Applications of Artificial Intelligence for Retinopathy of Prematurity Screening
J. Peter Campbell, Praveer Singh, Travis K. Redd, James M. Brown, Parag K. Shah, Prema Subramanian, Renu P. Rajan, Nita Valikodath, Emily Cole, Susan Ostmo, R.V. Paul Chan, Venkatapathy Narendran, Michael F. Chiang, Jayashree Kalpathy–Cramer
Abstract
OBJECTIVES: Childhood blindness from retinopathy of prematurity (ROP) is increasing as a result of improvements in neonatal care worldwide. We evaluate the effectiveness of artificial intelligence (AI)-based screening in an Indian ROP telemedicine program and whether differences in ROP severity between neonatal care units (NCUs) identified by using AI are related to differences in oxygen-titrating capability. METHODS: External validation study of an existing AI-based quantitative severity scale for ROP on a data set of images from the Retinopathy of Prematurity Eradication Save Our Sight ROP telemedicine program in India. All images were assigned an ROP severity score (1-9) by using the Imaging and Informatics in Retinopathy of Prematurity Deep Learning system. We calculated the area under the receiver operating characteristic curve and sensitivity and specificity for treatment-requiring retinopathy of prematurity. Using multivariable linear regression, we evaluated the mean and median ROP severity in each NCU as a function of mean birth weight, gestational age, and the presence of oxygen blenders and pulse oxygenation monitors. RESULTS: = .007, with adjustment for birth weight and gestational age). CONCLUSIONS: Integration of AI into ROP screening programs may lead to improved access to care for secondary prevention of ROP and may facilitate assessment of disease epidemiology and NCU resources.