Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves

Weaver, John; Milford, Karen; Rickard, Mandy; Logan, Joey; Erdman, Lauren; Viteri, Bernarda; D’Souza, Neeta; Cucchiara, Andy; Skreta, Marta; Keefe, Daniel T.; Shah, Salima; Selman, Antoine Fermin; Fischer, Katherine M.; Weiß, Dana; Long, Christopher J.; Lorenzo, Armando J.; Fan, Yong; Tasian, Greg E.

doi:10.1007/s00467-022-05677-0

Pediatr Nephrol

2022

DOI: 10.1007/s00467-022-05677-0

|View full text |Cite

Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves

John Weaver

Karen Milford

Mandy Rickard

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Assessment of the Landscape and Current Utilization

Ahmad,

Khondker,

Kwong

et al. 2024

Curr Treat Options Peds

View full text Add to dashboard Cite

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

hi@scite.ai

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

Henderson, NV 89052, USA

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.

Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves

Cited by 5 publications

References 28 publications

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Assessment of the Landscape and Current Utilization

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Assessment of the Landscape and Current Utilization

Clinical prediction models in children that use repeated measurements with time-varying covariates: a scoping review

Deep learning of renal scans in children with antenatal hydronephrosis

Contact Info

Product

Resources

About