Selfie fundus imaging for diabetic retinopathy screening

Eye-brain connections revealed by multimodal retinal and brain imaging genetics

The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

The role of endothelial growth factor and tear levels in diabetic retinopathy in type 2 diabetes

PURPOSE

To evaluate the tear level of VEGF and the quantity of tear film in type 2 diabetic patients.

METHODS

Thirty patients with diabetic retinopathy (DR group) and 30 patients with no DR (NDR group), and 30 healthy subjects with age and gender matching were enrolled in this prospective comparative study. The tear samples were collected using the Schirmer strips, and the amount of moisture absorbed by the strips was used to determine the quantitative level of the tear film. The concentration of VEGF in the tear samples was measured using the enzyme-linked immunosorbent assay method. The variables were compared with an independent t-test and covariance analysis.

RESULTS

Mean tear level of VEGF was significantly higher in DR group (235.42 pg/ml) compared to NDR (75.11 pg/ml) and control (58.77 pg/ml) groups (P ≤ 0.001). There was no significant difference in the mean of VEGF between NDR and control patients (P = 1.00). Mean quantitative tear film levels were 7.15%, 9.72%, and 15.11% in DR, NDR, and healthy subjects, respectively (P < 0.05). The pairwise analysis showed significant differences in the level of VEGF between DR and both NDR (P = 0.001) and normal (P = 0.017) groups. However, there was no significant difference observed between NDR and normal eyes (P = 0.743).

CONCLUSION

The VEGF level in tear was higher in diabetic patients with DR, independent of tear volume. The tear VEGF measurement can be used as a valuable predictor to prevent DR in diabetic patients.

A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images

Objective

To develop and validate an artificial intelligence diagnostic model based on fundus images for predicting Carotid Intima-Media Thickness (CIMT) in individuals with Type 2 Diabetes Mellitus (T2DM).

Methods

In total, 1236 patients with T2DM who had both retinal fundus images and CIMT ultrasound records within a single hospital stay were enrolled. Data were divided into normal and thickened groups and sent to eight deep learning models: convolutional neural networks of the eight models were all based on ResNet or ResNeXt. Their encoder and decoder modes are different, including the standard mode, the Parallel learning mode, and the Siamese mode. Except for the six unimodal networks, two multimodal networks based on ResNeXt under the Parallel learning mode or the Siamese mode were embedded with ages. Performance of eight models were compared via the confusion matrix, precision, recall, specificity, F1 value, and ROC curve, and recall was regarded as the main indicator. Besides, Grad-CAM was used to visualize the decisions made by Siamese ResNeXt network, which is the best performance.

Results

Performance of various models demonstrated the following points: 1) the RexNeXt showed a notable improvement over the ResNet; 2) the structural Siamese networks, which extracted features parallelly and independently, exhibited slight performance enhancements compared to the traditional networks. Notably, the Siamese networks resulted in significant improvements; 3) the performance of classification declined if the age factor was embedded in the network. Taken together, the Siamese ResNeXt unimodal model performed best for its superior efficacy and robustness. This model achieved a recall rate of 88.0% and an AUC value of 90.88% in the validation subset. Additionally, heatmaps calculated by the Grad-CAM algorithm presented concentrated and orderly mappings around the optic disc vascular area in normal CIMT groups and dispersed, irregular patterns in thickened CIMT groups.

Conclusion

We provided a Siamese ResNeXt neural network for predicting the carotid intimal thickness of patients with T2DM from fundus images and confirmed the correlation between fundus microvascular lesions and CIMT.

Recent developments of telemedicine in glaucoma

PURPOSE OF REVIEW

Telemedicine has an increasingly significant role in the fields of ophthalmology and glaucoma. This review covers recent advancements in the development and optimization of teleglaucoma techniques and applications.

RECENT FINDINGS

Glaucoma monitoring and diagnosis via remote tonometry, perimetry, and fundus imaging have become a possibility based on recent developments. Many applications work in combination with smart devices, virtual reality, and artificial intelligence and have been tested in patient populations against conventional "reference-standard" measurement tools, demonstrating promising results. Of note, there is still much progress to be made in teleglaucoma and telemedicine at large, such as accessibility to internet, broadband, and smart devices, application affordability, and reimbursement for remote services. However, continued development and optimization of these applications suggest that the implementation of remote monitoring will be a mainstay for glaucoma patient care.

SUMMARY

Especially since the beginning of the COVID-19 pandemic, remote patient care has taken on an important role in medicine and ophthalmology. Remote versions of tonometry, perimetry, and fundus imaging may allow for a more patient-centered and accessible future for glaucoma care.

Clinical utility of handheld fundus and smartphone-based camera for monitoring diabetic retinal diseases: a review study

Diabetic retinopathy (DR) is the leading global cause of vision loss, accounting for 4.8% of global blindness cases as estimated by the World Health Organization (WHO). Fundus photography is crucial in ophthalmology as a diagnostic tool for capturing retinal images. However, resource and infrastructure constraints limit access to traditional tabletop fundus cameras in developing countries. Additionally, these conventional cameras are expensive, bulky, and not easily transportable. In contrast, the newer generation of handheld and smartphone-based fundus cameras offers portability, user-friendliness, and affordability. Despite their potential, there is a lack of comprehensive review studies examining the clinical utilities of these handheld (e.g. Zeiss Visuscout 100, Volk Pictor Plus, Volk Pictor Prestige, Remidio NMFOP, FC161) and smartphone-based (e.g. D-EYE, iExaminer, Peek Retina, Volk iNview, Volk Vistaview, oDocs visoScope, oDocs Nun, oDocs Nun IR) fundus cameras. This review study aims to evaluate the feasibility and practicality of these available handheld and smartphone-based cameras in medical settings, emphasizing their advantages over traditional tabletop fundus cameras. By highlighting various clinical settings and use scenarios, this review aims to fill this gap by evaluating the efficiency, feasibility, cost-effectiveness, and remote capabilities of handheld and smartphone fundus cameras, ultimately enhancing the accessibility of ophthalmic services.

Navigating the Frontier: Emerging Techniques for Detecting Microvascular Complications in Type 2 Diabetes Mellitus: A Comprehensive Review

This review comprehensively explores emerging techniques for detecting microvascular complications in Type 2 Diabetes Mellitus (T2DM), addressing the critical need for advancements in early detection and management. As T2DM continues to rise globally, microvascular complications, including retinopathy, nephropathy, and neuropathy, contribute significantly to the morbidity and mortality associated with the condition. The review synthesizes key findings, revealing various emerging technologies, from advanced imaging modalities to genomic and proteomic approaches. It underscores the potential for personalized medicine, emphasizing the importance of tailoring diagnostic strategies to individual patient profiles. Challenges, including the lack of standardized criteria and issues related to patient adherence, highlight the necessity for collaborative efforts. The conclusion issues a call to action, advocating for enhanced collaboration, increased research investment, patient empowerment through education, and seamless integration of emerging diagnostic techniques into routine clinical care. The review envisions a transformative shift in detecting and managing microvascular complications in T2DM, ultimately improving patient outcomes and contributing to a healthier future for individuals affected by this prevalent metabolic disorder.

Review of smartphone funduscopy for diabetic retinopathy screening

I detail advances in funduscopy diagnostic systems integrating smartphones. Smartphone funduscopy devices are comprised of lens devices connecting with smartphones and software applications to be used for mobile retinal image capturing and diagnosis of diabetic retinopathy. This is particularly beneficial to automate and mobilize retinopathy screening techniques and methods in remote and rural areas as those diabetes patients are often not meeting the required regular screening for diabetic retinopathy. Smartphone retinal image grading systems enable retinopathy to be screened remotely as teleophthalmology or as a stand-alone point-of-care-testing system. Smartphone funduscopy aims to avoid the need for patients to be seen by expert ophthalmologists, which can reduce patient travel, time taken for images to be processed, appointment backlog, health service overhead costs, and the workload burden for expert ophthalmologists.

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.

Diabetic Retinopathy - a Common Disease

Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus and one of the leading causes of visual impairment in working age individuals in the western world. The treatment of DR depends on its severity, so it is of great importance to detect patients as early as possible, in order to initiate early treatment and preserve vision. Despite currently insufficient screening participation, patients with diabetes already visit ophthalmological practices and clinics above average. Their medical care, including DR diagnostics and treatment has been making up an increasing proportion of ophthalmic activity for years. Since the prevalence of diabetes is increasing dramatically worldwide and a further increase is also predicted for Germany, the challenge for ophthalmologists is likely to grow considerably. As the same time, the diagnostic possibilities for differentiating DR and the therapeutic measures, especially with IVOM therapy, are becoming more and more complex, which increases the time burden in everyday clinical practice. The hope to avoid healthcare deficits and to further improve screening rates and visual acuity prognosis in patients with DR is based, among other things, on camera-assisted screening supported by artificial intelligence. Better diabetes management to reduce the prevalence of DR, as well as longer-acting drugs to treat DR, could also improve the care and help reduce the burden on ophthalmology practices.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank

As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.