Diabetic Retinopathy Screening in Patients with Diabetes Using a Handheld Fundus Camera: The Experience from the South-Eastern Region in Hungary

Author:

Eszes Dóra Júlia1,Szabó Dóra Júlia2,Russell Greg3,Lengyel Csaba4,Várkonyi Tamás4,Paulik Edit1,Nagymajtényi László1,Facskó Andrea2,Petrovski Goran5,Petrovski Beáta Éva56ORCID

Affiliation:

1. Department of Public Health, Faculty of Medicine, University of Szeged, Szeged, Hungary

2. Department of Ophthalmology, Szent-Györgyi Albert Clinical Center, Faculty of Medicine, University of Szeged, Szeged, Hungary

3. Eyenuk Inc., Clinical Development, Woodland Hills, CA, USA

4. Department of Medicine, Medical Faculty, Albert Szent-Györgyi Clinical Center, University of Szeged, Szeged, Hungary

5. Center for Eye Research, Department of Ophthalmology, Oslo University Hospital and Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

6. The A. I. Evdokimov Moscow State University of Medicine and Dentistry of the Ministry of Healthcare the Russian Federation, Moscow, Russia

Abstract

Purpose. Diabetic retinopathy (DR) is the leading cause of vision loss among active adults in industrialized countries. We aimed to investigate the prevalence of diabetes mellitus (DM), DR and its different grades, in patients with DM in the Csongrád County, South-Eastern region, Hungary. Furthermore, we aimed to detect the risk factors for developing DR and the diabetology/ophthalmology screening patterns and frequencies, as well as the effect of socioeconomic status- (SES-) related factors on the health and behavior of DM patients. Methods. A cross-sectional study was conducted on adults (>18 years) involving handheld fundus camera screening (Smartscope Pro Optomed, Finland) and image assessment using the Spectra DR software (Health Intelligence, England). Self-completed questionnaires on self-perceived health status (SPHS) and health behavior, as well as visual acuity, HbA1c level, type of DM, and attendance at healthcare services were also recorded. Results. 787 participants with fundus camera images and full self-administered questionnaires were included in the study; 46.2% of the images were unassessable. T1D and T2D were present in 13.5% and 86.5% of the participants, respectively. Among the T1D and T2D patients, 25.0% and 33.5% had DR, respectively. The SES showed significant proportion differences in the T1D group. Lower education was associated with a lower DR rate compared to non-DR (7.7% vs. 40.5%), while bad/very bad perceived financial status was associated with significantly higher DR proportion compared to non-DR (63.6% vs. 22.2%). Neither the SPHS nor the health behavior showed a significant relationship with the disease for both DM groups. Mild nonproliferative retinopathy without maculopathy (R1M0) was detected in 6% and 23% of the T1D and T2D patients having DR, respectively; R1 with maculopathy (R1M1) was present in 82% and 66% of the T1D and T2D groups, respectively. Both moderate nonproliferative retinopathy with maculopathy (R2M1) and active proliferative retinopathy with maculopathy (R3M1) were detected in 6% and 7% of the T1D and T2D patients having DR, respectively. The level of HbA1c affected the attendance at the diabetology screening ( HbA 1 c > 7 % associated with >50% of all quarter-yearly attendance in DM patients, and with 10% of the diabetology screening nonattendance). Conclusion. The prevalence of DM and DR in the studied population in Hungary followed the country trend, with a slightly higher sight-threatening DR than the previously reported national average. SES appears to affect the DR rate, in particular, for T1D. Although DR screening using handheld cameras seems to be simple and dynamic, much training and experience, as well as overcoming the issue of decreased optic clarity is needed to achieve a proper level of image assessability, and in particular, for use in future telemedicine or artificial intelligence screening programs.

Publisher

Hindawi Limited

Subject

Endocrinology,Endocrinology, Diabetes and Metabolism

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Role of Machine and Deep Learning Techniques in Diabetic Retinopathy Detection;Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence;2022-10-21

2. Deep Learning for Diabetic Retinopathy Detection: Challenges and Opportunities;Next Generation Healthcare Informatics;2022

3. Low Serum Magnesium Level Can be a Risk Factor for Retinopathy in Diabetic Patients: A Cross-Sectional Controlled Study;Medical Bulletin of Haseki;2021-09-01

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