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- W4385264096 abstract "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." @default.
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- W4385264096 date "2023-01-01" @default.
- W4385264096 modified "2023-10-12" @default.
- W4385264096 title "Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the Literature" @default.
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- W4385264096 doi "https://doi.org/10.1159/000531441" @default.
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