disease care pathway to environmental impact, in this review we will highlight seven key areas in which reduced emissions and pollution could be targeted. Examining these approaches for improved environmental sustainability is critical in driving the transformation needed to preserve our health and wellbeing.

Providing capacity in glaucoma care using trained and accredited optometrists: A qualitative evaluation

INTRODUCTION

The role of optometrists in glaucoma within primary and secondary care has been well described. Whilst many studies examined safety and clinical effectiveness, there is a paucity of qualitative research evaluating enablers and barriers for optometrists delivering glaucoma care. The aims of this study are to investigate qualitatively, and from a multi-stakeholder perspective whether optometric glaucoma care is accepted as an effective alternative to traditional models and what contextual factors impact upon their success.

METHODS

Patients were recruited from clinics at Manchester Royal Eye Hospital and nationally via a Glaucoma UK registrant database. Optometrists, ophthalmologists, and other stakeholders involved in glaucoma services were recruited via direct contact and through an optometry educational event. Interviews and focus groups were recorded and transcribed anonymously, then analysed using the framework method and NVivo 12.

RESULTS

Interviews and focus groups were conducted with 38 participants including 14 optometrists and 6 ophthalmologists (from all 4 UK nations), and 15 patients and 3 commissioners/other stakeholders. Themes emerging related to: enablers and drivers; challenges and barriers; training; laser; professional practice; the role of other health professionals; commissioning; COVID-19; and patient experience.

CONCLUSION

Success in developing glaucoma services with optometrists and other health professionals is reliant on multi-stakeholder input, investment in technology and training, inter-professional respect and appropriate time and funding to set up and deliver services. The multi-stakeholder perspective affirms there is notable support for developing glaucoma services delivered by optometrists in primary and secondary care, with caveats around training, appropriate case selection and clinical responsibility.

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.

From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film

Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.

Comparing the impact of three-dimensional digital visualization technology versus traditional microscopy on microsurgeons in microsurgery: a prospective self-controlled study

BACKGROUND

Emerging three-dimensional digital visualization technology (DVT) provides more advantages than traditional microscopy in microsurgery; however, its impact on microsurgeons' visual and nervous systems and delicate microsurgery is still unclear, which hinders the wider implementation of DVT in digital visualization for microsurgery.

METHODS AND MATERIAL

Forty-two microsurgeons from the Zhongshan Ophthalmic Center were enrolled in this prospective self-controlled study. Each microsurgeon consecutively performed 30 min conjunctival sutures using a three-dimensional digital display and a microscope, respectively. Visual function, autonomic nerve activity, and subjective symptoms were evaluated before and immediately after the operation. Visual functions, including accommodative lag, accommodative amplitude, near point of convergence and contrast sensitivity function (CSF), were measured by an expert optometrist. Heart rate variability was recorded by a wearable device for monitoring autonomic nervous activity. Subjective symptoms were evaluated by questionnaires. Microsurgical performance was assessed by the video-based Objective Structured Assessment of Technical Skill (OSATS) tool.

RESULTS

Accommodative lag decreased from 0.63 (0.18) diopters (D) to 0.55 (0.16) D ( P =0.014), area under the log contrast sensitivity function increased from 1.49 (0.15) to 1.52 (0.14) ( P =0.037), and heart rate variability decreased from 36.00 (13.54) milliseconds (ms) to 32.26 (12.35) ms ( P =0.004) after using the DVT, but the changes showed no differences compared to traditional microscopy ( P >0.05). No statistical significance was observed for global OSATS scores between the two rounds of operations [mean difference, 0.05 (95% CI: -1.17 to 1.08) points; P =0.95]. Subjective symptoms were quite mild after using both techniques.

CONCLUSIONS

The impact of DVT-based procedures on microsurgeons includes enhanced accommodation and sympathetic activity, but the changes and surgical performance are not significantly different from those of microscopy-based microsurgery. Our findings indicate that short-term use of DVT is reliable for microsurgery and the long-term effect of using DVT deserve more consideration.

Artificial Intelligence in Telemedicine: A Global Perspective Visualization Analysis

Background: The use of artificial intelligence (AI) in telemedicine has been a popular topic in academic research in recent years, resulting in a surge of literature publications. This study provides a scientific overview of AI in telemedicine through bibliometric and visualization analysis. Methods: The Web of Science Core Collection was used as the data source, and the search was conducted on June 1, 2023. A total of 2,860 articles and review studies published in English between 2010 and 2023 were included. This study analyzed general information on the field, trends in publication output, countries/regions, authors, journals, influential articles, keyword usage, and knowledge flows between disciplines. The Bibliometrix R package, VOSviewer, and CiteSpace were used for the analysis. Results: The rate of articles published on AI in telemedicine is increasing by ∼42.1% annually. The United States and China are the top two countries in terms of the number of articles published, accounting for 37.1% of the overall publication volume. In addition to AI and telemedicine, machine learning, digital health, and deep learning are the top three keywords in terms of frequency of occurrence. The keyword time trend graph shows that ChatGPT became one of the important keywords in 2023. The analysis of burst detection suggests that mobile health, based on mobile phones, may be a promising area for future research. Conclusions: This study systematically reviewed the development of AI in telemedicine and identified current research hotspots and future research directions. The results will provide impetus for the innovative development of this field.

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.

Manhattan Vision Screening and Follow-Up Study: (NYC-SIGHT)Tele-Retinal Image Findings and Importance of Photography

Purpose: To describe tele-retinal abnormality image findings from the Manhattan Vision Screening and Follow-up Study (NYC-SIGHT), which aims to investigate whether community-based eye health outreach strategies using telemedicine can improve visual outcomes among at-risk populations in Upper Manhattan. Methods: A 5-year prospective, cluster-randomized clinical trial was conducted. Eligible individuals aged 40 years and older were recruited from affordable housing developments and senior centers in New York City. Participants underwent on-site eye health screening (best-corrected visual acuity, intraocular pressure [IOP] measurements, and fundus photography). Fundus images were graded via telemedicine by a retina specialist. Multivariate logistic regression modeling was used to assess the factors associated with abnormal retinal findings requiring referral to ophthalmology. Results: Participants with a retinal abnormality on fundus photography (n = 157) were predominantly older adults, with a mean age of 68.4 ± 11.1 years, female (63.7%), African American (50.3%), and Hispanic (43.3%). A total of 32 participants in our study passed the vision and IOP screening but had an abnormal retinal image and ocular pathology that would have been missed without fundus photography. Individuals who self-identified as having preexisting glaucoma (odds ratio [OR] = 3.749, 95% confidence interval [CI] = 1.741-8.074, p = 0.0001) and had severe vision impairment (OR = 4.1034, 95% CI = 2.0740-8.1186, p = 0.000) at the screening had significantly higher odds of having an abnormal retinal image. Conclusion: This community-based study targeted populations at-risk for eye disease, improved access to eye care, detected a significant number of retinal image abnormalities requiring follow-up by using telemedicine, and provided evidence of the importance of fundus photography during eye health screenings. CTR number: NCT04271709.

Recent advances in the treatment and delivery system of diabetic retinopathy

Diabetic retinopathy (DR) is a highly tissue-specific neurovascular complication of type 1 and type 2 diabetes mellitus and is among the leading causes of blindness worldwide. Pathophysiological changes in DR encompass neurodegeneration, inflammation, and oxidative stress. Current treatments for DR, including anti-vascular endothelial growth factor, steroids, laser photocoagulation, and vitrectomy have limitations and adverse reactions, necessitating the exploration of novel treatment strategies. This review aims to summarize the current pathophysiology, therapeutic approaches, and available drug-delivery methods for treating DR, and discuss their respective development potentials. Recent research indicates the efficacy of novel receptor inhibitors and agonists, such as aldose reductase inhibitors, angiotensin-converting enzyme inhibitors, peroxisome proliferator-activated receptor alpha agonists, and novel drugs in delaying DR. Furthermore, with continuous advancements in nanotechnology, a new form of drug delivery has been developed that can address certain limitations of clinical drug therapy, such as low solubility and poor penetration. This review serves as a theoretical foundation for future research on DR treatment. While highlighting promising therapeutic targets, it underscores the need for continuous exploration to enhance our understanding of DR pathogenesis. The limitations of current treatments and the potential for future advancements emphasize the importance of ongoing research in this field.

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.

Application of AI on cholangiocarcinoma

Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.

The performance of a deep learning system in assisting junior ophthalmologists in diagnosing 13 major fundus diseases: a prospective multi-center clinical trial

Artificial intelligence (AI)-based diagnostic systems have been reported to improve fundus disease screening in previous studies. This multicenter prospective self-controlled clinical trial aims to evaluate the diagnostic performance of a deep learning system (DLS) in assisting junior ophthalmologists in detecting 13 major fundus diseases. A total of 1493 fundus images from 748 patients were prospectively collected from five tertiary hospitals in China. Nine junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the DLS. The diagnostic performance was evaluated among three groups: DLS-assisted junior ophthalmologist group (test group), junior ophthalmologist group (control group) and DLS group. The diagnostic consistency was 84.9% (95%CI, 83.0% ~ 86.9%), 72.9% (95%CI, 70.3% ~ 75.6%) and 85.5% (95%CI, 83.5% ~ 87.4%) in the test group, control group and DLS group, respectively. With the help of the proposed DLS, the diagnostic consistency of junior ophthalmologists improved by approximately 12% (95% CI, 9.1% ~ 14.9%) with statistical significance (P < 0.001). For the detection of 13 diseases, the test group achieved significant higher sensitivities (72.2% ~ 100.0%) and comparable specificities (90.8% ~ 98.7%) comparing with the control group (sensitivities, 50% ~ 100%; specificities 96.7 ~ 99.8%). The DLS group presented similar performance to the test group in the detection of any fundus abnormality (sensitivity, 95.7%; specificity, 87.2%) and each of the 13 diseases (sensitivity, 83.3% ~ 100.0%; specificity, 89.0 ~ 98.0%). The proposed DLS provided a novel approach for the automatic detection of 13 major fundus diseases with high diagnostic consistency and assisted to improve the performance of junior ophthalmologists, resulting especially in reducing the risk of missed diagnoses. ClinicalTrials.gov NCT04723160.

Challenges of artificial intelligence in medicine and dermatology

Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in health care outcomes. Addressing bias requires careful examination of the data used to train AI models and implementation of strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and health care providers. Ethical considerations arise when using AI in health care, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in health care. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing health care disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.

Residual toxins on aquatic animals in the Pacific areas: Current findings and potential health effects

The Pacific Ocean is among the five largest and deepest oceans in the world. The area of the Pacific Ocean covers about 28 % of the Earth's surface. This is the habitat of many marine species, and its diversity is recognized as a fundamental element of Pacific culture and heritage. The ecosystems of aquatic animals are highly affected by climate change and by other factors. Residual toxins on aquatic animals can be categorized into two types based on origin: toxins of marine origin and toxins associated with human activity. Residual toxins have emerged as a global concern in recent years due to their frequent presence in aquatic environments. Furthermore, residual toxins in organisms living in the marine environment in the Pacific Ocean region also seriously affect food safety, food security, and especially human health. In this review we discuss important issues about residual toxins on aquatic animals in the Pacific areas specifically about the types of toxins that exist in marine animals, their contamination pathways in the Asia, Pacific region and the potential health effects for humans, the application of information technology and artificial intelligence in residual toxins on aquatic animal.

Insights into artificial intelligence in myopia management: from a data perspective

Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.

Artificial intelligence in retinal imaging: current status and future prospects

INTRODUCTION

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

AREAS COVERED

This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature.

EXPERT OPINION

The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.

Diabetic Macular Edema Optical Coherence Tomography Biomarkers Detected with EfficientNetV2B1 and ConvNeXt

(1) Background: Diabetes mellitus (DM) is a growing challenge, both for patients and physicians, in order to control the impact on health and prevent complications. Millions of patients with diabetes require medical attention, which generates problems regarding the limited time for screening but also addressability difficulties for consultation and management. As a result, screening programs for vision-threatening complications due to DM have to be more efficient in the future in order to cope with such a great healthcare burden. Diabetic macular edema (DME) is a severe complication of DM that can be prevented if it is timely screened with the help of optical coherence tomography (OCT) devices. Newly developing state-of-the-art artificial intelligence (AI) algorithms can assist physicians in analyzing large datasets and flag potential risks. By using AI algorithms in order to process OCT images of large populations, the screening capacity and speed can be increased so that patients can be timely treated. This quick response gives the physicians a chance to intervene and prevent disability. (2) Methods: This study evaluated ConvNeXt and EfficientNet architectures in correctly identifying DME patterns on real-life OCT images for screening purposes. (3) Results: Firstly, we obtained models that differentiate between diabetic retinopathy (DR) and healthy scans with an accuracy of 0.98. Secondly, we obtained a model that can indicate the presence of edema, detachment of the subfoveolar neurosensory retina, and hyperreflective foci (HF) without using pixel level annotation. Lastly, we analyzed the extent to which the pretrained weights on natural images "understand" OCT scans. (4) Conclusions: Pretrained networks such as ConvNeXt or EfficientNet correctly identify features relevant to the differentiation between healthy retinas and DR, even though they were pretrained on natural images. Another important aspect of our research is that the differentiation between biomarkers and their localization can be obtained even without pixel-level annotation. The "three biomarkers model" is able to identify obvious subfoveal neurosensory detachments, retinal edema, and hyperreflective foci, as well as very small subfoveal detachments. In conclusion, our study points out the possible usefulness of AI-assisted diagnosis of DME for lowering healthcare costs, increasing the quality of life of patients with diabetes, and reducing the waiting time until an appropriate ophthalmological consultation and treatment can be performed.

A deep network DeepOpacityNet for detection of cataracts from color fundus photographs

BACKGROUND

Cataract diagnosis typically requires in-person evaluation by an ophthalmologist. However, color fundus photography (CFP) is widely performed outside ophthalmology clinics, which could be exploited to increase the accessibility of cataract screening by automated detection.

METHODS

DeepOpacityNet was developed to detect cataracts from CFP and highlight the most relevant CFP features associated with cataracts. We used 17,514 CFPs from 2573 AREDS2 participants curated from the Age-Related Eye Diseases Study 2 (AREDS2) dataset, of which 8681 CFPs were labeled with cataracts. The ground truth labels were transferred from slit-lamp examination of nuclear cataracts and reading center grading of anterior segment photographs for cortical and posterior subcapsular cataracts. DeepOpacityNet was internally validated on an independent test set (20%), compared to three ophthalmologists on a subset of the test set (100 CFPs), externally validated on three datasets obtained from the Singapore Epidemiology of Eye Diseases study (SEED), and visualized to highlight important features.

RESULTS

Internally, DeepOpacityNet achieved a superior accuracy of 0.66 (95% confidence interval (CI): 0.64-0.68) and an area under the curve (AUC) of 0.72 (95% CI: 0.70-0.74), compared to that of other state-of-the-art methods. DeepOpacityNet achieved an accuracy of 0.75, compared to an accuracy of 0.67 for the ophthalmologist with the highest performance. Externally, DeepOpacityNet achieved AUC scores of 0.86, 0.88, and 0.89 on SEED datasets, demonstrating the generalizability of our proposed method. Visualizations show that the visibility of blood vessels could be characteristic of cataract absence while blurred regions could be characteristic of cataract presence.

CONCLUSIONS

DeepOpacityNet could detect cataracts from CFPs in AREDS2 with performance superior to that of ophthalmologists and generate interpretable results. The code and models are available at https://github.com/ncbi/DeepOpacityNet ( https://doi.org/10.5281/zenodo.10127002 ).

Revolutionizing ocular cancer management: a narrative review on exploring the potential role of ChatGPT

This paper pioneers the exploration of ocular cancer, and its management with the help of Artificial Intelligence (AI) technology. Existing literature presents a significant increase in new eye cancer cases in 2023, experiencing a higher incidence rate. Extensive research was conducted using online databases such as PubMed, ACM Digital Library, ScienceDirect, and Springer. To conduct this review, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines are used. Of the collected 62 studies, only 20 documents met the inclusion criteria. The review study identifies seven ocular cancer types. Important challenges associated with ocular cancer are highlighted, including limited awareness about eye cancer, restricted healthcare access, financial barriers, and insufficient infrastructure support. Financial barriers is one of the widely examined ocular cancer challenges in the literature. The potential role and limitations of ChatGPT are discussed, emphasizing its usefulness in providing general information to physicians, noting its inability to deliver up-to-date information. The paper concludes by presenting the potential future applications of ChatGPT to advance research on ocular cancer globally.

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.

Teleophthalmology and retina: a review of current tools, pathways and services

Telemedicine, the use of telecommunication and information technology to deliver healthcare remotely, has evolved beyond recognition since its inception in the 1970s. Advances in telecommunication infrastructure, the advent of the Internet, exponential growth in computing power and associated computer-aided diagnosis, and medical imaging developments have created an environment where telemedicine is more accessible and capable than ever before, particularly in the field of ophthalmology. Ever-increasing global demand for ophthalmic services due to population growth and ageing together with insufficient supply of ophthalmologists requires new models of healthcare provision integrating telemedicine to meet present day challenges, with the recent COVID-19 pandemic providing the catalyst for the widespread adoption and acceptance of teleophthalmology. In this review we discuss the history, present and future application of telemedicine within the field of ophthalmology, and specifically retinal disease. We consider the strengths and limitations of teleophthalmology, its role in screening, community and hospital management of retinal disease, patient and clinician attitudes, and barriers to its adoption.

Personalized Care in Eye Health: Exploring Opportunities, Challenges, and the Road Ahead for Chatbots

In modern eye care, the adoption of ophthalmology chatbots stands out as a pivotal technological progression. These digital assistants present numerous benefits, such as better access to vital information, heightened patient interaction, and streamlined triaging. Recent evaluations have highlighted their performance in both the triage of ophthalmology conditions and ophthalmology knowledge assessment, underscoring their potential and areas for improvement. However, assimilating these chatbots into the prevailing healthcare infrastructures brings challenges. These encompass ethical dilemmas, legal compliance, seamless integration with electronic health records (EHR), and fostering effective dialogue with medical professionals. Addressing these challenges necessitates the creation of bespoke standards and protocols for ophthalmology chatbots. The horizon for these chatbots is illuminated by advancements and anticipated innovations, poised to redefine the delivery of eye care. The synergy of artificial intelligence (AI) and machine learning (ML) with chatbots amplifies their diagnostic prowess. Additionally, their capability to adapt linguistically and culturally ensures they can cater to a global patient demographic. In this article, we explore in detail the utilization of chatbots in ophthalmology, examining their accuracy, reliability, data protection, security, transparency, potential algorithmic biases, and ethical considerations. We provide a comprehensive review of their roles in the triage of ophthalmology conditions and knowledge assessment, emphasizing their significance and future potential in the field.

Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis

BACKGROUND/OBJECTIVE

Pathologic myopia (PM) is a major cause of severe visual impairment and blindness, and current applications of artificial intelligence (AI) have covered the diagnosis and classification of PM. This meta-analysis and systematic review aimed to evaluate the overall performance of AI-based models in detecting PM and related complications.

METHODS

We searched PubMed, Scopus, Embase, Web of Science and IEEE Xplore for eligible studies before Dec 20, 2022. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We calculated the pooled sensitivity (SEN), specificity (SPE) and the summary area under the curve (AUC) using a random effects model, to evaluate the performance of AI in the detection of PM based on fundus or optical coherence tomography (OCT) images.

RESULTS

22 studies were included in the systematic review, and 14 of them were included in the quantitative analysis. Of all included studies, SEN and SPE ranged from 80.0% to 98.7% and from 79.5% to 100.0% for PM detection, respectively. For the detection of PM, the summary AUC was 0.99 (95% confidence interval (CI) 0.97 to 0.99), and the pooled SEN and SPE were 0.95 (95% CI 0.92 to 0.96) and 0.97 (95% CI: 0.94 to 0.98), respectively. For the detection of PM-related choroid neovascularization (CNV), the summary AUC was 0.99 (95% CI: 0.97 to 0.99).

CONCLUSION

Our review demonstrated the excellent performance of current AI algorithms in detecting PM and related complications based on fundus and OCT images.

Bridging the Camera Domain Gap With Image-to-Image Translation Improves Glaucoma Diagnosis

Purpose

The purpose of this study was to improve the automated diagnosis of glaucomatous optic neuropathy (GON), we propose a generative adversarial network (GAN) model that translates Optain images to Topcon images.

Methods

We trained the GAN model on 725 paired images from Topcon and Optain cameras and externally validated it using an additional 843 paired images collected from the Aravind Eye Hospital in India. An optic disc segmentation model was used to assess the disparities in disc parameters across cameras. The performance of the translated images was evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), 95% limits of agreement (LOA), Pearson's correlations, and Cohen's Kappa coefficient. The evaluation compared the performance of the GON model on Topcon photographs as a reference to that of Optain photographs and GAN-translated photographs.

Results

The GAN model significantly reduced Optain false positive results for GON diagnosis, with RMSE, PSNR, and SSIM of GAN images being 0.067, 14.31, and 0.64, respectively, the mean difference of VCDR and cup-to-disc area ratio between Topcon and GAN images being 0.03, 95% LOA ranging from -0.09 to 0.15 and -0.05 to 0.10. Pearson correlation coefficients increased from 0.61 to 0.85 in VCDR and 0.70 to 0.89 in cup-to-disc area ratio, whereas Cohen's Kappa improved from 0.32 to 0.60 after GAN translation.

Conclusions

Image-to-image translation across cameras can be achieved by using GAN to solve the problem of disc overexposure in Optain cameras.

Translational Relevance

Our approach enhances the generalizability of deep learning diagnostic models, ensuring their performance on cameras that are outside of the original training data set.

External validation of a deep learning detection system for glaucomatous optic neuropathy: a real-world multicentre study

OBJECTIVES

To conduct an external validation of an automated artificial intelligence (AI) diagnostic system using fundus photographs from a real-life multicentre cohort.

METHODS

We designed external validation in multiple scenarios, consisting of 3049 images from Qilu Hospital of Shandong University in China (QHSDU, validation dataset 1), 7495 images from three other hospitals in China (validation dataset 2), and 516 images from high myopia (HM) population of QHSDU (validation dataset 3). The corresponding sensitivity, specificity and accuracy of this AI diagnostic system to identify glaucomatous optic neuropathy (GON) were calculated.

RESULTS

In validation datasets 1 and 2, the algorithm yielded accuracy of 93.18% and 91.40%, area under the receiver operating curves (AUC) of 95.17% and 96.64%, and significantly higher sensitivity of 91.75% and 91.41%, respectively, compared to manual graders. On the subsets complicated with retinal comorbidities, such as diabetic retinopathy or age-related macular degeneration, in validation datasets 1 and 2, the algorithm achieved accuracy of 87.54% and 93.81%, and AUC of 97.02% and 97.46%, respectively. In validation dataset 3, the algorithm achieved comparable accuracy of 81.98% and AUC of 87.49%, with a sensitivity of 83.61% and specificity of 81.76% on GON recognition specifically in the HM population.

CONCLUSIONS

With acceptable generalization capability across varying levels of image quality, different clinical centres, or certain retinal comorbidities, such as HM, the automatic AI diagnostic system had the potential to provide expert-level glaucoma detection.

A comparative study on the knowledge levels of artificial intelligence programs in diagnosing ophthalmic pathologies and intraocular tumors evaluated their superiority and potential utility

PURPOSE

This study aimed to test the knowledge levels of ChatGPT, Bing, and Bard artificial intelligence chatbots, which have been released by three different manufacturers, about ophthalmic pathologies and intraocular tumors, to test their usability and to investigate the presence of superiority to each other.

METHODS

Thirty-six questions were obtained from the American Academy and Ophthalmology 2022-2023 Basic and Clinical Science Course Ophthalmic Pathology and Intraocular Tumor study questions section. Each question was asked separately for the ChatGPT, Bing, and Bard artificial intelligence programs. Answers to the questions were categorized as correct or incorrect. The statistical relationship between the correct and incorrect response rates of the artificial intelligence programs was determined.

RESULTS

From the artificial intelligence chatbots, ChatGPT gave the correct answer to 58.6% of the questions asked, Bing gave the correct answer to 63.9%, and Bard gave the correct answer to 69.4%. No statistical significance was found between the rates of correct answers to the questions in all 3 artificial intelligence programs (p = 0.705, Pearson Chi-square test).

CONCLUSION

Artificial intelligence chatbots can be used to access information related to ophthalmic pathologies and intraocular tumors. However, in the evaluation of the data, it should be noted that not all questions can be answered correctly. Care should be taken when examining the answers.

Large language models and their impact in ophthalmology

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning

BACKGROUND/AIMS

Fundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA images is complex and time-consuming, and the ability of diagnosis is uneven among different ophthalmologists. The aim of the study is to develop a clinically usable multilevel classification deep learning model for FFA images, including prediagnosis assessment and lesion classification.

METHODS

A total of 15 599 FFA images of 1558 eyes from 845 patients diagnosed with DR were collected and annotated. Three convolutional neural network (CNN) models were trained to generate the label of image quality, location, laterality of eye, phase and five lesions. Performance of the models was evaluated by accuracy, F-1 score, the area under the curve and human-machine comparison. The images with false positive and false negative results were analysed in detail.

RESULTS

Compared with LeNet-5 and VGG16, ResNet18 got the best result, achieving an accuracy of 80.79%-93.34% for prediagnosis assessment and an accuracy of 63.67%-88.88% for lesion detection. The human-machine comparison showed that the CNN had similar accuracy with junior ophthalmologists. The false positive and false negative analysis indicated a direction of improvement.

CONCLUSION

This is the first study to do automated standardised labelling on FFA images. Our model is able to be applied in clinical practice, and will make great contributions to the development of intelligent diagnosis of FFA images.

"Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration"

INTRODUCTION

Age-related macular degeneration (AMD) affects millions of people globally, leading to a surge in online research of putative diagnoses, causing potential misinformation and anxiety in patients and their parents. This study explores the efficacy of artificial intelligence-derived large language models (LLMs) like in addressing AMD patients' questions.

METHODS

ChatGPT 3.5 (2023), Bing AI (2023), and Google Bard (2023) were adopted as LLMs. Patients' questions were subdivided in two question categories, (a) general medical advice and (b) pre- and post-intravitreal injection advice and classified as (1) accurate and sufficient (2) partially accurate but sufficient and (3) inaccurate and not sufficient. Non-parametric test has been done to compare the means between the 3 LLMs scores and also an analysis of variance and reliability tests were performed among the 3 groups.

RESULTS

In category a) of questions, the average score was 1.20 (± 0.41) with ChatGPT 3.5, 1.60 (± 0.63) with Bing AI and 1.60 (± 0.73) with Google Bard, showing no significant differences among the 3 groups (p = 0.129). The average score in category b was 1.07 (± 0.27) with ChatGPT 3.5, 1.69 (± 0.63) with Bing AI and 1.38 (± 0.63) with Google Bard, showing a significant difference among the 3 groups (p = 0.0042). Reliability statistics showed Chronbach's α of 0.237 (range 0.448, 0.096-0.544).

CONCLUSION

ChatGPT 3.5 consistently offered the most accurate and satisfactory responses, particularly with technical queries. While LLMs displayed promise in providing precise information about AMD; however, further improvements are needed especially in more technical questions.

Impact of an email advice service on filtering and refining ophthalmology referrals in England

PURPOSE

The growing capacity-demand imbalance has necessitated the accelerated digital transformation of eye care services. The role of Oxford Eye Hospital's (OEH) email advice service has become even more relevant in the post-Covid era. We sought to evaluate its impact on referrals to secondary care.

METHODS

The consultant-led OEH email advice service primarily targets primary eye care personnel (optometrists and GPs) requiring clinical advice on patient referral. Emails received between September and November 2020 were analysed for demographic data, contents, characteristics, and outcomes. Thematic analysis was performed. A user feedback survey was conducted.

RESULTS

A total of 828 emails were received over the 3-month study period (mean 9.1/day). They were predominantly from optometrists (77.9%) and general practitioners (16.1%). Of the 81.0% (671) relating to clinical advice, over half (54.8%) included images from a variety of modalities, and following review, over half (55.5%) were deemed suitable for management in the community, while 36.5% were referred directly to appropriate subspecialty clinics. Only 8.1% required urgent assessment in eye casualty. Thematic analysis showed that this service was most useful for retinal lesions, optical coherence tomography abnormalities, and borderline abnormal optic discs. No adverse events were identified. User feedback was very positive.

CONCLUSION

A secure email advice service is a safe and low-maintenance modality that provides direct and efficient two-way communication between primary and secondary eye care professionals. It allows rapid response to clinical queries, referral filtering and refinement, and streamlining of patient referral pathways. Users (predominantly optometrists) were overwhelmingly positive about its usefulness in clinical practice.

Endpoints for clinical trials in ophthalmology

With the identification of novel targets, the number of interventional clinical trials in ophthalmology has increased. Visual acuity has for a long time been considered the gold standard endpoint for clinical trials, but in the recent years it became evident that other endpoints are required for many indications including geographic atrophy and inherited retinal disease. In glaucoma the currently available drugs were approved based on their IOP lowering capacity. Some recent findings do, however, indicate that at the same level of IOP reduction, not all drugs have the same effect on visual field progression. For neuroprotection trials in glaucoma, novel surrogate endpoints are required, which may either include functional or structural parameters or a combination of both. A number of potential surrogate endpoints for ophthalmology clinical trials have been identified, but their validation is complicated and requires solid scientific evidence. In this article we summarize candidates for clinical endpoints in ophthalmology with a focus on retinal disease and glaucoma. Functional and structural biomarkers, as well as quality of life measures are discussed, and their potential to serve as endpoints in pivotal trials is critically evaluated.

The Sensitivity of Ultra-Widefield Fundus Photography Versus Scleral Depressed Examination for Detection of Retinal Horseshoe Tears

PURPOSE

Ultra-widefield (UWF) imaging is commonly used in ophthalmology in tandem with scleral depressed examinations (SDE) to evaluate peripheral retinal disease. Because of the increased reliance on this technology in tele-ophthalmology, it is critical to evaluate its efficacy for detecting the peripheral retina when performed in isolation. Therefore, we sought to evaluate UWF imaging sensitivity in detecting retinal horseshoe tears (HSTs).

STUDY DESIGN

Retrospective clinical validity and reliability study.

METHODS

A single-institutional retrospective analysis was performed on patients at the Shiley Eye Institute, University of California, San Diego. Patients with HSTs seen on SDE who underwent treatment with laser were included in the study. A total of 140 patients with HSTs in the right and/or left eyes met the inclusion criteria. Those with concomitant ruptured globes, retinal detachments, and vitreous hemorrhages were excluded. A total of 123 patients with 135 HSTs were included in the final analysis. The primary outcome was the number of HSTs detected by UWF imaging. A secondary outcome was HST location. Sensitivity was measured with respect to HST location, and statistical significance was calculated by Fisher exact testing.

RESULTS

A total of 69 (51.1%) HSTs were visualized on UWF images and 66 (48.9%) were not visualized. The sensitivity of UWF imaging in capturing HSTs was 7 of 41 (17.1%), 8 of 25 (32.0%), 7 of 14 (50.0%), and 47 of 55 (85.5%) for the superior, inferior, nasal, and temporal quadrants, respectively. Sensitivities between HST visibility and location were statistically significant (P < .001).

CONCLUSIONS

Nearly half of HSTs were missed by UWF imaging. This study demonstrates that UWF imaging alone is not sufficiently sensitive to exclude the presence of HSTs.

Detection and Classification of Diabetic Macular Edema with a Desktop-Based Code-Free Machine Learning Tool

Objectives

To evaluate the effectiveness of the Lobe application, a machine learning (ML) tool that can be used on a personal computer without requiring coding expertise, in the recognition and classification of diabetic macular edema (DME) in spectral-domain optical coherence tomography (SD-OCT) scans.

Materials and Methods

A total of 695 cross-sectional SD-OCT images from 336 patients with DME and 200 OCT images of 200 healthy controls were included. Images with DME were classified into three main types: diffuse retinal edema (DRE), cystoid macular edema (CME), and cystoid macular degeneration (CMD). To develop the ML model, we used the desktop-based code-free Lobe application, which includes a pre-trained ResNet-50 V2 convolutional neural network and is available free of charge. The performance of the trained model in recognizing and classifying DME was evaluated with 41 DRE, 28 CMD, 70 CME, and 40 normal SD-OCT images that were not used in the training.

Results

The developed model showed 99.28% sensitivity and 100% specificity for class-independent detection of DME. Sensitivity and specificity by labels were 87.80% and 98.57% for DRE, 96.43% and 99.29% for CME, and 95.71% and 95.41% for CMD, respectively.

Conclusion

To our knowledge, this is the first evaluation of the effectiveness of Lobe with ophthalmological images, and the results indicate that it can be used with high efficiency in the recognition and classification of DME from SD-OCT images by ophthalmologists without coding expertise.

Impact of Covid-19 on Pediatric Ophthalmology Care: Lessons Learned

The COVID-19 pandemic came with many new challenges that forced personal and professional lifestyle modifications. Medical facilities were in scarcity against this new unknown enemy and were challenged with the overloaded patient flow, scarcity of healthcare staff, and evolving treatment modalities with a better understanding of the virus each day. Ophthalmology as a "branch of medicine" suffered challenges initially because of a lack of guidelines for patient management, close working distance during routine examinations, and halt of major surgeries, including cataracts. Pediatric ophthalmology had major implications, as reduced outpatient visits would mean deeper amblyopia, and changed lifestyles, including online classes and home refinement, predisposing children to myopia, digital eye strain, and worsening of strabismus. COVID-19 also unveiled underlying accommodation and convergence anomalies that predisposed pediatric and adolescent patients to an increased prevalence of headache and acute onset esotropia. Teleophthalmology and other innovative solutions, including the use of prism glasses, safe slit-lamp shields, alternative ways of school screening with the use of photoscreeners, performing retinoscopy only when needed, and using autorefractors were among the few guidelines or modifications adopted which helped in the efficient and safe management of pediatric patients. Many pediatric ophthalmologists also suffered in terms of financial constraints due to loss of salary or even closure of private practices. School screening and retinopathy of prematurity screening suffered a great setback and costed a lot of vision years, data of which remains under-reported. Important implications and learnings from the pandemic to mitigate future similar situations include using teleophthalmology and virtual platforms for the triage of patients, managing non-emergency conditions without physical consultations, and utilizing home-based vision assessment techniques customized for different age groups. Though this pandemic had a lot of negative implications, the innovations, modifications, and other important learnings helped pediatric ophthalmologists in navigating safely.

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.

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.

Navigating the Landscape of Personalized Medicine: The Relevance of ChatGPT, BingChat, and Bard AI in Nephrology Literature Searches

BACKGROUND AND OBJECTIVES

Literature reviews are foundational to understanding medical evidence. With AI tools like ChatGPT, Bing Chat and Bard AI emerging as potential aids in this domain, this study aimed to individually assess their citation accuracy within Nephrology, comparing their performance in providing precise.

MATERIALS AND METHODS

We generated the prompt to solicit 20 references in Vancouver style in each 12 Nephrology topics, using ChatGPT, Bing Chat and Bard. We verified the existence and accuracy of the provided references using PubMed, Google Scholar, and Web of Science. We categorized the validity of the references from the AI chatbot into (1) incomplete, (2) fabricated, (3) inaccurate, and (4) accurate.

RESULTS

A total of 199 (83%), 158 (66%) and 112 (47%) unique references were provided from ChatGPT, Bing Chat and Bard, respectively. ChatGPT provided 76 (38%) accurate, 82 (41%) inaccurate, 32 (16%) fabricated and 9 (5%) incomplete references. Bing Chat provided 47 (30%) accurate, 77 (49%) inaccurate, 21 (13%) fabricated and 13 (8%) incomplete references. In contrast, Bard provided 3 (3%) accurate, 26 (23%) inaccurate, 71 (63%) fabricated and 12 (11%) incomplete references. The most common error type across platforms was incorrect DOIs.

CONCLUSIONS

In the field of medicine, the necessity for faultless adherence to research integrity is highlighted, asserting that even small errors cannot be tolerated. The outcomes of this investigation draw attention to inconsistent citation accuracy across the different AI tools evaluated. Despite some promising results, the discrepancies identified call for a cautious and rigorous vetting of AI-sourced references in medicine. Such chatbots, before becoming standard tools, need substantial refinements to assure unwavering precision in their outputs.