AIMC Topic: Retinopathy of Prematurity
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Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study.
Journal of medical Internet research
Apr 23, 2025
BACKGROUND: Retinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular endothelial growth factor (anti-VEGF) is required to prevent retinal detachment with consequent visi...
Prediction of retinopathy of prematurity development and treatment need with machine learning models.
BMC ophthalmology
Apr 10, 2025
BACKGROUND: To evaluate the effectiveness of machine learning (ML) models in predicting the occurrence of retinopathy of prematurity (ROP) and treatment need.
AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings.
JAMA network open
Apr 1, 2025
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 requi...
Retinal Vascularization Rate Predicts Retinopathy of Prematurity and Remains Unaffected by Low-Dose Bevacizumab Treatment.
American journal of ophthalmology
Mar 5, 2025
PURPOSE: To assess the rate of retinal vascularization derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as predictor of type 1 ROP and characterize the effect of anti-vascular endothelial growth factor (anti...
Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence.
Biomedical physics & engineering express
Nov 21, 2024
Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts ea...
Image Processing, Computer-Assisted
Support Vector Machine
Principal Component Analysis
Fundus Oculi
Retina
Image Interpretation, Computer-Assisted
Retinopathy of Prematurity
Algorithms
Infant, Newborn
Diagnosis, Computer-Assisted
ROC Curve
Humans
Neural Networks, Computer
Artificial Intelligence
Machine Learning
Assessing the Responses of Large Language Models (ChatGPT-4, Claude 3, Gemini, and Microsoft Copilot) to Frequently Asked Questions in Retinopathy of Prematurity: A Study on Readability and Appropriateness.
Journal of pediatric ophthalmology and strabismus
Oct 28, 2024
PURPOSE: To assess the appropriateness and readability of responses provided by four large language models (LLMs) (ChatGPT-4, Claude 3, Gemini, and Microsoft Copilot) to parents' queries pertaining to retinopathy of prematurity (ROP).
Risk factors for the time to development of retinopathy of prematurity in premature infants in Iran: a machine learning approach.
BMC ophthalmology
Aug 23, 2024
BACKGROUND: Retinopathy of prematurity (ROP), is a preventable leading cause of blindness in infants and is a condition in which the immature retina experiences abnormal blood vessel growth. The development of ROP is multifactorial; nevertheless, the...
Morphological Rule-Constrained Object Detection of Key Structures in Infant Fundus Image.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 9, 2024
The detection of optic disc and macula is an essential step for ROP (Retinopathy of prematurity) zone segmentation and disease diagnosis. This paper aims to enhance deep learning-based object detection with domain-specific morphological rules. Based ...
Prediction models for retinopathy of prematurity occurrence based on artificial neural network.
BMC ophthalmology
Aug 5, 2024
INTRODUCTION: Early prediction and timely treatment are essential for minimizing the risk of visual loss or blindness of retinopathy of prematurity, emphasizing the importance of ROP screening in clinical routine.
Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.
Eye (London, England)
Jun 25, 2024
PURPOSE: To provide automatic detection of Type 1 retinopathy of prematurity (ROP), Type 2 ROP, and A-ROP by deep learning-based analysis of fundus images obtained by clinical examination using convolutional neural networks.
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