Prediction models for retinopathy of prematurity occurrence based on artificial neural network.

Journal: BMC ophthalmology

PMID: 39103779

Abstract

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.

Authors

Rong Wu

Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
He Chen

School of Food and Biological Engineering, Shaanxi University of Science and Technology Xi&#;an, China.
Yichen Bai

Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, No.253 Gongyedadao Middle Road City, Guangzhou, Guangdong, 510282, China.
Yu Zhang

College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
Songfu Feng

Department of Ophthalmology, Zhujiang Hospital of Southern Medical University, Guangzhou, China.
Xiaohe Lu

Department of Ophthalmology, Zhujiang Hospital of Southern Medical University, Guangzhou, China.

Keywords

Algorithms Female Gestational Age Humans Infant, Newborn Male Neonatal Screening Neural Networks, Computer Predictive Value of Tests Retinopathy of Prematurity Retrospective Studies Risk Factors ROC Curve

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View on PubMed Access via DOI PubMed (39103779)

Prediction models for retinopathy of prematurity occurrence based on artificial neural network.

Abstract

Authors

Keywords

External Resources

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