Year: 2023
Type: article
Study design: Observational Study
Abstract: Diabetes mellitus presents a significant global health care burden with the number of affected persons estimated to be 463 million in 2019. 1 In 2010, 3.7 million persons with diabetic retinopathy (DR) were visually impaired, and 800 000 were blind. 2 The COVID-19 pandemic exposed a need for wider implementation of both tele-medicine and alternative methods of screening patients, such as artificial intelligence (AI).Many patients with diabetes, a high-risk group for COVID-related complications, refused to follow-up when treatment facilities became available due to fear of being exposed to the virus.3,4 Artificial intelligence provides a cost-effective method to enhance the effectiveness of tele-ophthalmology (Tele) to provide necessary screening in a population in which access to in-person clinical examination by an expert is difficult or impossible.5,6 This study assessed the concordance of DR detection via Tele, AI, and the gold standard of an in-person examination by a retinal specialist.Patients who had undergone both an in-person and Tele evaluation of retinal images between April 2021 and June 2021 were identified.Data collected included noninvasive nonmydriatic fundus photos taken by a nonmydriatic Canon CR-2 Plus AF Retinal Imaging camera (Tokyo, Japan), grading results of the fundus photos by a retina specialist via Tele based on the International Clinical Diabetic Retinopathy (ICDR) classification scale, and diagnosis of DR based on ICDR classification scale during an in-person clinic visit in which a dilated fundus exam was performed.The fundus photos were sent for grading by the AI program, EyeArt (EyeNuk, CA), which is designed to grade fundus photos for DR severity (none, mild, or more than mild).The concordance between each grading method was compared using Cohen's Kappa (κ) coefficient.When comparing 1194041D STXXX10.
Institution: Rutgers New Jersey Medical School
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4386251193