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Multinational External Validation of Autonomous Retinopathy of Prematurity Screening
Aaron S Coyner1, Tom Murickan1, Minn A Oh1
1Casey Eye Institute, Oregon Health & Science University, Portland.
Insights
Autonomous artificial intelligence (AI) screening effectively detects more-than-mild ROP (mtmROP) and type 1 ROP in infants. This technology can improve access to ROP screening, especially in low-resource settings, acting as a force multiplier for prevention.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Retinopathy of prematurity (ROP) is a major cause of childhood blindness.
- Significant disparities in ROP outcomes exist between high- and low-income countries due to limited screening access.
Purpose of the Study:
- To evaluate the efficacy of an autonomous AI-based system for detecting more-than-mild ROP (mtmROP) and type 1 ROP.
- To assess the AI's performance in real-world telemedicine settings.
Main Methods:
- A deep learning algorithm was developed to identify mtmROP and type 1 ROP from eye examinations via telemedicine.
- The AI was trained on the i-ROP dataset and validated on external SUNDROP and AECS datasets.
Main Results:
- The AI demonstrated high diagnostic accuracy, with examination-level AUROCs of 0.896-0.920 for mtmROP and 0.982-0.985 for type 1 ROP.
- Sensitivity for mtmROP detection was high (80.8%-83.5%), and all infants with type 1 ROP screened positive (100%).
Conclusions:
- Autonomous AI-based ROP screening is a viable and effective tool for secondary prevention of ROP.
- This technology can serve as a force multiplier, expanding ROP screening accessibility in diverse healthcare settings.
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.
