RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images

Zhou, Hao; Yang, Wenhan; Sun, Limei; Huang, Li; Li, Songshan; Luo, Xiaoling; Jin, Yili; Sun, Wei; Yan, Wenjia; Li, Jing; Ding, Xiaoyan; He, Yao; Xie, Zhi

doi:10.1007/s10278-024-01154-2

J Digit Imaging. Inform. med.

2024

DOI: 10.1007/s10278-024-01154-2

|View full text |Cite

RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images

Hao Zhou,

Wenhan Yang,

Limei Sun

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning

Yang,

Zhou,

Zhang

et al. 2024

Bioengineering

View full text Add to dashboard Cite

Accurate evaluation of retinopathy of prematurity (ROP) severity is vital for screening and proper treatment. Current deep-learning-based automated AI systems for assessing ROP severity do not follow clinical guidelines and are opaque. The aim of this study is to develop an interpretable AI system by mimicking the clinical screening process to determine ROP severity level. A total of 6100 RetCam Ⅲ wide-field digital retinal images were collected from Guangdong Women and Children Hospital at Panyu (PY) and Zhongshan Ophthalmic Center (ZOC). A total of 3330 images of 520 pediatric patients from PY were annotated to train an object detection model to detect lesion type and location. A total of 2770 images of 81 pediatric patients from ZOC were annotated for stage, zone, and the presence of plus disease. Integrating stage, zone, and the presence of plus disease according to clinical guidelines yields ROP severity such that an interpretable AI system was developed to provide the stage from the lesion type, the zone from the lesion location, and the presence of plus disease from a plus disease classification model. The ROP severity was calculated accordingly and compared with the assessment of a human expert. Our method achieved an area under the curve (AUC) of 0.95 (95% confidence interval [CI] 0.90–0.98) in assessing the severity level of ROP. Compared with clinical doctors, our method achieved the highest F1 score value of 0.76 in assessing the severity level of ROP. In conclusion, we developed an interpretable AI system for assessing the severity level of ROP that shows significant potential for use in clinical practice for ROP severity level screening.

show abstract

An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning

Yang,

Zhou,

Zhang

et al. 2024

Bioengineering

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images

Cited by 1 publication

References 50 publications

An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning

An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning

Contact Info

Product

Resources

About