Applications of Artificial Intelligence for Retinopathy of Prematurity Screening

Campbell, J Peter, Singh, Praveer, Redd, Travis , Brown, James M., Shah, Parag K, Subramanian, Prema, Rajan, Renu, Valikodath, Nita, Cole, Emily, Ostmo, Susan, Chan, R V Paul, Venkatapathy, Narendran, Chiang, Michael F and Kalpathy-Cramer, Jayashree (2021) Applications of Artificial Intelligence for Retinopathy of Prematurity Screening. Pediatrics, 147 (3). e2020016618. ISSN 0031-4005

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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: The area under the receiver operating characteristic curve for detection of treatment requiring retinopathy of prematurity was 0.98, with 100% sensitivity and 78% specificity. We found higher median (interquartile range) ROP severity in NCUs without oxygen blenders and pulse oxygenation monitors, most apparent in bigger infants (.1500 g and 31 weeks’ gestation: 2.7 [2.5–3.0] vs 3.1 [2.4–3.8]; P = .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.

Keywords:deep learning, retinopathy of prematurity, fundus image analysis, artificial intelligence
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
B Subjects allied to Medicine > B500 Ophthalmics
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:46550
Deposited On:13 Oct 2021 08:42

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