Telemedical Diabetic Retinopathy Screening in a Primary Care Setting: Quality of Retinal Photographs and Accuracy of Automated Image Analysis

ABSTRACT

Background

Screening for diabetic eye disease (DED) and general diabetes care is often separate, which leads to delays and low adherence to DED screening recommendations. Thus, we assessed the feasibility, achieved image quality, and possible barriers of telemedical DED screening in a point-of-care general practice setting and the accuracy of an automated algorithm for detection of DED.

Methods

Patients with diabetes were recruited at general practices. Retinal images were acquired using a non-mydriatic camera (CenterVue, Italy) by medical assistants. Images were quality assessed and double graded by two graders. All images were also graded automatically using a commercially available artificial intelligence (AI) algorithm (EyeArt version 2.1.0, Eyenuk Inc.).

Results

A total of 75 patients (147 eyes; mean age 69 years, 96% type 2 diabetes) were included. Most of the patients (51; 68%) preferred DED screening at the general practice, but only twenty-four (32%) were willing to pay for this service. Images of 63 patients (84%) were determined to be evaluable, and DED was diagnosed in 6 patients (8.0%). The algorithm’s positive/negative predictive values (95% confidence interval) were 0.80 (0.28–0.99)/1.00 (0.92–1.00) and 0.75 (0.19–0.99)/0.98 (0.88–1.00) for detection of any DED and referral-warranted DED, respectively.

Overall, the number of referrals was 18 (24%) for manual telemedical assessment and 31 (41%) for the artificial intelligence (AI) algorithm, resulting in a relative increase of referrals by 72% when using AI.

Conclusions

Our study shows that achieved overall image quality in a telemedical GP-based DED screening was sufficient and that it would be accepted by medical assistants and patients in most cases. However, good image quality and integration into existing workflow remain challenging. Based on these findings, a larger-scale implementation study is warranted.

KEYWORDS:

• Diabetic retinopathy screening
• automated image analysis
• deep learning
• image quality grading
• image analysis algorithm
• DR screening
• image quality scale
• diabetic eye disease screening
• diagnostic test accuracy
• primary care
• retinal photography quality
• retinal photographs quality
• EyeArt
• point of care
• general practitioner screening
• disagreement human automatic algorithm

Declaration of interests statement

Else Kröner-Fresenius-Stiftung/German Scholars Organization (EKFS/GSO 16) provided funding to Finger RP, and the BONFOR GEROK Program, Faculty of Medicine, University of Bonn (Grant No. O-137.0028), provided funding to Wintergerst MWM. Eyenuk Inc. provided free automated analyses of digital retinal images for this study. Wintergerst MWM received a travel grant and imaging devices from DigiSight Technologies, was a consultant for and received a grant, travel reimbursements, and imaging devices from Heine Optotechnik, and received honoraria and travel reimbursements from ASKIN & CO GmbH, a grant and travel reimbursements from Berlin-Chemie AG, and imaging devices from D-EYE. Bejan V, Hartmann V, Schnorrenberg M, Bleckwenn M, and Weckbecker K have nothing to disclose. Finger RP was a consultant for Bayer, Novartis, Santen, Opthea, Novelion, Retina Implant, and Oxford Innovation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.