AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings

Importance Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. If detected and treated early, ROP-associated blindness is preventable; however, identifying patients who might respond to treatment requires screening over time, which is challenging in low-resource settings where access to pediatric ophthalmologists and pediatric ocular imaging cameras is limited. Objective To develop and assess the performance of a machine learning algorithm that uses smartphone-collected videos to perform retinal screening for ROP in low-resource settings. Design, Setting, and Participants This diagnostic study used smartphone-obtained videos of fundi in premature neonates with and without ROP in Mexico and Argentina between May 12, 2020, and October 31, 2023. Machine-learning (ML)–driven algorithms were developed to process a video, identify the best frames within the video, and use those frames to determine whether ROP was likely or not. Eligible neonates born with gestational age less than 36 weeks or birth weight less than 1500 g were included on the study. Exposures An ML algorithm applied to a smartphone-obtained video. Main Outcomes and Measures The ML algorithms’ ability to identify high-quality retinal images and classify those images as indicating ROP or not at the frame and patient levels, measured by accuracy, specificity, and sensitivity, compared with classifications from 3 pediatric ophthalmologists. Results A total of 524 videos were collected for 512 neonates with median gestational age of 32 weeks (range, 25-36 weeks) and median birth weight of 1610 g (range, 580-2800 g). The frame selection model identified high-quality retinal images from 397 of 456 videos (87.1%; 95% CI, 84.0%-90.1%) reserved for testing model performance. Across all test videos, 97.4% (95% CI, 96.7%-98.1%) of high-quality retinal images selected by the model contained fundus images. At the frame level, the ROP classifier model had a sensitivity of 76.7% (95% CI, 69.9%-83.5%); at the patient level, the classifier model had a sensitivity of 93.3% (95% CI, 86.4%-100%). At both levels, the model’s sensitivity was higher than that for the panel of pediatric ophthalmologists (frame level: 71.4% [95% CI, 64.1%-78.7%]; patient level: 73.3% [95% CI, 61.0%-85.6%]). Specificity and accuracy were higher for ophthalmologist classification vs the ML model. Conclusions and Relevance In this diagnostic study, a process that used smartphone-collected videos of premature neonates’ fundi to determine whether high-quality retinal images were present had high sensitivity to classify such images as indicating or not indicating ROP but lower specificity and accuracy than ophthalmologist assessment. This process costs a fraction of the current process for retinal image collection and classification and could be used to expand access to ROP screening in low-resource settings, with potential to help prevent the most common cause of preventable childhood blindness.