Identification of factors to increase efficacy of telemedicine screening for diabetic retinopathy in endocrinology practices using the Intelligent Retinal Imaging System (IRIS) platform

Surgical strategy and outcome in patients with bilateral proliferative diabetic retinopathy

OBJECTIVES

To investigate the current surgery strategies for bilateral proliferative diabetic retinopathy (PDR), as well as the surgical outcomes of patients with bilateral PDR who underwent pars plana vitrectomy (PPV).

MATERIALS

Patients undergoing bilateral vitrectomy for PDR from January 2019 to December 2020 at The Eye Hospital of Wenzhou Medical University were enrolled. Clinical data were collected from the electronic medical records. Factors associated with the time interval between the surgeries on two eyes and postoperative visual outcomes were analyzed.

RESULTS

In total, 152 patients with bilateral PDR who underwent bilateral PPV were included in this analysis. Mean age was 53.7 ± 11.4 years. Compared with second-surgery eyes, 60.5% of first-surgery eyes had worse preoperative best-corrected visual acuity (BCVA). The overall PPV time (median, quartile range) between first and second surgeries eye was 1.40 (0.70, 3.15) months. Multivariate analysis showed that the preoperative BCVA of the second-surgery eye had a significant effect on the inter-surgery time interval (P = 0.048). First-surgery eyes had greater vision improvement than second-surgery eyes (Difference of the logarithm of the minimum angle of resolution [LogMAR] BCVA: - 1.00 [- 1.48, - 0.12] versus 0.00 [- 1.30, 0.00], respectively, P < 0.001), especially when eyes with poorer BCVA underwent PPV first (- 1.15 [- 1.87, - 0.54] versus 0.00 [- 0.70, 0.00], respectively, P < 0.001).

CONCLUSIONS

Visual acuity is a significant factor that influences surgical strategies, including both surgery order and interval, for patients with bilateral PDR. The eyes operated upon first show more vision improvement due to prompt surgery.

Epidemiology of Treated Diabetes Ocular Complications in France 2008-2018-The LANDSCAPE French Nationwide Study

AIM

LANDSCAPE aimed to estimate the annual incidence and prevalence of treated diabetic macular edema (DME) and proliferative diabetic retinopathy (PDR) between 2008 and 2018.

METHODS

This French nationwide observational study used data from the French National Health Insurance Databases covering 99% of the French population. Data about healthcare consumption were used to identify adults treated with anti-VEGFs or dexamethasone implants (for DME) and with pan-retinal photocoagulation (for PDR). All French patients newly treated between 2008 and 2018 were included. Incidence and prevalence of treated DME and PDR were estimated for the age-matched general population and the population with diabetes in France. Sociodemographic characteristics and medical history were described in both populations.

RESULTS

We identified 53,584 treated DME patients and 127,273 treated PDR patients between 2008 and 2018, and 11,901 DME and 11,996 PDR new incident patients in 2018. The treated DME incidence in 2018 was 2.5 per 10,000 in the general population and 37.3 per 10,000 in the population with diabetes. Prevalence in 2018 was 9.5 and 143.7 per 10,000 in the respective populations. Treated PDR incidence in 2018 was 2.3 per 10,000 in the general population and 31.2 per 10,000 in the population with diabetes. Prevalence in 2018 was 19.9 and 270.3 per 10,000 in the respective populations. Incidence and prevalence were not age-dependent. Incidence of treated PDR incidence was relatively stable from 2008-2018. Incidence of treated DME incidence rose from 2012-2018, probably due to widening access to newly available treatments, such as anti-VEGFs.

CONCLUSIONS

We provide exhaustive nationwide data on the incidence and prevalence of treated diabetic ocular complications in France over a 10-year period.

A New Deep Learning Algorithm with Activation Mapping for Diabetic Retinopathy: Backtesting after 10 Years of Tele-Ophthalmology

We report the development of a deep learning algorithm (AI) to detect signs of diabetic retinopathy (DR) from fundus images. For this, we use a ResNet-50 neural network with a double resolution, the addition of Squeeze–Excitation blocks, pre-trained in ImageNet, and trained for 50 epochs using the Adam optimizer. The AI-based algorithm not only classifies an image as pathological or not but also detects and highlights those signs that allow DR to be identified. For development, we have used a database of about half a million images classified in a real clinical environment by family doctors (FDs), ophthalmologists, or both. The AI was able to detect more than 95% of cases worse than mild DR and had 70% fewer misclassifications of healthy cases than FDs. In addition, the AI was able to detect DR signs in 1258 patients before they were detected by FDs, representing 7.9% of the total number of DR patients detected by the FDs. These results suggest that AI is at least comparable to the evaluation of FDs. We suggest that it may be useful to use signaling tools such as an aid to diagnosis rather than an AI as a stand-alone tool.

Prevalence and associated factors of diabetic retinopathy among type 2 diabetes mellitus patients in Brunei Darussalam - A cross-sectional study

Purpose

To determine the prevalence of diabetic retinopathy (DR) and the factors associated with retinopathy among type 2 diabetes mellitus (DM) patients in Brunei Darussalam.

Methods

Cross-sectional study of all type 2 DM patients who attended diabetic eye screening over a 3-month period at one of four government hospitals. We assessed association between DR with the following variables: age, sex, glycated hemoglobin (HbA1c), duration of DM, hypertension, hyperlipidemia, and microalbuminuria.

Results

There were 341 patients (female, 58.9%; mean age, 55.3 ± 11.9 years) with a mean duration of DM of 9.4 ± 7.4 years and mean serum HbA1c of 8.4% ± 1.9%. The overall prevalence of any DR was 22.6% (95% confidence interval, 18.8–27.1) with prevalence rates of 4.1% (95% confidence interval, 2.1–6.4) for proliferative DR and 9.7% (95% confidence interval, 6.8–13.2) for vision-threatening DR. Multivariate analysis showed that DR was significantly associated with certain age groups (reduced in older age groups), longer duration of DM (11 years or more), poor control (HbA1c >9.0%) and presence of any microalbuminuria.

Conclusions

DR affects one in five patients with DM in Brunei Darussalam, comparable to rates reported for other Asian populations. It is especially worrying that one in ten patients with DM had vision-threatening DR. DR was significantly associated with longer duration of DM, poor control and presence of microalbuminuria but reduced in older age groups. It is important to advocate good control right from the time of diagnosis of DM and institute timely and effective management of retinopathy. DR was significantly associated with longer duration of DM, poor control of diabetes, and presence of microalbuminuria but reduced in older age groups.

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as “interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients.” This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings

BACKGROUND

Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are among the leading causes of blindness in the United States and other developed countries. Early detection is the key to prevention and effective treatment. We have built an artificial intelligence-based screening system which utilizes a cloud-based platform for combined large scale screening through primary care settings for early diagnosis of these diseases.

METHODS

iHealthScreen Inc., an independent medical software company, has developed automated AMD and DR screening systems utilizing a telemedicine platform based on deep machine learning techniques. For both diseases, we prospectively imaged both eyes of 340 unselected non-dilated subjects over 50 years of age. For DR specifically, 152 diabetic patients at New York Eye and Ear faculty retina practices, ophthalmic and primary care clinics in New York city with color fundus cameras. Following the initial review of the images, 308 images with other confounding conditions like high myopia and vascular occlusion, and poor quality were excluded, leaving 676 eligible images for AMD and DR evaluation. Three ophthalmologists evaluated each of the images, and after adjudication, the patients were determined referrable or non-referable for AMD DR. Concerning AMD, 172 were labeled referable (intermediate or late), and 504 were non-referable (no or early). Concurrently, regarding DR, 33 were referable (moderate or worse), and 643 were non-referable (none or mild). All images were uploaded to iHealthScreen's telemedicine platform and analyzed by the automated systems for both diseases. The system performances are tested on per eye basis with sensitivity, specificity, accuracy, and kappa scores with respect to the professional graders.

RESULTS

In identifying referable DR, the system achieved a sensitivity of 97.0% and a specificity of 96.3%, and a kappa score of 0.70 on this prospective dataset. For AMD, the sensitivity was 86.6%, the specificity of 92.1%, and a kappa score of 0.76.

CONCLUSIONS

The AMD and DR screening tools achieved excellent performance operating together to identify two retinal diseases prospectively in mixed datasets, demonstrating the feasibility of such tools in the early diagnosis of eye diseases. These early screening tools will help create an even more comprehensive system capable of being trained on other retinal pathologies, a goal within reach for public health deployment.

Automated diabetic retinopathy screening for primary care settings using deep learning

Diabetic Retinopathy (DR) is one of the leading causes of blindness in the United States and other high-income countries. Early detection is key to prevention, which could be achieved effectively with a fully automated screening tool performing well on clinically relevant measures in primary care settings. We have built an artificial intelligence-based tool on a cloud-based platform for large-scale screening of DR as referable or non-referable. In this paper, we aim to validate this tool built using deep learning based techniques. The cloud-based screening model was developed and tested using deep learning techniques with 88702 images from the Kaggle dataset and externally validated using 1748 high-resolution images of the retina (or fundus images) from the Messidor-2 dataset. For validation in the primary care settings, 264 images were taken prospectively from two diabetes clinics in Queens, New York. The images were uploaded to the cloud-based software for testing the automated system as compared to expert ophthalmologists’ evaluations of referable DR. Measures used were area under the curve (AUC), sensitivity, and specificity of the screening model with respect to professional graders. The screening system achieved a high sensitivity of 99.21% and a specificity of 97.59% on the Kaggle test dataset with an AUC of 0.9992. The system was also externally validated in Messidor-2, where it achieved a sensitivity of 97.63% and a specificity of 99.49% (AUC, 0.9985). On primary care data, the sensitivity was 92.3% overall (12/13 referable images are correctly identified), and overall specificity was 94.8% (233/251 non-referable images). The proposed DR screening tool achieves state-of-the-art performance among the publicly available datasets: Kaggle and Messidor-2 to the best of our knowledge. The performance on various clinically relevant measures demonstrates that the tool is suitable for screening and early diagnosis of DR in primary care settings.

[Current trends in the screening for diabetic retinopathy]

The incidence of diabetes in the world is steadily increasing, and so is growing the number of cases of vision loss and blindness resulting from diabetic retinopathy (DR). This pathology is asymptomatic in the initial stages, but only the early treatment can be effective. In this regard, DR screening is an important and actual problem. This article reviews the principles, criteria, and problems of the currently run DR screening programs that are based on digital photography of the fundus. Special attention is paid to the displayed biomarkers and their role in DR screening. Various research methods are described, such as fluorescence angiography, optical coherence tomography, optical coherence tomography agniography, laser scanning ophthalmoscopy, which can be used to visualize pathological changes in the retina associated with DR. These changes were considered as potential screening biomarkers for DR. The review also describes new areas of screening based on telemedicine, artificial intelligence, and mobile photo-registering devices.

Enhancing Risk Assessment in Patients with Diabetic Retinopathy by Combining Measures of Retinal Function and Structure

Purpose

To determine whether combining measures of retinal structure and function predicts need for intervention for diabetic retinopathy (DR) better than either modality alone.

Methods

The study sample consisted of 279 diabetic patients who participated in an earlier cross-sectional study. Patients were excluded if they were previously treated for macular edema or proliferative DR or if they had other retinopathies. Medical records were reviewed for ocular interventions including vitrectomy, intravitreal injection, and laser treatment. Need for intervention was analyzed using Kaplan-Meier analyses and Cox proportional hazards. Baseline electroretinograms and fundus photographs were obtained. Two definitions of structural positive findings were as follows: 1. Early Treatment of Diabetic Retinopathy Study diabetic retinopathy severity scale (ETDRS-DR) severity ≥ level 53 (ETDRS-DR+) and 2. ETDRS-DR+ or clinically significant macular edema (VTDR+). A positive function finding corresponded to a RETeval DR Score >23.5 (RETeval+).

Results

For patients with VTDR+ the incidence of intervention was 19%, 31%, and 53% after 1, 2, and 3 years of follow-up. In these patients, intervention incidence increased to 34%, 54%, and 74% the subsequent 1, 2, and 3 years if function was above criterion (RETeval+), whereas RETeval-  results reduced the risk to 3%, 4%, and 29%, respectively, reducing risk to similar levels seen for patients with VTDR- results at baseline.

Conclusions

Prediction of subsequent intervention was best when combining structural and functional information.

Translational Relevance

This study demonstrates that clinical management of diabetic retinopathy is improved by adding electroretinography to fundus photographic information in assessing the risk of the need for intervention.

IDF Diabetes Atlas: A review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018

AIMS

The purpose of this study is to assess the prevalence of diabetic retinopathy (DR) world-wide from articles published since 2015 where the assessment of the presence and severity of DR was based on retinal images.

METHODS

A total of 4 databases were searched for the MESH terms diabetic retinopathy and prevalence. Of 112 publications 32 studies were included and individual data pooled for analysis. The presence of any DR or diabetic macular edema (DME) was recorded and severity as mild, moderate or severe non-proliferative DR (NPDR), proliferative DR (PDR) and DME and/or clinically significant macular edema (CSME). The level of severity of DR reported refer to persons with diabetes and not individual eyes.

RESULTS

The global prevalence of DR and DME, for the period 2015 to 2019 were 27.0% for any DR comprising of 25.2%, NPDR, 1.4% PDR and 4.6% DME. The lowest prevalence was in Europe at 20.6% and South East Asia at 12.5% and highest in Africa at 33.8%, Middle East and North Africa 33.8%, and the Western Pacific region at 36.2%.

CONCLUSIONS

This study illustrated difficulties in deriving a meaningful global prevalence rate for DR and DME due to the lack of uniformity in defining the study populations, methodological differences, retinal image capture and grading criteria. Therefore, international consensus is required using a minimal data set for future studies.

Multimodal imaging of diabetic retinopathy

PURPOSE OF REVIEW

Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and a leading cause of blindness throughout the world. Ocular imaging continues to play a vital role in the diagnosis, management and monitoring of diabetic retinopathy. Major technological advancements in imaging over the past decade have improved our understanding and knowledge of diabetic retinopathy and therefore a multimodal approach to imaging has become the standard of care.

RECENT FINDINGS

Updates to traditional technologies such as digital fundus photography along with recent advancements in optical coherence tomography (OCT) and OCT angiography (OCTA) have provided clinicians with new information and improved efficiency.

SUMMARY

In this review, we describe the benefits and clinical applications of several imaging techniques in diabetic retinopathy including color photography, fluorescein angiography, OCT, OCTA and adaptive optics. Understanding the indications and limitations of each technology allows clinicians to gain the most information from each modality and thereby optimize patient care.