Clinical validation of a smartphone-based retinal camera for diabetic retinopathy screening

Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol

Purpose

To evaluate the performance of artificial intelligence (AI) systems embedded in a mobile, handheld retinal camera, with a single retinal image protocol, in detecting both diabetic retinopathy (DR) and more-than-mild diabetic retinopathy (mtmDR).

Design

Multicenter cross-sectional diagnostic study, conducted at 3 diabetes care and eye care facilities.

Participants

A total of 327 individuals with diabetes mellitus (type 1 or type 2) underwent a retinal imaging protocol enabling expert reading and automated analysis.

Methods

Participants underwent fundus photographs using a portable retinal camera (Phelcom Eyer). The captured images were automatically analyzed by deep learning algorithms retinal alteration score (RAS) and diabetic retinopathy alteration score (DRAS), consisting of convolutional neural networks trained on EyePACS data sets and fine-tuned using data sets of portable device fundus images. The ground truth was the classification of DR corresponding to adjudicated expert reading, performed by 3 certified ophthalmologists.

Main Outcome Measures

Primary outcome measures included the sensitivity and specificity of the AI system in detecting DR and/or mtmDR using a single-field, macula-centered fundus photograph for each eye, compared with a rigorous clinical reference standard comprising the reading center grading of 2-field imaging protocol using the International Classification of Diabetic Retinopathy severity scale.

Results

Of 327 analyzed patients (mean age, 57.0 ± 16.8 years; mean diabetes duration, 16.3 ± 9.7 years), 307 completed the study protocol. Sensitivity and specificity of the AI system were high in detecting any DR with DRAS (sensitivity, 90.48% [95% confidence interval (CI), 84.99%-94.46%]; specificity, 90.65% [95% CI, 84.54%-94.93%]) and mtmDR with the combination of RAS and DRAS (sensitivity, 90.23% [95% CI, 83.87%-94.69%]; specificity, 85.06% [95% CI, 78.88%-90.00%]). The area under the receiver operating characteristic curve was 0.95 for any DR and 0.89 for mtmDR.

Conclusions

This study showed a high accuracy for the detection of DR in different levels of severity with a single retinal photo per eye in an all-in-one solution, composed of a portable retinal camera powered by AI. Such a strategy holds great potential for increasing coverage rates of screening programs, contributing to prevention of avoidable blindness.

Financial Disclosures

F.K.M. is a medical consultant for Phelcom Technologies. J.A.S. is Chief Executive Officer and proprietary of Phelcom Technologies. D.L. is Chief Technology Officer and proprietary of Phelcom Technologies. P.V.P. is an employee at Phelcom Technologies.

Image quality comparison of AirDoc portable retina camera versus eyer in a diabetic retinopathy screening program

BACKGROUND

Diabetic retinopathy (DR) stands as the foremost cause of preventable blindness in adults. Despite efforts to expand DR screening coverage in the Brazilian public healthcare system, challenges persist due to various factors including social, medical, and financial constraints. Our objective was to evaluate the quality of images obtained with the AirDoc, a novel device, compared to Eyer portable camera which has already been clinically validated.

METHODS

Images were captured by two portable retinal devices: AirDoc and Eyer. The included patients had their fundus images obtained in a screening program conducted in Blumenau, Santa Catarina. Two retina specialists independently assessed image's quality. A comparison was performed between both devices regarding image quality and the presence of artifacts.

RESULTS

The analysis included 129 patients (mean age of 61 years), with 29 (43.28%) male and an average disease duration of 11.1 ± 8 years. In Ardoc, 21 (16.28%) images were classified as poor quality, with 88 (68%) presenting artifacts; in Eyer, 4 (3.1%) images were classified as poor quality, with 94 (72.87%) presenting artifacts.

CONCLUSIONS

Although both Eyer and AirDoc devices show potential as screening tools, the AirDoc images displayed higher rates of ungradable and low-quality images, that may directly affect the DR and DME grading. We must acknowledge the limitations of our study, including the relatively small sample size. Therefore, the interpretations of our analyses should be approached with caution, and further investigations with larger patient cohorts are warranted to validate our findings.

Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation

PURPOSE

Diabetic retinopathy (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to ophthalmic care is limited. This study presents a proof of concept for utilizing a portable handheld retinal camera with an embedded artificial intelligence (AI) platform, complemented by a synchronous remote confirmation by retina specialists, for DR screening in an underserved rural area.

DESIGN

Retrospective cohort study.

SUBJECTS

A total of 1115 individuals with diabetes.

METHODS

A retrospective analysis of a screening initiative conducted in 4 municipalities in Northeastern Brazil, targeting the diabetic population. A portable handheld retinal camera captured macula-centered and disc-centered images, which were analyzed by the AI system. Immediate push notifications were sent out to retina specialists upon the detection of significant abnormalities, enabling synchronous verification and confirmation, with on-site patient feedback within minutes. Referral criteria were established, and all referred patients underwent a complete ophthalmic work-up and subsequent treatment.

MAIN OUTCOME MEASURES

Proof-of-concept implementation success.

RESULTS

Out of 2052 invited individuals, 1115 participated, with a mean age of 60.93 years and diabetes duration of 7.52 years; 66.03% were women. The screening covered 2222 eyes, revealing various retinal conditions. Referable eyes for DR were 11.84%, with an additional 13% for other conditions (diagnoses included various stages of DR, media opacity, nevus, drusen, enlarged cup-to-disc ratio, pigmentary changes, and other). Artificial intelligence performance for overall detection of referable cases (both DR and other conditions) was as follows: sensitivity 84.23% (95% confidence interval (CI), 82.63-85.84), specificity 80.79% (95% CI, 79.05-82.53). When we assessed whether AI matched any clinical diagnosis, be it referable or not, sensitivity was 85.67% (95% CI, 84.12-87.22), specificity was 98.86 (95% CI, 98.39-99.33), and area under the curve was 0.92 (95% CI, 0.91-0.94).

CONCLUSIONS

The integration of a portable device, AI analysis, and synchronous medical validation has the potential to play a crucial role in preventing blindness from DR, especially in socially unequal scenarios.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.

A Narrative Review of the Advances in Screening Methods for Diabetic Retinopathy: Enhancing Early Detection and Vision Preservation

Diabetes mellitus (DM) is putting a great burden worldwide. This rise in DM cases, both type 1 and 2, significantly impacts public health. India has grappled with a diabetes epidemic for several years, leading to many misdiagnosed and untreated diabetes cases. Diabetes remains a significant factor in adult-onset blindness despite improvements in diabetes management. This increases the danger of diabetic retinopathy (DR) with permanent loss of sight for those affected. The screening for DR aims to identify those persons with complications arising from diabetes or DR, which could potentially result in blindness, so that treatment can be started immediately and blindness can be avoided. A comprehensive health system approach is required to ensure that the public sector in India effectively screens for DR. Improving patient outcomes and avoiding visual loss depends significantly on early identification and treatment. This article discusses the actions that should be implemented to establish a national effort for systematic DR screening. It also highlights the importance of screening in DR and its impact on treatment effectiveness. Regular screenings enable the early detection of retinopathy, allowing for timely intervention and treatment. Early screening helps prevent complications associated with DR, such as macular edema or retinal detachment. Screening also assists healthcare providers in planning, optimizing treatment approaches, and monitoring treatment effectiveness. Meanwhile, early intervention is essential for enhancing treatment outcomes, thus enhancing the chances of preserving vision and preventing further progression of the disease. This helps in improving the overall management of this sight-threatening complication.