Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.

Journal: Eye (London, England)

PMID: 38918566

Abstract

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.

Authors

Eşay Kıran Yenice

Department of Ophthalmology, University of Health Sciences, Etlik Zübeyde Hanım Maternity and Women's Health Teaching and Research Hospital, Ankara, Turkey. esay_kiran@hotmail.com.
Caner Kara

Department of Ophthalmology, Etlik City Hospital, Ankara, Türkiye.
Cagatay Berke Erdas

Department of Computer Engineering, Baskent University, Ankara, Türkiye.

Keywords

Algorithms Area Under Curve Deep Learning Female Fundus Oculi Gestational Age Humans Infant, Newborn Infant, Premature Male Neural Networks, Computer Retinopathy of Prematurity Retrospective Studies ROC Curve Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (38918566)

Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.

Abstract

Authors

Keywords

External Resources

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