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- Title
Multinational External Validation of Autonomous Retinopathy of Prematurity Screening.
- Authors
Coyner, Aaron S.; Murickan, Tom; Oh, Minn A.; Young, Benjamin K.; Ostmo, Susan R.; Singh, Praveer; Chan, R. V. Paul; Moshfeghi, Darius M.; Shah, Parag K.; Venkatapathy, Narendran; Chiang, Michael F.; Kalpathy-Cramer, Jayashree; Campbell, J. Peter
- Abstract
Key Points: Question: How does a fully autonomous artificial intelligence system perform in identifying more-than-mild retinopathy of prematurity (mtmROP) and type 1 ROP? Findings: In this diagnostic study, the performance of an artificial intelligence system, which was trained and calibrated using 2530 examinations from 843 infants in the i-ROP study, had more than 80% sensitivity and specificity for mtmROP and 100% sensitivity for type 1 ROP in 2 large external ROP programs (SUNDROP and AECS), with potential physician workload reductions of 80% in both populations. Meaning: While not available for clinical practice settings at this time, these results provide evidence that autonomous ROP screening may be effective in ROP telemedicine programs, without substantial risk of missing severe ROP. This diagnostic study evaluates how well autonomous artificial intelligence–based retinopathy of prematurity screening can detect more-than-mild and type 1 retinopathy of prematurity. Importance: Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening. Objective: To evaluate how well autonomous artificial intelligence (AI)–based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. Design, Setting, and Participants: This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. Exposures: An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. Main Outcomes and Measures: The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels. Results: The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis. Conclusions and Relevance: Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.
- Publication
JAMA Ophthalmology, 2024, Vol 142, Issue 4, p327
- ISSN
2168-6165
- Publication type
Article
- DOI
10.1001/jamaophthalmol.2024.0045