Format:
Online-Ressource
ISSN:
1661-7819
Content:
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.
In:
volume:120
In:
number:5
In:
year:2023
In:
pages:558-565
In:
extent:8
In:
Neonatology, Basel ; Freiburg, Br. ; Paris ; London ; New York ; Bangalore ; Bangkok ; Singapore ; Tokyo ; Sydney : Karger, 2007-, 120, Heft 5 (2023), 558-565 (gesamt 8), 1661-7819
Language:
English
URN:
urn:nbn:de:101:1-2023101200352873811164
URL:
https://doi.org/10.1159/000531441
URL:
https://nbn-resolving.org/urn:nbn:de:101:1-2023101200352873811164
URL:
https://d-nb.info/130574389X/34
URL:
https://karger.com/doi/10.1159/000531441
Bookmarklink