{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T19:42:56Z","timestamp":1793821376691,"version":"4.6.1"},"reference-count":32,"publisher":"S. Karger AG","issue":"5","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neonatology"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>Retinopathy of prematurity (ROP) is a potentially blinding disease in premature neonates that requires a skilled workforce for diagnosis, monitoring, and treatment. Artificial intelligence is a valuable tool that clinicians employ to reduce the screening burden on ophthalmologists and neonatologists and improve the detection of treatment-requiring ROP. Neural networks such as convolutional neural networks and deep learning (DL) systems are used to calculate a vascular severity score (VSS), an important component of various risk models. These DL systems have been validated in various studies, which are reviewed here. Most importantly, we discuss a promising study that validated a DL system that could predict the development of ROP despite a lack of clinical evidence of disease on the first retinal examination. Additionally, there is promise in utilizing these systems through telemedicine in more rural and resource-limited areas. This review highlights the value of these DL systems in early ROP diagnosis. <\/jats:p>","DOI":"10.1159\/000531441","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T06:08:05Z","timestamp":1691129285000},"page":"558-565","source":"Crossref","is-referenced-by-count":13,"title":["Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the 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