TY - JOUR
T1 - Diabetic Retinopathy Screening with Automated Retinal Image Analysis in a Primary Care Setting Improves Adherence to Ophthalmic Care
AU - Liu, James
AU - Gibson, Ella
AU - Ramchal, Shawn
AU - Shankar, Vikram
AU - Piggott, Kisha
AU - Sychev, Yevgeniy
AU - Li, Albert S.
AU - Rao, Prabakar K.
AU - Margolis, Todd P.
AU - Fondahn, Emily
AU - Bhaskaranand, Malavika
AU - Solanki, Kaushal
AU - Rajagopal, Rithwick
N1 - Funding Information:
Supported by the National Eye Institute, National Institutes of Health , Bethesda, Maryland (grant no.: K08EY025269 [R.R.]); and Research to Prevent Blindness , Inc, New York, New York (R.R.). The camera and automated screening software used in the study were provided by Eyenuk, Inc. The company was involved in the data collection and management, but otherwise had no role in the design or conduct of this research.
Publisher Copyright:
© 2020 American Academy of Ophthalmology
PY - 2021/1
Y1 - 2021/1
N2 - Purpose: Retinal screening examinations can prevent vision loss resulting from diabetes but are costly and highly underused. We hypothesized that artificial intelligence-assisted nonmydriatic point-of-care screening administered during primary care visits would increase the adherence to recommendations for follow-up eye care in patients with diabetes. Design: Prospective cohort study. Participants: Adults 18 years of age or older with a clinical diagnosis of diabetes being cared for in a metropolitan primary care practice for low-income patients. Methods: All participants underwent nonmydriatic fundus photography followed by automated retinal image analysis with human supervision. Patients with positive or inconclusive screening results were referred for comprehensive ophthalmic evaluation. Adherence to referral recommendations was recorded and compared with the historical adherence rate from the same clinic. Main Outcome Measure: Rate of adherence to eye screening recommendations. Results: By automated screening, 8.3% of the 180 study participants had referable diabetic eye disease, 13.3% had vision-threatening disease, and 29.4% showed inconclusive results. The remaining 48.9% showed negative screening results, confirmed by human overread, and were not referred for follow-up ophthalmic evaluation. Overall, the automated platform showed a sensitivity of 100% (confidence interval, 92.3%–100%) in detecting an abnormal screening results, whereas its specificity was 65.7% (confidence interval, 57.0%–73.7%). Among patients referred for follow-up ophthalmic evaluation, the adherence rate was 55.4% at 1 year compared with the historical adherence rate of 18.7% (P < 0.0001, Fisher exact test). Conclusions: Implementation of an automated diabetic retinopathy screening system in a primary care clinic serving a low-income metropolitan patient population improved adherence to follow-up eye care recommendations while reducing referrals for patients with low-risk features.
AB - Purpose: Retinal screening examinations can prevent vision loss resulting from diabetes but are costly and highly underused. We hypothesized that artificial intelligence-assisted nonmydriatic point-of-care screening administered during primary care visits would increase the adherence to recommendations for follow-up eye care in patients with diabetes. Design: Prospective cohort study. Participants: Adults 18 years of age or older with a clinical diagnosis of diabetes being cared for in a metropolitan primary care practice for low-income patients. Methods: All participants underwent nonmydriatic fundus photography followed by automated retinal image analysis with human supervision. Patients with positive or inconclusive screening results were referred for comprehensive ophthalmic evaluation. Adherence to referral recommendations was recorded and compared with the historical adherence rate from the same clinic. Main Outcome Measure: Rate of adherence to eye screening recommendations. Results: By automated screening, 8.3% of the 180 study participants had referable diabetic eye disease, 13.3% had vision-threatening disease, and 29.4% showed inconclusive results. The remaining 48.9% showed negative screening results, confirmed by human overread, and were not referred for follow-up ophthalmic evaluation. Overall, the automated platform showed a sensitivity of 100% (confidence interval, 92.3%–100%) in detecting an abnormal screening results, whereas its specificity was 65.7% (confidence interval, 57.0%–73.7%). Among patients referred for follow-up ophthalmic evaluation, the adherence rate was 55.4% at 1 year compared with the historical adherence rate of 18.7% (P < 0.0001, Fisher exact test). Conclusions: Implementation of an automated diabetic retinopathy screening system in a primary care clinic serving a low-income metropolitan patient population improved adherence to follow-up eye care recommendations while reducing referrals for patients with low-risk features.
UR - http://www.scopus.com/inward/record.url?scp=85088789743&partnerID=8YFLogxK
U2 - 10.1016/j.oret.2020.06.016
DO - 10.1016/j.oret.2020.06.016
M3 - Article
C2 - 32562885
AN - SCOPUS:85088789743
SN - 2468-6530
VL - 5
SP - 71
EP - 77
JO - Ophthalmology Retina
JF - Ophthalmology Retina
IS - 1
ER -