Use of a Deep Learning-Based Disease Severity Scale for Assessment of Retinopathy of Prematurity Primary Prevention in India

Background: There is an epidemic of retinopathy of prematurity (ROP) in low- and middle-income countries (LMIC) in part due to heterogeneity in adherence to best practices for oxygen monitoring in neonatal care units (NCU). The purpose of this study was to determine whether a deep learning based ROP severity score obtained from fundus images in a telemedicine program could be applied at the population level to compare epidemiologic differences between NCUs in South India. Methods: Fundus photos were obtained from at-risk infants at participating hospitals in the Aravind Eye Hospital ROP telemedicine system. An ROP severity score from 1-9 was calculated for each eye examination using methods previously published. Population (NCU) level ROP severity was compared across hospitals using multivariate linear regression adjusting for demographic risk factors for ROP. Masked ROP technicians assessed adherence to best practices using a quality score (1-5) for oxygen monitoring in each NCU. Findings: Images from initial screening exams of 128 infants at 11 hospitals were analyzed. Mean ROP severity was significantly different between hospitals (P=0.003). In multivariate regression controlling for birth weight, gestational age, and postnatal age, a higher NCU quality score was associated with lower ROP severity (P=0.001). Of note, the NCU with the lowest quality score had the highest ROP severity score (1.6 points above the mean, P=0.001), while the NCU with the highest quality score had the lowest ROP severity score (1.2 points below the mean, P=0.001). Interpretation: We identified variability in ROP severity between NCUs that correlated with best practices for oxygen monitoring. These findings suggest the potential utility of using image-based deep learning not just for patient-level diagnosis, but also for population-level epidemiologic surveillance of primary prevention practices, and may have broader applicability for a variety of medical conditions beyond ROP. Funding Statement: This project was supported by grants R01EY19474, K12EY027720, and P30EY10572 from the National Institutes of Health (Bethesda, MD), by grants SCH1622679, SCH-1622542, and SCH-1622536 from the National Science Foundation (Arlington, VA), and by unrestricted departmental funding and a Career Development Award (JPC) from Research to Prevent Blindness (New York, NY). Declaration of Interests: Michael F. Chiang is an unpaid member of the Scientific Advisory Board for Clarity Medical Systems (Pleasanton, CA), a Consultant for Novartis (Basel, Switzerland), and an initial member of Inteleretina (Honolulu, HI). R. V. Paul Chan is on the Scientific Advisory Board for Visunex Medical Systems (Pleasanton, CA), and a Consultant for Genentech (South San Francisco, CA). Michael Chiang and J. Peter Campbell receive research support from Genentech. J. Peter Campbell, James M. Brown, Susan Ostmo, R.V.P. Chan, Jayaskhree Kalpathy-Cramer, and Michael F. Chiang have a patent application pending on the described technology. All other authors have no conflicts of interest. Ethics Approval Statement: This study was conducted in accordance with Health Insurance Portability and Accountability Act (HIPAA) guidelines, and Institutional Review Board (IRB) approval was obtained at both Oregon Health & Science University and the Aravind Eye Hospital.