Mads Fonager Nørgaard1,2, Jakob Grauslund1,2. 1. Department of Ophthalmology, Odense University Hospital, Odense, Denmark. 2. Research Unit of Ophthalmology, Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.
Abstract
PURPOSE: Worldwide ophthalmologists are challenged by the rapid rise in the prevalence of diabetes. Diabetic retinopathy (DR) is the most common complication in diabetes, and possible consequences range from mild visual impairment to blindness. Repetitive screening for DR is cost-effective, but it is also a costly and strenuous affair. Several studies have examined the application of automated image analysis to solve this problem. Large populations are needed to assess the efficacy of such programs, and a standardized and rigorous methodology is important to give an indication of system performance in actual clinical settings. METHODS: In a systematic review, we aimed to identify studies with methodology and design that are similar or replicate actual screening scenarios. A total of 1,231 publications were identified through PubMed, Cochrane Library, and Embase searches. Three manual search strategies were carried out to identify publications missed in the primary search. Four levels of screening identified 7 studies applicable for inclusion. RESULTS: Seven studies were included. The detection of DR had high sensitivities (87.0-95.2%) but lower specificities (49.6-68.8%). False-negative results were related to mild DR with a low risk of progression within 1 year. Several studies reported missed cases of diabetic macular edema. A meta-analysis was not conducted as studies were not suitable for direct comparison or statistical analysis. CONCLUSION: The study demonstrates that despite limited specificity, automated retinal image analysis may potentially be valuable in different DR screening scenarios with a relatively high sensitivity and a substantial workload reduction.
PURPOSE: Worldwide ophthalmologists are challenged by the rapid rise in the prevalence of diabetes. Diabetic retinopathy (DR) is the most common complication in diabetes, and possible consequences range from mild visual impairment to blindness. Repetitive screening for DR is cost-effective, but it is also a costly and strenuous affair. Several studies have examined the application of automated image analysis to solve this problem. Large populations are needed to assess the efficacy of such programs, and a standardized and rigorous methodology is important to give an indication of system performance in actual clinical settings. METHODS: In a systematic review, we aimed to identify studies with methodology and design that are similar or replicate actual screening scenarios. A total of 1,231 publications were identified through PubMed, Cochrane Library, and Embase searches. Three manual search strategies were carried out to identify publications missed in the primary search. Four levels of screening identified 7 studies applicable for inclusion. RESULTS: Seven studies were included. The detection of DR had high sensitivities (87.0-95.2%) but lower specificities (49.6-68.8%). False-negative results were related to mild DR with a low risk of progression within 1 year. Several studies reported missed cases of diabetic macular edema. A meta-analysis was not conducted as studies were not suitable for direct comparison or statistical analysis. CONCLUSION: The study demonstrates that despite limited specificity, automated retinal image analysis may potentially be valuable in different DR screening scenarios with a relatively high sensitivity and a substantial workload reduction.
Authors: Blake M Snyder; Sang Min Nam; Preeyanuch Khunsongkiet; Sakarin Ausayakhun; Thidarat Leeungurasatien; Maxwell R Leiter; Artem Sevastopolsky; Ashlin S Joye; Elyse J Berlinberg; Yingna Liu; David A Ramirez; Caitlin A Moe; Somsanguan Ausayakhun; Robert L Stamper; Jeremy D Keenan Journal: PLoS One Date: 2019-08-08 Impact factor: 3.240
Authors: Pruthvi Raj; Samiksha Singh; Melissa G Lewis; Rajan Shukla; G V S Murthy; Clare Gilbert Journal: Indian J Ophthalmol Date: 2020-02 Impact factor: 1.848
Authors: Paul Nderitu; Joan M Nunez do Rio; Ms Laura Webster; Samantha S Mann; David Hopkins; M Jorge Cardoso; Marc Modat; Christos Bergeles; Timothy L Jackson Journal: Sci Rep Date: 2022-07-01 Impact factor: 4.996
Authors: Rafael Rodriguez-Acuña; Eduardo Mayoral; Manuel Aguilar-Diosdado; Reyes Rave; Beatriz Oyarzabal; Carmen Lama; Ana Carriazo; Maria Asuncion Martinez-Brocca Journal: BMJ Open Diabetes Res Care Date: 2020-10
Authors: Andreia Marisa Penso Pereira; Raul Manuel da Silva Laureano; Fernando Buarque de Lima Neto Journal: BMC Health Serv Res Date: 2021-07-30 Impact factor: 2.655