Validation of Smartphone Based Retinal Photography for Diabetic Retinopathy Screening

Clinical Applications and Future Directions of Smartphone Fundus Imaging

The advent of smartphone fundus imaging technology has marked a significant evolution in the field of ophthalmology, offering a novel approach to the diagnosis and management of retinopathy. This review provides an overview of smartphone fundus imaging, including clinical applications, advantages, limitations, clinical applications, and future directions. The traditional fundus imaging techniques are limited by their cost, portability, and accessibility, particularly in resource-limited settings. Smartphone fundus imaging emerges as a cost-effective, portable, and accessible alternative. This technology facilitates the early detection and monitoring of various retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders, thereby democratizing access to essential diagnostic services. Despite its advantages, smartphone fundus imaging faces challenges in image quality, standardization, regulatory considerations, and medicolegal issues. By addressing these limitations, this review highlights the areas for future research and development to fully harness the potential of smartphone fundus imaging in enhancing patient care and visual outcomes. The integration of this technology into telemedicine is also discussed, underscoring its role in facilitating remote patient care and collaborative care among physicians. Through this review, we aim to contribute to the understanding and advancement of smartphone fundus imaging as a valuable tool in ophthalmic practice, paving the way for its broader adoption and integration into medical diagnostics.

Wide-field imaging with smartphone based fundus camera: grading of severity of diabetic retinopathy and locating peripheral lesions in diabetic retinopathy

AIM

To assess the performance of smartphone based wide-field retinal imaging (WFI) versus ultra-wide-field imaging (UWFI) for assessment of sight-threatening diabetic retinopathy (STDR) as well as locating predominantly peripheral lesions (PPL) of DR.

METHODS

Individuals with type 2 diabetes with varying grades of DR underwent nonmydriatic UWFI with Daytona Plus camera followed by mydriatic WFI with smartphone-based Vistaro camera at a tertiary care diabetes centre in South India in 2021-22. Grading of DR as well as identification of PPL (DR lesions beyond the posterior pole) in the retinal images of both cameras was performed by senior retina specialists. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The sensitivity and specificity of smartphone based WFI for detection of PPL and STDR was assessed. Agreement between the graders for both cameras was compared.

RESULTS

Retinal imaging was carried out in 318 eyes of 160 individuals (mean age 54.7 ± 9 years; mean duration of diabetes 16.6 ± 7.9 years). The sensitivity and specificity for detection of STDR by Vistaro camera was 92.7% (95% CI 80.1-98.5) and 96.6% (95% CI 91.5-99.1) respectively and 95.1% (95% CI 83.5-99.4) and 95.7% (95% CI 90.3-98.6) by Daytona Plus respectively. PPL were detected in 89 (27.9%) eyes by WFI by Vistaro camera and in 160 (50.3%) eyes by UWFI. However, this did not translate to any significant difference in the grading of STDR between the two imaging systems. In both devices, PPL were most common in supero-temporal quadrant (34%). The prevalence of PPL increased with increasing severity of DR with both cameras (p < 0.001). The kappa comparison between the 2 graders for varying grades of severity of DR was 0.802 (p < 0.001) for Vistaro and 0.753 (p < 0.001) for Daytona Plus camera.

CONCLUSION

Mydriatic smartphone-based widefield imaging has high sensitivity and specificity for detecting STDR and can be used to screen for peripheral retinal lesions beyond the posterior pole in individuals with diabetes.

Prevalence of Selected Ophthalmic Diseases Using a Smartphone-Based Fundus Imaging System in Quang Tri and Thai Nguyen, Vietnam

OBJECTIVES

This study investigated the prevalence of ophthalmic diseases in Quang Tri and Thai Nguyen, Vietnam, utilizing a smartphone-based fundus imaging (SBFI) system.

METHODS

This cross-sectional study included nearly 10,000 patients who visited community health centers between July and August 2019. All participants underwent visual acuity testing and fundus imaging. We collected demographic data and medical histories, and fundus images were captured using the EYELIKE system. Data were compiled on an online platform, allowing clinicians from other regions to make diagnoses.

RESULTS

The study revealed significant variations in visual acuity and the prevalence of ophthalmic diseases between the two regions. Quang Tri had a higher proportion of individuals with good eyesight compared to Thai Nguyen. In Quang Tri, nearly 50% of the population had media haze, while in Thai Nguyen, about one-third of the population was affected. The prevalence of glaucomatous optic nerve and age-related macular degeneration was approximately 1% higher in Quang Tri than in Thai Nguyen. These findings provide valuable insights into the eye health status of these regions, indicating that eye health in Quang Tri was poorer than in Thai Nguyen.

CONCLUSIONS

The prevalence rates of ophthalmic conditions in this study were within the expected ranges compared to those in other Asian countries, though they were somewhat low. The SBFI method, being simpler and more efficient than the Rapid Assessment of Avoidable Blindness, offers a promising approach for measuring and estimating the prevalence of ophthalmic diseases.

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.

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

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

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.

Diagnostic Accuracy of Hand-Held Fundus Camera and Artificial Intelligence in Diabetic Retinopathy Screening

Our study aimed to assess the role of a hand-held fundus camera and artificial intelligence (AI)-based grading system in diabetic retinopathy (DR) screening and determine its diagnostic accuracy in detecting DR compared with clinical examination and a standard fundus camera. This cross-sectional instrument validation study, as a part of the International Diabetes Federation (IDF) Diabetic Retinopathy Screening Project, included 160 patients (320 eyes) with type 2 diabetes (T2DM). After the standard indirect slit-lamp fundoscopy, each patient first underwent fundus photography with a standard 45° camera VISUCAM Zeiss and then with a hand-held camera TANG (Shanghai Zhi Tang Health Technology Co., Ltd.). Two retina specialists independently graded the images taken with the standard camera, while the images taken with the hand-held camera were graded using the DeepDR system and an independent IDF ophthalmologist. The three screening methods did not differ in detecting moderate/severe nonproliferative and proliferative DR. The area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, kappa (ĸ) agreement, diagnostic odds ratio, and diagnostic effectiveness for a hand-held camera compared to clinical examination were 0.921, 89.1%, 100%, 100%, 91.4%, infinity, 0.11, 0.86, 936.48, and 94.9%, while compared to the standard fundus camera were 0.883, 83.2%, 100%, 100%, 87.3%, infinity, 0.17, 0.78, 574.6, and 92.2%. The results of our study suggest that fundus photography with a hand-held camera and AI-based grading system is a short, simple, and accurate method for the screening and early detection of DR, comparable to clinical examination and fundus photography with a standard camera.

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.

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.

Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study

BACKGROUND

To compare the performance of Medios (offline) and EyeArt (online) artificial intelligence (AI) algorithms for detecting diabetic retinopathy (DR) on images captured using fundus-on-smartphone photography in a remote outreach field setting.

METHODS

In June, 2019 in the Yucatan Peninsula, 248 patients, many of whom had chronic visual impairment, were screened for DR using two portable Remidio fundus-on-phone cameras, and 2130 images obtained were analyzed, retrospectively, by Medios and EyeArt. Screening performance metrics also were determined retrospectively using masked image analysis combined with clinical examination results as the reference standard.

RESULTS

A total of 129 patients were determined to have some level of DR; 119 patients had no DR. Medios was capable of evaluating every patient with a sensitivity (95% confidence intervals [CIs]) of 94% (88%-97%) and specificity of 94% (88%-98%). Owing primarily to photographer error, EyeArt evaluated 156 patients with a sensitivity of 94% (86%-98%) and specificity of 86% (77%-93%). In a head-to-head comparison of 110 patients, the sensitivities of Medios and EyeArt were 99% (93%-100%) and 95% (87%-99%). The specificities for both were 88% (73%-97%).

CONCLUSIONS

Medios and EyeArt AI algorithms demonstrated high levels of sensitivity and specificity for detecting DR when applied in this real-world field setting. Both programs should be considered in remote, large-scale DR screening campaigns where immediate results are desirable, and in the case of EyeArt, online access is possible.

Accuracy of Low-Cost, Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening: A Systematic Review

Purpose

Diabetic retinopathy (DR) is a leading cause of blindness. Early DR screening is essential, but the infrastructure can be less affordable in low resource countries. This study aims to review the accuracy of low-cost smartphone-based fundus cameras for DR screening in adult patients with diabetes.

Methods

We performed a systematic literature search to find studies that reported the sensitivity and specificity of low-cost smartphone-based devices for fundus photography in adult patients with diabetes. We searched three databases (MEDLINE, Google Scholar, Scopus) and one register (Cochrane CENTRAL). We presented the accuracy values by grouping the diagnosis into three: any DR, referrable DR, and diabetic macular oedema (DMO). Risk of bias and applicability of the studies were assessed using QUADAS-2.

Results

Five out of 294 retrieved records were included with a total of six smartphone-based devices reviewed. All of the reference diagnostic methods used in the included studies were either indirect ophthalmoscopy or slit-lamp examinations and all smartphone-based devices' imaging protocols used mydriatic drops. The reported sensitivity and specificity for any DR were 52-92.2% and 73.3-99%; for referral DR were 21-91.4% and 64.9-100%; and for DMO were 29.4-81% and 95-100%, respectively.

Conclusion

Sensitivity available low-cost smartphone-based devices for DR screening were acceptable and their specificity particularly for detecting referrable DR and DMO were considerably good. These findings support their potential utilization for DR screening in a low resources setting.

Reducing the Cost of Diabetes Care with Telemedicine, Smartphone, and Home Monitoring

The effect of an increasing diabetes population has resulted in escalated costs and overburdened physicians. The increase in cost is not due to the disease per se, but because of its largely preventable complications. Patient-friendly technologies are proven to significantly reduce complications and thereby cost, but seldom practised. Telemedicine is increasingly being utilized in diabetology to improve access to health care, quality of care, and clinical/psychosocial outcomes in patients with diabetes (PWD). In PWD, patient-physician interactions are essential for improving health outcomes and preventing long-term complications. Smartphones are one of the basic modalities for telemedicine application. Mobile phone messaging applications, including text messaging and multimedia message service, could offer a convenient and cost-effective way to support desirable health behaviors. There are diabetes-related mobile apps mainly focusing on self-management of diabetes, lifestyle modification, and medication adherence motivation. With the widespread availability of high-speed Internet, remote monitoring has also become popular. Home monitoring of blood glucose and blood pressure, wearable devices, and continuous glucose monitoring also play a vital role in bringing down the long‑term vascular complications of diabetes and thereby reduce the overall cost and improve the quality of life of patients. There are hundreds of tech platforms for diabetes management, of which only a few with proven efficacy and safety are recommended by physicians.

Agreement of a Novel Artificial Intelligence Software With Optical Coherence Tomography and Manual Grading of the Optic Disc in Glaucoma

PRCIS

The offline artificial intelligence (AI) on a smartphone-based fundus camera shows good agreement and correlation with the vertical cup-to-disc ratio (vCDR) from the spectral-domain optical coherence tomography (SD-OCT) and manual grading by experts.

PURPOSE

The purpose of this study is to assess the agreement of vCDR measured by a new AI software from optic disc images obtained using a validated smartphone-based imaging device, with SD-OCT vCDR measurements, and manual grading by experts on a stereoscopic fundus camera.

METHODS

In a prospective, cross-sectional study, participants above 18 years (Glaucoma and normal) underwent a dilated fundus evaluation, followed by optic disc imaging including a 42-degree monoscopic disc-centered image (Remidio NM-FOP-10), a 30-degree stereoscopic disc-centered image (Kowa nonmyd WX-3D desktop fundus camera), and disc analysis (Cirrus SD-OCT). Remidio FOP images were analyzed for vCDR using the new AI software, and Kowa stereoscopic images were manually graded by 3 fellowship-trained glaucoma specialists.

RESULTS

We included 473 eyes of 244 participants. The vCDR values from the new AI software showed strong agreement with SD-OCT measurements [95% limits of agreement (LoA)=-0.13 to 0.16]. The agreement with SD-OCT was marginally better in eyes with higher vCDR (95% LoA=-0.15 to 0.12 for vCDR>0.8). Interclass correlation coefficient was 0.90 (95% CI, 0.88-0.91). The vCDR values from AI software showed a good correlation with the manual segmentation by experts (interclass correlation coefficient=0.89, 95% CI, 0.87-0.91) on stereoscopic images (95% LoA=-0.18 to 0.11) with agreement better for eyes with vCDR>0.8 (LoA=-0.12 to 0.08).

CONCLUSIONS

The new AI software vCDR measurements had an excellent agreement and correlation with the SD-OCT and manual grading. The ability of the Medios AI to work offline, without requiring cloud-based inferencing, is an added advantage.

Diagnostic accuracy of handheld fundus photography: A comparative study of three commercially available cameras

The objective of this study was to compare the sensitivity and specificity of handheld fundus cameras in detecting diabetic retinopathy (DR), diabetic macular edema (DME), and macular degeneration. Participants in the study, conducted at Maharaj Nakorn Hospital in Northern Thailand between September 2018 and May 2019, underwent an ophthalmologist examination as well as mydriatic fundus photography with three handheld fundus cameras (iNview, Peek Retina, Pictor Plus). Photographs were graded and adjudicated by masked ophthalmologists. Outcome measures included the sensitivity and specificity of each fundus camera for detecting DR, DME, and macular degeneration, relative to ophthalmologist examination. Fundus photographs of 355 eyes from 185 participants were captured with each of the three retinal cameras. Of the 355 eyes, 102 had DR, 71 had DME, and 89 had macular degeneration on ophthalmologist examination. The Pictor Plus was the most sensitive camera for each of the diseases (73-77%) and also achieved relatively high specificity (77-91%). The Peek Retina was the most specific (96-99%), although in part due to its low sensitivity (6-18%). The iNview had slightly lower estimates of sensitivity (55-72%) and specificity (86-90%) compared to the Pictor Plus. These findings demonstrated that the handheld cameras achieved high specificity but variable sensitivities in detecting DR, DME, and macular degeneration. The Pictor Plus, iNview, and Peek Retina would have distinct advantages and disadvantages when applied for utilization in tele-ophthalmology retinal screening programs.

Evaluation of a New Neural Network Classifier for Diabetic Retinopathy

BACKGROUND

Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of color fundus photographs. By applying the net-work to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME), we collect sufficient information to classify patients by grades R0 and R1 or above, M0 and M1.

METHODS

The AI grading system was trained on screening data to evaluate the presence of DR and DME. The core algorithm of the system is a deep learning network that segments relevant anatomical features in a retinal image. Patients were graded according to the standard NHS Diabetic Eye Screening Program feature-based grading protocol.

RESULTS

The algorithm performance was evaluated with a series of 6,981 patient retinal images from routine diabetic eye screenings. It correctly predicted 98.9% of retinopathy events and 95.5% of maculopathy events. Non-disease events prediction rate was 68.6% for retinopathy and 81.2% for maculopathy.

CONCLUSION

This novel deep learning model was trained and tested on patient data from annual diabetic retinopathy screenings can classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without running a long AI training procedure. The incorporation of the AI grading system can increase the graders' productivity and improve the final outcome accuracy of the screening process.

The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases

In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.

Validity of smartphone-based retinal photography (PEEK-retina) compared to the standard ophthalmic fundus camera in diagnosing diabetic retinopathy in Uganda: A cross-sectional study

Introduction

Diabetic retinopathy (DR) is one of the major complications of diabetes mellitus and is a significant cause of blindness worldwide. In Uganda, the prevalence of diabetes is approximately 2.7% of the urban population and 1% in rural areas. Many diabetics cannot access an eye exam due to the lack of less costly and user-friendly equipment that primary eye workers can use. Smartphone-based fundus photography allows for a cheap and mobile fundus examination. The study aimed to determine the sensitivity and specificity of the Portable Eye Examination Kit (PEEK) retina compared to a standard ophthalmic fundus camera (Zeiss Visucam 200) for the diagnosis of DR.

Methods

From January-March 2020, 286 people with diabetes (type 1 & 2) patients were seen at Kiruddu National referral hospital diabetes clinic. All participants had funduscopy with PEEK retina and the standard ophthalmic fundus camera following ophthalmic examination and pupillary dilation. The PEEK retina’s sensitivity, specificity and reliability were determined using an ophthalmic fundus camera as the gold standard.

Results

The participants’ mean age was 51 with a standard deviation of ±11years, 213 (74.5%) were females, and the majority (93.4%) had Type 2 diabetes. The overall Sensitivity of PEEK retina for DR was 84% (95% CI 70.9–83.5), while the specificity was 79.9% (95% CI 76–83.5) with a positive predictive value (PPV) of 30.9% (95% CI 23.2–39.4) and a negative predictive value (NPV) of 97.9% (95% CI 95.9–99.1).

Conclusions

PEEK retina has high sensitivity and specificity, making it suitable for screening and diagnostic purposes. Therefore, we recommend the integration of the PEEK retina in the screening and diagnosis of DR in resource-limited settings.

Tele-Ophthalmology Versus Face-to-Face Retinal Consultation for Assessment of Diabetic Retinopathy in Diabetes Care Centers in India: A Multicenter Cross-Sectional Study

Aim: To evaluate the effectiveness of tele-ophthalmology (TO) versus face-to-face screening for diabetic retinopathy (DR) in diabetes care centers (DCC) across India. Methods: This is an observational, multicenter, retrospective, cross-sectional study of DR screening in individuals with diabetes performed across 35 branches of a chain of DCC in 20 cities in India over 1 year. In 30 DCC, DR screening was performed by TO, where retinal images obtained using Fundus on Phone camera were uploaded through the telemedicine network for centralized DR grading by eight retina specialists. In five DCC, DR screening was performed by fundus examination (FE) by the same retina specialists. The rate of detection of sight-threatening DR (STDR) (defined as the presence of proliferative DR and/or diabetic macular edema) through the two modes was compared. Results: A total of 58,612 individuals were screened for DR from January 1, 2018 to December 31, 2018: 25,316 by TO and 33,296 by FE. The mean age and mean duration of diabetes of the individuals with diabetes screened by TO was 55.8 ± 11.2 years and 9.5 ± 7.3 years; and in individuals screened by FE, it was 57.5 ± 11.6 years and 11.5 ± 8.0 years respectively. The mean glycated hemoglobin was 8.8% ± 2.1% and 8.5% ± 1.9% in the two groups, respectively. Any DR was detected in 31.7% (95% confidence interval [CI]: 31.0-32.3) by tele-screening and in 38.5% (95% CI: 37.9-39.0) by FE, whereas STDR was detected in 7.3% (95% CI: 7.0-7.7) by TO and in 10.5% (95% CI: 10.2-10.9) by FE. Overall, 11.4% individuals with diabetes in the TO group, including 4.1% with ungradable images, were advised referral to retina specialists for further management. Conclusion: Screening for DR at DCC using TO is feasible and effective for STDR detection in India and may be adopted throughout India.

Comparison of Handheld Retinal Imaging with ETDRS 7-Standard Field Photography for Diabetic Retinopathy and Diabetic Macular Edema

PURPOSE

To compare nonmydriatic (NM) and mydriatic (MD) handheld retinal imaging with standard ETDRS 7-field color fundus photography (ETDRS photographs) for the assessment of diabetic retinopathy (DR) and diabetic macular edema (DME).

DESIGN

Prospective, comparative, instrument validation study.

SUBJECTS

A total of 225 eyes from 116 patients with diabetes mellitus.

METHODS

Following a standardized protocol, NM and MD images were acquired using handheld retinal cameras (NM images: Aurora, Smartscope, and RetinaVue-700; MD images: Aurora, Smartscope, RetinaVue-700, and iNview) and dilated ETDRS photographs. Grading was performed at a centralized reading center using the International Clinical Classification for DR and DME. Kappa statistics (simple [K], weighted [Kw]) assessed the level of agreement for DR and DME. Sensitivity and specificity were calculated for any DR, referable DR (refDR), and vision-threatening DR (vtDR).

MAIN OUTCOME MEASURES

Agreement for DR and DME; sensitivity and specificity for any DR, refDR, and vtDR; ungradable rates.

RESULTS

Severity by ETDRS photographs: no DR, 33.3%; mild nonproliferative DR, 20.4%; moderate DR, 14.2%; severe DR, 11.6%; proliferative DR, 20.4%; no DME, 68.0%; DME, 9.3%; non-center involving clinically significant DME, 4.9%; center-involving clinically significant DME, 12.4%; and ungradable, 5.3%. For NM handheld retinal imaging, Kw was 0.70 to 0.73 for DR and 0.76 to 0.83 for DME. For MD handheld retinal imaging, Kw was 0.68 to 0.75 for DR and 0.77 to 0.91 for DME. Thresholds for sensitivity (0.80) and specificity (0.95) were met by NM images acquired using Smartscope and MD images acquired using Aurora and RetinaVue-700 cameras for any DR and by MD images acquired using Aurora and RetinaVue-700 cameras for refDR. Thresholds for sensitivity and specificity were met by MD images acquired using Aurora and RetinaVue-700 for DME. Nonmydriatic and MD ungradable rates for DR were 15.1% to 38.3% and 0% to 33.8%, respectively.

CONCLUSIONS

Following standardized protocols, NM and MD handheld retinal imaging devices have substantial agreement levels for DR and DME. With mydriasis, not all handheld retinal imaging devices meet standards for sensitivity and specificity in identifying any DR and refDR. None of the handheld devices met the established 95% specificity for vtDR, suggesting that lower referral thresholds should be used if handheld devices must be utilized. When using handheld devices, the ungradable rate is significantly reduced with mydriasis and DME sensitivity thresholds are only achieved following dilation.

Evaluation of a remote telemedicine platform using a novel handheld fundus camera: Physician and patient perceptions from real-world experience

BACKGROUND

Although teleophthalmology has gained traction in recent years, it is at the center of the coronavirus disease pandemic. However, most hospitals are not ready owing to a severe lack of real-world experience. Furthermore, a limited number of studies have evaluated telemedicine applications on remote islands. This study aimed to evaluate real-world clinical and referral accuracy, image quality, physician-perceived diagnostic certainty, and patient satisfaction with telemedicine eye screening using a novel handheld fundus camera in a rural and medically underserved population.

METHODS

This prospective study included 176 eyes from a remote island. All participants underwent a comprehensive ophthalmic examination. Nonmydriatic retinal images obtained using a handheld fundus camera were reviewed by two retinal specialists to determine image quality, diagnosis, and need for referrals. The agreement of diagnosis between image-based assessments was compared with that of binocular indirect ophthalmoscopic assessments.

RESULTS

Image quality of fundus photographs was considered acceptable or ideal in 97.7% and 95.5% of eyes assessed by two reviewers, respectively. There was considerable agreement in diagnosis between the indirect ophthalmoscopic assessment and image-based assessment by two reviewers (Cohen's kappa = 0.80 and 0.78, respectively). Likewise, substantial agreement was achieved in the referrals. The sensitivity for referable retinopathy from the two reviewers was 78% (95% confidence interval [CI], 57%-91%) and 78% (95% CI, 57%-91%), whereas specificity was 99% (95% CI, 95%-99%] and 98% (95% CI, 93%-99%), respectively. For physicians' perceived certainty of diagnosis, 93.8% and 90.3% were considered either certain or reliable. Overall, 97.4% of participants were satisfied with their experiences and greatly valued the telemedicine services.

CONCLUSION

Novel fundus camera-based telemedicine screening demonstrated high accuracy in detecting clinically significant retinopathy in real-world settings. It achieved high patient satisfaction and physician-perceived certainty in diagnosis with reliable image quality, which may be scaled internationally to overcome geographical barriers under the global pandemic.

Optics and Utility of Low-Cost Smartphone-Based Portable Digital Fundus Camera System for Screening of Retinal Diseases

Purpose: To describe optical principles and utility of inexpensive, portable, non-contact digital smartphone-based camera for the acquisition of fundus photographs for the evaluation of retinal disorders. Methods: The digital camera has a high-quality glass 25 D condensing lens attached to a 21.4-megapixel smartphone camera. The white-emitting LED light of the smartphone at low illumination levels is used to visualize the fundus and limit source reflection. The camera captures a high-definition fundus (5344 × 4016) image on a complementary metal oxide semiconductor (CMO) with an area of 6.3 mm × 4.5 mm. The auto-acquisition mode of the device facilitates the quick capture of the image from continuous video streaming in a fraction of a second. Results: This new smartphone-based camera provides high-resolution digital images of the retina (50° telescopic view) in patients at a fraction of the cost (USD 1000) of established, non-transportable, office-based fundus photography systems. Conclusions: The portable user-friendly smartphone-based digital camera is a useful alternative for the acquisition of fundus photographs and provides a tool for screening retinal diseases in various clinical settings such as primary care clinics or emergency rooms. The ease of acquisition of photographs from a continuously streaming video of fundus obviates the need for a skilled photographer.

Imaging diabetic retinal disease: clinical imaging requirements

Diabetic retinopathy (DR) is a sight-threatening complication of diabetes mellitus (DM) and it contributes substantially to the burden of disease globally. During the last decades, the development of multiple imaging modalities to evaluate DR, combined with emerging treatment possibilities, has led to the implementation of large-scale screening programmes resulting in improved prevention of vision loss. However, not all patients are able to participate in such programmes and not all are at equal risk of DR development and progression. In this review, we discuss the relevance of the currently available imaging modalities for the evaluation of DR: colour fundus photography (CFP), ultrawide-field photography (UWFP), fundus fluorescein angiography (FFA), optical coherence tomography (OCT), OCT angiography (OCTA) and functional testing. Furthermore, we suggest where a particular imaging technique of DR may aid the evaluation of the disease in different clinical settings. Combining information from various imaging modalities may enable the design of more personalized care including the initiation of treatment and understanding the progression of disease more adequately.

Your mileage may vary: impact of data input method for a deep learning bone age app's predictions

OBJECTIVE

The purpose of this study was to evaluate agreement in predictions made by a bone age prediction application ("app") among three data input methods.

METHODS

The 16Bit Bone Age app is a browser-based deep learning application for predicting bone age on pediatric hand radiographs; recommended data input methods are direct image file upload or smartphone-capture of image. We collected 50 hand radiographs, split equally among 5 bone age groups. Three observers used the 16Bit Bone Age app to assess these images using 3 different data input methods: (1) direct image upload, (2) smartphone photo of image in radiology reading room, and (3) smartphone photo of image in a clinic.

RESULTS

Interobserver agreement was excellent for direct upload (ICC = 1.00) and for photos in reading room (ICC = 0.96) and good for photos in clinic (ICC = 0.82), respectively. Intraobserver agreement for the entire test set across the 3 data input methods was variable with ICCs of 0.95, 0.96, and 0.57 for the 3 observers, respectively.

DISCUSSION

Our findings indicate that different data input methods can result in discordant bone age predictions from the 16Bit Bone Age app. Further study is needed to determine the impact of data input methods, such as smartphone image capture, on deep learning app performance and accuracy.

Efficacy of artificial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients

AIM

To explore the efficacy of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) patients.

METHODS

Data were obtained from 549 T2DM patients who visited the Fundus Disease Center at Henan Provincial People's Hospital from 2018/10-2020/09. DR identification and grading were conducted by two retina specialists, EyeWisdom®DSS and EyeWisdom®MCS, with ophthalmologist grading as reference standard, efficacy of EyeWisdom was evaluated according to sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS

Ophthalmologists detected 324 DR cases. Among them, there were 43 of mild non-proliferative DR (NPDR), 79 of moderate NPDR, 61 of severe NPDR, and 141 of proliferative DR (PDR). EyeWisdom®DSS detected 337 DR and EyeWisdom®MCS detected 264 DR. Sensitivity and specificity of EyeWisdom®DSS were 91.0%(95 %CI: 87.3%-93.8%) and 81.3% (95 %CI: 75.5%-86.1%), while EyeWisdom®MCS correctly identified 76.2%(95 %CI: 71.1%-80.7%) of patients with DR and 92.4%(95 %CI: 87.9%-95.4%) of patients without DR. EyeWisdom®DSS showed 76.5%(95 %CI: 69.6%-82.3%) sensitivity and 78.4%(95 %CI: 73.7%-82.5%) specificity for detecting NPDR and 64.5%(95 %CI: 56.0%-72.3%) sensitivity and 93.1%(95 %CI: 90.1%-95.3%) specificity for diagnosing PDR.

CONCLUSION

EyeWisdom®DSS is effective in screening for DR, and the accuracy of EyeWisdom®MCS was higher for identifying patients without DR. It is valuable to carry out AI-based DR screening in poorer regions.

Risk and Impact of Severe Acute Respiratory Syndrome Coronavirus 2 Infection on Corneal Transplantation: A Case-Control Study

PURPOSE

The purpose of this study was to evaluate the risk of symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection after corneal transplantation surgery, with cataract surgeries as controls, and the impact of the novel coronavirus disease pandemic in the clinical and surgical complications of corneal transplantation and cataract surgeries.

METHODS

A retrospective matched case-control study of 480 consecutive individuals who underwent surgery at the Bascom Palmer Eye Institute between May 2020 and November 2020. A total of 240 patients who underwent corneal transplantation with tissue obtained from the Florida Lions Eye Bank were age, race, ethnicity, and sex matched with 240 patients who underwent cataract surgery during the same day and by the same surgical team. Only the first corneal transplant or cataract surgery during this period was considered for each individual. All donors and recipients were deemed SARS-CoV-2 negative by a nasopharyngeal polymerase chain reaction test before surgery. Postoperative SARS-CoV-2 infections were defined as previously SARS-CoV-2(-) individuals who developed symptoms or had a positive SARS-CoV-2 polymerase chain reaction test during the first postoperative month.

RESULTS

Mean age, sex, race, and ethnicity were similar between groups. There were no differences between the corneal transplant and cataract groups in the rates of SARS-CoV-2 infection before (5.8% vs. 7.5%, P= 0.6) or after surgery (2.9% vs. 2.9%, P = 1). The rates of postoperative complications did not increase during the pandemic, compared with previously reported ranges.

CONCLUSIONS

In this study, postoperative SARS-CoV-2 infection was similar for individuals undergoing corneal transplantation or cataract surgery. Further research is required to evaluate the transmission of SARS-CoV-2 through corneal tissue.

[Smartphone-based fundus imaging: applications and adapters]

BACKGROUND

Smartphone-based fundus imaging (SBFI) is an innovative and low-cost alternative for color fundus photography. Since the first reports on this topic more than 10 years ago a large number of studies on different adapters and clinical applications have been published.

OBJECTIVE

The aim of this review article is to provide an overview on the development of SBFI and adapters and clinical applications published so far.

MATERIAL AND METHODS

A literature search was performed using the MEDLINE and Science Citation Index Expanded databases without time restrictions.

RESULTS

Overall, 11 adapters were included and compared in terms of exemplary image material, field of view, acquisition costs, weight, software, application range, smartphone compatibility and certification. Previously published SBFI applications are screening for diabetic retinopathy, glaucoma and retinopathy of prematurity as well as the application in emergency medicine, pediatrics and medical education/teaching. Image quality of conventional retinal cameras is in general superior to SBFI. First approaches on automatic detection of diabetic retinopathy through SBFI are promising and the use of automatic image processing algorithms enables the generation of wide-field image montages.

CONCLUSION

SBFI is a versatile, mobile, low-cost alternative to conventional equipment for color fundus photography. In addition, it facilitates the delegation of ophthalmological examinations to assistance personnel in telemedical settings, could simplify retinal documentation, improve teaching, and improve ophthalmological care, particularly in countries with low and middle incomes.

Use of offline artificial intelligence in a smartphone-based fundus camera for community screening of diabetic retinopathy

Purpose:

The aim of the study was to analyse the reliability of an offline artificial intelligence (AI) algorithm for community screening of diabetic retinopathy.

Methods:

A total of 1378 patients with diabetes visiting public dispensaries under the administration of the Municipal Corporation of Greater Mumbai between August 2018 and September 2019 were enrolled for the study. Fundus images were captured by non-specialist operators using a smartphone-based camera covering the posterior pole, including the disc and macula, and the nasal and temporal fields. The offline AI algorithm on the smartphone marked the images as referable diabetic retinopathy (RDR) or non-RDR, which were then compared against the grading by two vitreoretinal surgeons to derive upon the sensitivity and specificity of the algorithm.

Results:

Out of 1378 patients, gradable fundus images were obtained and analysed for 1294 patients. The sensitivity and specificity of diagnosing RDR were 100% (95% CI: 94.72–100.00%) and 89.55% (95% CI: 87.76–91.16%), respectively; the same values for any diabetic retinopathy (DR) were 89.13% (95% CI: 82.71–93.79%) and 94.43% (95% CI: 91.89–94.74%), respectively, with no false-negative results.

Conclusion:

The robustness of the offline AI algorithm was established in this study making it a reliable tool for community-based DR screening.

Utility of a smartphone assisted direct ophthalmoscope camera for a general practitioner in screening of diabetic retinopathy at a primary health care center

Purpose:

To assess the use of smartphone-based direct ophthalmoscope photography for screening of diabetic retinopathy (DR) in known diabetic patients walking into a general practitioner's clinic and referring them to a vitreoretinal specialist for further evaluation and management if required.

Methodos:

The study included 94 eyes of 47 walk-in patients in a general practitioner's OPD who were known to have type 2 diabetes mellitus and were already on treatment for the same.

Results:

The study included 47 patients with diabetes with a mean age of 56.2 ± 9.4 years. The Cohen's kappa values revealed that the diagnosis related to the DR status made using a camera was in substantial agreement with the clinical diagnosis (Kappa value: 0.770). The Cohen's kappa values revealed that the diagnosis related to the DME made using a camera was in moderate agreement with the clinical diagnosis (Kappa value: 0.410). The agreement between the findings of the camera and clinical diagnosis was statistically significant (P < 0.05).

Conclusion:

Direct ophthalmoscope-based smartphone imaging can be a useful tool in the OPD of a general practitioner. These images can be assessed for retinopathy, and patients can be referred to a vitreoretinal specialist for further evaluation and management if needed. Hence, the burden of vision loss due to complications of DR in the rural sector can be abridged.

Various models for diabetic retinopathy screening that can be applied to India

The increased burden of diabetes in India has resulted in an increase in the complications of diabetes including sight-threatening diabetic retinopathy (DR). Visual impairment and blindness due to DR can be prevented by early detection and management of sight-threatening DR. Life-long evaluation by repetitive retinal screening of people with diabetes is an essential strategy as DR has an asymptomatic presentation. Fundus examination by trained ophthalmologists and fundus photography are established modes of screening. Various modes of opportunistic screening have been followed in India. Hospital-based screening (diabetes care/eye care) and community-based screening are the common modes. Tele-ophthalmology programs based on retinal imaging, remote interpretation, and grading of DR by trained graders/ophthalmologists have facilitated greater coverage of DR screening and enabled timely referral of those with sight-threatening DR. DR screening programs use nonmydriatic or mydriatic fundus cameras for retinal photography. Hand-held/smartphone-based fundus cameras that are portable, less expensive, and easy to use in remote places are gaining popularity. Good retinal image quality and accurate diagnosis play an important role in reducing unnecessary referrals. Recent advances like nonmydriatic ultrawide field fundus photography can be used for DR screening, though likely to be more expensive. The advent of artificial intelligence and deep learning has raised the possibility of automated detection of DR. Efforts to increase the awareness regarding DR is essential to ensure compliance to regular follow-up. Cost-effective sustainable models will ensure systematic nation-wide DR screening in the country.

Selfie fundus imaging for diabetic retinopathy screening

Background

Regular screening for retinopathy and timely intervention reduces blindness from diabetes by 90%. Screening is currently dependent on the interpretation of images captured by trained technicians. Inherent barriers of accessibility and affordability with this approach impede widespread success of retinopathy screening programs. Herein, we report our observations on the potential of a novel approach, Selfie Fundus Imaging (SFI), to enhance diabetic retinopathy screening.

Methods

The study was undertaken over a two-month period during COVID 19 lockdown. 60 diabetic patients participated in the study. Retinal images were captured using three different approaches, handheld smartphone-based photographs captured by patients themselves after a short video-assisted training session (SFI group), and smartphone-based photographs captured by a trained technician and photographs taken on desktop conventional digital fundus camera (Gold standard). Sensitivity and kappa statistics was determined for retinopathy and macular oedema grading.

Findings

Mean age of the study participants was 52.4 years ± 9.8 years and 78% were men. Of 120 images captured using SFI, 90% were centred-gradable, 8% were decentred-gradable and 2% were ungradable. 82% patients captured the image within a minute (majority by 31–45 s). The sensitivity of SFI to detect diabetic retinopathy was 88.39%. Agreement between SFI grading and standard fundus photograph grading was 85.86% with substantial kappa (0.77). For the detection of diabetic macular oedema, the agreement between SFI images and standard images was 93.67, with almost perfect kappa (0.91).

Conclusion

Fundus images were captured by patients using SFI without major difficulty and were comparable to images taken by trained specialist. With greater penetrance, advances, and availability of mobile photographic technology, we believe that SFI would positively impact the success of diabetic retinopathy screening programs by breaking the barriers of availability, accessibility, and affordability. SFI could ensure continuation of screening schedules for diabetic retinopathy, even in the face a highly contagious pandemic.

A Novel, Smartphone-Based, Teleophthalmology-Enabled, Widefield Fundus Imaging Device With an Autocapture Algorithm

Purpose

Widefield imaging can detect signs of retinal pathology extending beyond the posterior pole and is currently moving to the forefront of posterior segment imaging. We report a novel, smartphone-based, telemedicine-enabled, mydriatic, widefield retinal imaging device with autofocus and autocapture capabilities to be used by non-specialist operators.

Methods

The Remidio Vistaro uses an annular illumination design without cross-polarizers to eliminate Purkinje reflexes. The measured resolution using the US Air Force target test was 64 line pairs (lp)/mm in the center, 57 lp/mm in the middle, and 45 lp/mm in the periphery of a single-shot retinal image. An autocapture algorithm was developed to capture images automatically upon reaching the correct working distance. The field of view (FOV) was validated using both model and real eyes. A pilot study was conducted to objectively assess image quality. The FOVs of montaged images from the Vistaro were compared with regulatory-approved widefield and ultra-widefield devices.

Results

The FOV of the Vistaro was found to be approximately 65° in one shot. Automatic image capture was achieved in 80% of patient examinations within an average of 10 to 15 seconds. Consensus grading of image quality among three graders showed that 91.6% of the images were clinically useful. A two-field montage on the Vistaro was shown to exceed the cumulative FOV of a seven-field Early Treatment Diabetic Retinopathy Study image.

Conclusions

A novel, smartphone-based, portable, mydriatic, widefield imaging device can view the retina beyond the posterior pole with a FOV of 65° in one shot.

Translational Relevance

Smartphone-based widefield imaging can be widely used to screen for retinal pathologies beyond the posterior pole.

Recently updated global diabetic retinopathy screening guidelines: commonalities, differences, and future possibilities

Diabetic retinopathy (DR) is a global health burden. Screening for sight-threatening DR (STDR) is the first cost-effective step to decrease this burden. We analyzed the similarities and variations between the recent country-specific and the International Council of Ophthalmology (ICO) DR guideline to identify gaps and suggest possible solutions for future universal screening. We selected six representative national DR guidelines, one from each World Health Organization region, including Canada (North America), England (Europe), India (South- East Asia), Kenya (Africa), New Zealand (Western Pacific), and American Academy of Ophthalmology Preferred Practice Pattern (used in Latin America and East Mediterranean). We weighed the newer camera and artificial intelligence (AI) technology against the traditional screening methodologies. All guidelines agree that screening for DR and STDR in people with diabetes is currently led by an ophthalmologist; few engage non-ophthalmologists. Significant variations exist in the screening location and referral timelines. Screening with digital fundus photography has largely replaced traditional slit-lamp examination and ophthalmoscopy. The use of mydriatic digital 2-or 4-field fundus photography is the current norm; there is increasing interest in using non-mydriatic fundus cameras. The use of automated DR grading and tele-screening is currently sparse. Country-specific guidelines are necessary to align with national priorities and human resources. International guidelines such as the ICO DR guidelines remain useful in countries where no guidelines exist. Validation studies on AI and tele-screening call for urgent policy decisions to integrate DR screening into universal health coverage to reduce this global public health burden.

Imaging Modalities Employed in Diabetic Retinopathy Screening: A Review and Meta-Analysis

INTRODUCTION

Urbanization has caused dramatic changes in lifestyle, and these rapid transitions have led to an increased risk of noncommunicable diseases, such as type 2 diabetes. In terms of cost-effectiveness, screening for diabetic retinopathy is a critical aspect in diabetes management. The aim of this study was to review the imaging modalities employed for retinal examination in diabetic retinopathy screening.

METHODS

The PubMed and Web of Science databases were the main sources used to investigate the medical literature. An extensive search was performed to identify relevant articles concerning "imaging", "diabetic retinopathy" and "screening" up to 1 June 2021. Imaging techniques were divided into the following: (i) mydriatic fundus photography, (ii) non-mydriatic fundus photography, (iii) smartphone-based imaging, and (iv) ultrawide-field imaging. A meta-analysis was performed to analyze the performance and technical failure rate of each method.

RESULTS

The technical failure rates for mydriatic and non-mydriatic digital fundus photography, smartphone-based and ultrawide-field imaging were 3.4% (95% CI: 2.3-4.6%), 12.1% (95% CI: 5.4-18.7%), 5.3% (95% CI: 1.5-9.0%) and 2.2% (95% CI: 0.3-4.0%), respectively. The rate was significantly different between all analyzed techniques (p < 0.001), and the overall failure rate was 6.6% (4.9-8.3%; I2 = 97.2%). The publication bias factor for smartphone-based imaging was significantly higher than for mydriatic digital fundus photography and non-mydriatic digital fundus photography (b = -8.61, b = -2.59 and b = -7.03, respectively; p < 0.001). Ultrawide-field imaging studies were excluded from the final sensitivity/specificity analysis, as the total number of patients included was too small.

CONCLUSIONS

Regardless of the type of the device used, retinal photographs should be taken on eyes with dilated pupils, unless contraindicated, as this setting decreases the rate of ungradable images. Smartphone-based and ultrawide-field imaging may become potential alternative methods for optimized DR screening; however, there is not yet enough evidence for these techniques to displace mydriatic fundus photography.

Blue Widefield Images of Scanning Laser Ophthalmoscope Can Detect Retinal Ischemic Areas in Eyes With Diabetic Retinopathy

PURPOSE

To determine whether the hyporeflective areas in the blue images obtained by widefield scanning laser ophthalmoscopy (SLO) correspond to the non-perfused areas (NPAs) in the fluorescein angiograms (FA) in eyes with diabetic retinopathy (DR).

DESIGN

Retrospective observational case series.

METHODS

Ninety patients with diabetes mellitus (DM) were studied. All had undergone multicolor widefield SLO imaging. The NPAs in the FA images and hyporeflective areas in the blue widefield SLO images were examined. The morphology of the retina was determined by optical coherence tomography.

RESULTS

Hyporeflective areas in the blue SLO images were found with a rate of 76.6% in eyes with proliferative DR eyes. In a comparison of the hyporeflective areas of the blue SLO images to the non-perfused areas in the FA images, the appearance and the correspondence in the locations of these two types of images were found, and the rate was highly concordant with a Cohen's kappa value of 0.675.

CONCLUSIONS

The high concordance between the hyporeflective areas in the widefield blue SLO and the NPAs in the FA indicates that widefield blue SLO can be used to identify ischemic retinal areas in eyes with DR without the intravenous injection of any dye.

Clinical Role of Smartphone Fundus Imaging in Diabetic Retinopathy and Other Neuro-retinal Diseases

Purpose: In today's life, many electronic gadgets have the potential to become invaluable health care devices in future. The gadgets in this category include smartphones, smartwatches, and others. Till now, smartphone role has been highlighted on many occasions in different areas, and they continue to possess immense role in clinical documentation, clinical consultation, and digitalization of ocular care. In last one decade, many treatable conditions including diabetic retinopathy, glaucoma, and other pediatric retinal diseases are being imaged using smartphones.Methods: To comprehend this cumulative knowledge, a detailed medical literature search was conducted on PubMed/Medline, Scopus, and Web of Science till February 2021.Results: The included literature revealed a definitive progress in posterior segment imaging. From simple torch light with smartphone examination to present day compact handy devices with artificial intelligence integrated software's have changed the very perspectives of ocular imaging in ophthalmology. The consistently reproducible results, constantly improving imaging techniques, and most importantly their affordable costs have renegotiated their role as effective screening devices in ophthalmology. Moreover, the obtained field of view, ocular safety, and their key utility in non-ophthalmic specialties are also growing.Conclusions: To conclude, smartphone imaging can now be considered as a quick, cost-effective, and digitalized tool for posterior segment screenings, however, their definite role in routine ophthalmic clinics is yet to be established.

Progress of artificial intelligence in diabetic retinopathy screening

Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide, and the limited availability of qualified ophthalmologists restricts its early diagnosis. For the past few years, artificial intelligence technology has developed rapidly and has been applied in DR screening. The upcoming technology provides support on DR screening and improves the identification of DR lesions with a high sensitivity and specificity. This review aims to summarize the progress on automatic detection and classification models for the diagnosis of DR.

Remote screening of diabetic retinopathy using ultra-widefield retinal imaging

AIMS

To study the possibility of constructing a remote interpretation system for retinal images.

METHODS

An ultra-widefield (UWF) retinal imaging device was installed in the internal medicine department specializing in diabetes to obtain fundus images of patients with diabetes. Remote interpretation was conducted at Nagoya City University using a cloud server. The medical data, severity of retinopathy, and frequency of ophthalmologic visits were analyzed.

RESULTS

Four hundred ninety-nine patients (mean age, 62.5 ± 13.4 years) were included. The duration of diabetes in 240 (48.1%) patients was less than 10 years and 433 (86.7%) patients had a hemoglobin (Hb) A1c below 8%. Regarding the retinopathy severity, 360 (72.1%) patients had no diabetic retinopathy (NDR), 63 (12.6%) mild nonproliferative retinopathy (NPDR), 38 (7.64%) moderate NPDR, 13 (2.6%) severe NPDR, and 25 (5.0%) PDR. Two hundred forty-one (48.3%) patients had an ophthalmologic consultation within 1 year, 104 (20.8%) had no history of an ophthalmologic consultation. DR was not present in 86 (82.7%) patients who had never had an ophthalmologic examination, 30 (78.9%) patients with severe NPDR or PDR had had an ophthalmologic visit within 1 year. The frequency of ophthalmic visits was correlated negatively with age, diabetes duration, HbA1c, and severity of retinopathy.

CONCLUSION

Remote interpretation of DR using UWF retinal imaging was useful for retinopathy screening. During the COVID-19 pandemic, a remote screening system that can ensure compulsory social distancing and reduce the number of ophthalmic visits can be a safe system for patients and clinicians.

Learning curve evaluation upskilling retinal imaging using smartphones

Smartphone-based fundus imaging (SBFI) is a low-cost approach for screening of various ophthalmic diseases and particularly suited to resource limited settings. Thus, we assessed how best to upskill alternative healthcare cadres in SBFI and whether quality of obtained images is comparable to ophthalmologists. Ophthalmic assistants and ophthalmologists received a standardized training to SBFI (Heine iC2 combined with an iPhone 6) and 10 training examinations for capturing central retinal images. Examination time, total number of images, image alignment, usable field-of-view, and image quality (sharpness/focus, reflex artifacts, contrast/illumination) were analyzed. Thirty examiners (14 ophthalmic assistants and 16 ophthalmologists) and 14 volunteer test subjects were included. Mean examination time (1st and 10th training, respectively: 2.17 ± 1.54 and 0.56 ± 0.51 min, p < .0001), usable field-of-view (92 ± 16% and 98 ± 6.0%, p = .003) and image quality in terms of sharpness/focus (p = .002) improved by the training. Examination time was significantly shorter for ophthalmologists compared to ophthalmic assistants (10th training: 0.35 ± 0.21 and 0.79 ± 0.65 min, p = .011), but there was no significant difference in usable field-of-view and image quality. This study demonstrates the high learnability of SBFI with a relatively short training and mostly comparable results across healthcare cadres. The results will aid implementing and planning further SBFI field studies.

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

The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.

Diabetic retinopathy screening guidelines in India: All India Ophthalmological Society diabetic retinopathy task force and Vitreoretinal Society of India Consensus Statement

Diabetic retinopathy (DR) is an emerging preventable cause of blindness in India. All India Ophthalmology Society (AIOS) and Vitreo-Retinal Society of India (VRSI) have initiated several measures to improve of DR screening in India. This article is a consensus statement of the AIOS DR task force and VRSI on practical guidelines of DR screening in India. Although there are regional variations in the prevalence of diabetes in India at present, all the States in India should screen their population for diabetes and its complications. The purpose of DR screening is to identify people with sight-threatening DR (STDR) so that they are treated promptly to prevent blindness. This statement provides strategies for the identification of people with diabetes for DR screening, recommends screening intervals in people with diabetes with and without DR, and describes screening models that are feasible in India. The logistics of DR screening emphasizes the need for dynamic referral pathways with feedback mechanisms. It provides the clinical standards required for DR screening and treatment of STDR and addresses the governance and quality assurance (QA) standards for DR screening in Indian settings. Other aspects incorporate education and training, recommendations on Information technology (IT) infrastructure, potential use of artificial intelligence for grading, data capture, and requirements for maintenance of a DR registry. Finally, the recommendations include public awareness and the need to work with diabetologists to control the risk factors so as to have a long-term impact on prevention of diabetes blindness in India.

Clinically useful smartphone ophthalmic imaging techniques

Imaging devices in ophthalmology are numerous, and most of them are sophisticated and specialized for specific regions of the eye. In addition, these are fixed and involve close interaction of the patient and the examiner; therefore, simple, portable and tele facility-imbibed imaging tools can be considered optimal alternatives to routine exercises. In the last 10 years, utility of smartphones in ophthalmology is being continuously explored to unearth their potential benefits. In this direction, a smartphone device with/without simple attachments has been noted to aid in detailed, high-quality imaging of the ocular adnexa, cornea, angle, iris, lens, optic disc, and the retina including its periphery. In addition, such utility has also been extended in strabismology workup and intraocular pressure measurements. Hence, using these clinician friendly tools and techniques or by devising newer and more comprehensive tool kits, ophthalmic care can be well-managed with apt use of technology. Also, the smartphone companies are encouraged to collaborate with the medical experts to endeavor more, and help and serve the people better.

Digital health and diabetes: experience from India

The digitization of healthcare and its usage in the delivery of healthcare have experienced exponential growth across the world in recent times. India's fast-growing diabetes population has been exerting immense pressure on the country's healthcare infrastructure. Various innovative and evolving technologies are converging to impact the trajectory of digital health in diabetes. The diabetes community has been adopting various technologies such as connected glucose meters, continuous glucose monitoring systems, continuous subcutaneous insulin infusion, closed-loop systems, digitalization of health data, and diabetes-related apps for the prevention and management of the condition. India has provided some excellent examples in exploiting the potential of digital transformation in revamping the diabetes ecosystem. Yet, there are still various hurdles in technology development, healthcare delivery, as well as concerns related to data privacy, digital divide, policies by the government, role of stakeholders, attitude, and absorption by healthcare professionals, and hospitals. This article provides an overview of the digital diabetes technologies currently practiced in India and recommends the need for strong technology adaptation and policy interventions for an ideal roadmap of digitalization of diabetes care in the Indian milieu.

Accuracy of the smartphone-based nonmydriatic retinal camera in the detection of sight-threatening diabetic retinopathy

Purpose:

To evaluate the sensitivity and specificity of smartphone-based nonmydriatic (NM) retinal camera in the detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) in a tertiary eye care facility.

Methods:

Patients with diabetes underwent retinal photography with a smartphone-based NM fundus camera before mydriasis and standard 7-field fundus photography with a desktop mydriatic fundus camera after mydriasis. DR was graded using the international clinical classification of diabetic retinopathy system by two retinal expert ophthalmologists masked to each other and to the patient's identity. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to detect DR and STDR by NM retinal imaging were assessed.

Results:

245 people had gradable images in one or both eyes. DR and STDR were detected in 45.3% and 24.5%, respectively using NM camera, and in 57.6% and 28.6%, respectively using mydriatic camera. The sensitivity and specificity to detect any DR by NM camera was 75.2% (95% confidence interval (CI) 68.1–82.3) and 95.2% (95%CI 91.1–99.3). For STDR the values were 82.9% (95% CI 74.0–91.7) and 98.9% (95% CI 97.3–100), respectively. The PPV to detect any DR was 95.5% (95% CI 89.8–98.5) and NPV was 73.9% (95% CI 66.4–81.3); PPV for STDR detection was 96.7% (95% CI 92.1–100)) and NPV was 93.5% (95% CI 90.0–97.1).

Conclusion:

Smartphone-based NM retinal camera had fairly high sensitivity and specificity for detection of DR and STDR in this clinic-based study. Further studies are warranted in other settings.

Protocol on a multicentre statistical and economic modelling study of risk-based stratified and personalised screening for diabetes and its complications in India (SMART India)

INTRODUCTION

The aim of this study is to develop practical and affordable models to (a) diagnose people with diabetes and prediabetes and (b) identify those at risk of diabetes complications so that these models can be applied to the population in low-income and middle-income countries (LMIC) where laboratory tests are unaffordable.

METHODS AND ANALYSIS

This statistical and economic modelling study will be done on at least 48 000 prospectively recruited participants aged 40 years or above through community screening across 20 predefined regions in India. Each participant will be tested for capillary random blood glucose (RBG) and complete a detailed health-related questionnaire. People with known diabetes and all participants with predefined levels of RBG will undergo further tests, including point-of-care (POC) glycated haemoglobin (HbA1c), POC lipid profile and POC urine test for microalbuminuria, retinal photography using non-mydriatic hand-held retinal camera, visual acuity assessment in both eyes and complete quality of life questionnaires. The primary aim of the study is to develop a model and assess its diagnostic performance to predict HbA1c diagnosed diabetes from simple tests that can be applied in resource-limited settings; secondary outcomes include RBG cut-off for definition of prediabetes, diagnostic accuracy of cost-effective risk stratification models for diabetic retinopathy and models for identifying those at risk of complications of diabetes. Diagnostic accuracy inter-tests agreement, statistical and economic modelling will be performed, accounting for clustering effects.

ETHICS AND DISSEMINATION

The Indian Council of Medical Research/Health Ministry Screening Committee (HMSC/2018-0494 dated 17 December 2018 and institutional ethics committees of all the participating institutions approved the study. Results will be published in peer-reviewed journals and will be presented at national and international conferences.

TRIAL REGISTRATION NUMBER

ISRCTN57962668 V1.0 24/09/2018.

Medios- An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy

Purpose:

An observational study to assess the sensitivity and specificity of the Medios smartphone-based offline deep learning artificial intelligence (AI) software to detect diabetic retinopathy (DR) compared with the image diagnosis of ophthalmologists.

Methods:

Patients attending the outpatient services of a tertiary center for diabetes care underwent 3-field dilated retinal imaging using the Remidio NM FOP 10. Two fellowship-trained vitreoretinal specialists separately graded anonymized images and a patient-level diagnosis was reached based on grading of the worse eye. The images were subjected to offline grading using the Medios integrated AI-based software on the same smartphone used to acquire images. The sensitivity and specificity of the AI in detecting referable DR (moderate non-proliferative DR (NPDR) or worse disease) was compared to the gold standard diagnosis of the retina specialists.

Results:

Results include analysis of images from 297 patients of which 176 (59.2%) had no DR, 35 (11.7%) had mild NPDR, 41 (13.8%) had moderate NPDR, and 33 (11.1%) had severe NPDR. In addition, 12 (4%) patients had PDR and 36 (20.4%) had macular edema. Sensitivity and specificity of the AI in detecting referable DR was 98.84% (95% confidence interval [CI], 97.62–100%) and 86.73% (95% CI, 82.87–90.59%), respectively. The area under the curve was 0.92. The sensitivity for vision-threatening DR (VTDR) was 100%.

Conclusion:

The AI-based software had high sensitivity and specificity in detecting referable DR. Integration with the smartphone-based fundus camera with offline image grading has the potential for widespread applications in resource-poor settings.

Present and Future of Digital Health in Diabetes and Metabolic Disease

The use of information and communication technology (ICT) in medical and healthcare services goes beyond everyday life. Expectations of a new medical environment, not previously experienced by ICT, exist in the near future. In particular, chronic metabolic diseases such as diabetes and obesity, have a high prevalence and high social and economic burden. In addition, the continuous evaluation and monitoring of daily life is important for effective treatment and management. Therefore, the wide use of ICTbased digital health systems is required for the treatment and management of these diseases. In this article, we compiled a variety of digital health technologies introduced to date in the field of diabetes and metabolic diseases.

Portable, non-invasive video imaging of retinal blood flow dynamics

Retinal blood flow (RBF) information has the potential to offer insight into ophthalmic health and disease that is complementary to traditional anatomical biomarkers as well as to retinal perfusion information provided by fluorescence or optical coherence tomography angiography (OCT-A). The present study was performed to test the functional attributes and performance of the XyCAM RI, a non-invasive imager that obtains and assesses RBF information. The XyCAM RI was installed and used in two different settings to obtain video recordings of the blood flow in the optic nerve head region in eyes of healthy subjects. The mean blood flow velocity index (BFVi) in the optic disc and in each of multiple arterial and venous segments was obtained and shown to reveal a temporal waveform with a peak and trough that correlates with a cardiac cycle as revealed by a reference pulse oximeter (correlation between respective peak-to-peak distances was 0.977). The intra-session repeatability of the XyCAM RI was high with a coefficient of variation (CV) of 1.84 ± 1.13% across both sites. Artery-vein comparisons were made by estimating, in a pair of adjacent arterial and venous segments, various temporal waveform metrics such as pulsatility index, percent time in systole and diastole, and change in vascular blood volume over a cardiac cycle. All arterial metrics were shown to have significant differences with venous metrics (p < 0.001). The XyCAM RI, therefore, by obtaining repeatable blood flow measurements with high temporal resolution, permits the differential assessment of arterial and venous blood flow patterns in the retina that may facilitate research into disease pathophysiology and biomarker development for diagnostics.

Review of retinal cameras for global coverage of diabetic retinopathy screening

The global burden of diabetes has resulted in an increase in the prevalence of diabetic retinopathy (DR), a microvascular complication of diabetes. Lifelong repetitive screening for DR is essential for early detection and timely management to prevent visual impairment due to the silent sight-threatening disorder. Colour fundus photography (CFP) is helpful for documentation of the retinopathy as well as for counselling the patient. CFP has established roles in DR screening, detection, progression and monitoring of treatment response. DR screening programmes use validated mydriatic or non-mydriatic fundus cameras for retinal imaging and trained image graders identify referable DR. Smartphone-based fundus cameras and handheld fundus cameras that are cost-effective, portable and easy to handle in remote places are gaining popularity in recent years. The images captured with these low-cost devices can be immediately sent to trained ophthalmologists for grading of DR. Recent increase in numbers of telemedicine programmes based on imaging with digital fundus cameras and remote interpretation has facilitated larger population coverage of DR screening and timely referral of those with sight-threatening DR to ophthalmologists. Good-quality retinal imaging and accurate diagnosis are essential to reduce inappropriate referrals. Advances in digital imaging such as ultra-wide field imaging and multi-modal imaging have opened new avenues for assessing DR. Fundus cameras with integrated artificial intelligence (AI)-based automated algorithms can also provide instant DR diagnosis and reduce the burden of healthcare systems. We review the different types of fundus cameras currently used in DR screening and management around the world.