Assessment of Training Outcomes of Nurse Readers for Diabetic Retinopathy Telescreening: Validation Study

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.

Diabetic Retinopathy Screening Using a Portable Retinal Camera in Vanuatu

Background and Objective

Proof-of-concept study to test the feasibility of using an all-in-one portable retinal camera for the screening of diabetic retinopathy in the Pacific Island of Vanuatu, which has a high rate of diabetes and its associated complications and a dearth of ophthalmologists.

Study Design/Materials and methods

From February 10, 2020, through February 28, 2020, 49 patients with diabetes mellitus from three islands in Vanuatu were recruited to participate in the study. Demographics, basic health data and retinal photography were obtained. A non-mydriatic, handheld camera was used (Volk Pictor Plus).

Results

Eleven participants (24%) had referral-warranted diabetic retinopathy. There was moderately high inter-rater reliability for our dependent variables: referral status (κ = 0.62, 95% CI 0.42-0.83), retinopathy severity (κ = 0.76, 95% CI 0.55-0.96), and clinically significant macular edema (κ = 0.50, 95% CI 0.25-0.74).

Conclusion

Our study confirms that portable handheld cameras can be used to obtain retinal images of sufficient quality for diabetic retinopathy screening even in resource limited environments like Vanuatu. Among this cohort, a relatively high (24%) prevalence of referral-warranted diabetic retinopathy was found in Vanuatu.

Knowledge of Diabetic Retinopathy among Primary Care Nurses Performing Fundus Photography and Agreement with Ophthalmologists on Screening

Diabetic retinopathy (DR) is one of the complications of diabetes mellitus (DM), with macular oedema being one of the leading causes of avoidable blindness among individuals with DM worldwide. Fundus screening is the only method for early detection and treatment. High-quality training programmes for professionals performing primary care screening are essential to produce high-quality images that facilitate accurate lesion identification. This is a two-phase observational, descriptive, and cross-sectional study. The first phase analysed DR knowledge in a sample of nurses. The second phase explored agreement on DR screening between referral ophthalmologists in image assessment (gold standard) and a small group of nurses involved in the previous phase. In phase 1, the agreement rate for screening results was 90%. In phase 2, the overall raw agreement on the screening of fundus photography results between nurses and ophthalmologists was 75% (Cohen's kappa = 0.477; p < 0.001). Agreement on screening with ophthalmologists was moderate, suggesting that implementing a specific training programme for nurse-led imaging screening would help develop this competence among nurses, ensuring a good level of agreement and patient safety and adding value for users, and also for the sustainability of the healthcare system. This study was not registered.

Examining the Role of Telemedicine in Diabetic Retinopathy

With the increasing prevalence of diabetic retinopathy (DR), screening is of the utmost importance to prevent vision loss for patients and reduce financial costs for the healthcare system. Unfortunately, it appears that the capacity of optometrists and ophthalmologists to adequately perform in-person screenings of DR will be insufficient within the coming years. Telemedicine offers the opportunity to expand access to screening while reducing the economic and temporal burden associated with current in-person protocols. The present literature review summarizes the latest developments in telemedicine for DR screening, considerations for stakeholders, barriers to implementation, and future directions in this area. As the role of telemedicine in DR screening continues to expand, further work will be necessary to continually optimize practices and improve long-term patient outcomes.

Complex interventions to implement a diabetic retinopathy care pathway in the public health system in Kerala: the Nayanamritham study protocol

INTRODUCTION

Using a type 2 hybrid effectiveness-implementation design, we aim to pilot a diabetic retinopathy (DR) care pathway in the public health system in Kerala to understand how it can be scaled up to and sustained in the whole state.

METHODS AND ANALYSIS

Currently, there is no systematic DR screening programme in Kerala. Our intervention is a teleophthalmology pathway for people with diabetes in the non-communicable disease registers in 16 family health centres. The planned implementation strategy of the pathway will be developed based on the discrete Expert Recommendations for Implementing Change taxonomy. We will use both quantitative data from a cross-sectional study and qualitative data obtained from structured interviews, surveys and group discussions with stakeholders to report the effectiveness of the DR care pathway and evaluation of the implementation strategy.We will use logistic regression models to assess crude associations DR and sight-threatening diabetic retinopathy and fractional polynomials to account for the form of continuous covariates to predict uptake of DR screening. The primary effectiveness outcome is the proportion of patients in the non-communicable disease register with diabetes screened for DR over 12 months. Other outcomes include cost-effectiveness, safety, efficiency, patient satisfaction, timeliness and equity. The outcomes of evaluation of the implementation strategies include acceptability, feasibility, adoption, appropriateness, fidelity, penetration, costs and sustainability. Addition of more family health centres during the staggered initial phase of the programme will be considered as a sign of acceptability and feasibility. In the long term, the state-wide adoption of the DR care pathway will be considered as a successful outcome of the Nayanamritham study.

ETHICS AND DISSEMINATION

The study was approved by Indian Medical Research Council (2018-0551) dated 13 March 2019. Study findings will be disseminated through scientific publications and the report will inform adoption of the DR care pathway by Kerala state in future.

TRIAL REGISTRATION NUMBER

ISRCTN28942696.

The Role of Telemedicine, In-Home Testing and Artificial Intelligence to Alleviate an Increasingly Burdened Healthcare System: Diabetic Retinopathy

In the presence of the ever-increasing incidence of diabetes mellitus (DM), the prevalence of diabetic eye disease (DED) is also growing. Despite many improvements in diabetic care, DM remains a leading cause of visual impairment in working-age patients. So far, prevention has been the best way to protect vision. The sooner we diagnose DED, the more effective the treatment is. Thus, diabetic retinopathy (DR) screening, especially with imaging techniques, is a method of choice for vision protection. To alleviate the burden of diabetic patients who need ophthalmic care, telemedicine and in-home testing are used, supported by artificial intelligence (AI) algorithms. This is why we decided to evaluate current image teleophthalmology methods used for DR screening. We searched the PubMed platform for papers published over the last 5 years (2015–2020) using the following key words: telemedicine in diabetic retinopathy screening, diabetic retinopathy screening, automated diabetic retinopathy screening, artificial intelligence in diabetic retinopathy screening, smartphone diabetic retinopathy testing. We have included 118 original articles meeting the above criteria, discussing imaging diabetic retinopathy screening methods. We have found that fundus cameras, stable or mobile, are most commonly used for retinal photography, with portable fundus cameras also relatively common. Other possibilities involve the use of ultra-wide-field (UWF) imaging and even optical coherence tomography (OCT) devices for DR screening. Also, the role of smartphones is increasingly recognized in the field. Retinal fundus images are assessed by humans instantly or remotely, while AI algorithms seem to be useful tools facilitating retinal image assessment. The common use of smartphones and availability of relatively cheap, easy-to-use adapters for retinal photographs augmented by AI algorithms make it possible for eye fundus photographs to be taken by non-specialists and in non-medical setting. This opens the way for in-home testing conducted on a much larger scale in the future. In conclusion, based on current DR screening techniques, we can suggest that the future practice of eye care specialists will be widely supported by AI algorithms, and this way will be more effective.