Currently submitted to: JMIR Research Protocols
Date Submitted: Mar 8, 2025
Open Peer Review Period: Mar 10, 2025 - May 5, 2025
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
AI Approaches for Grading and Assessing Hydronephrosis Severity in Pediatric Patients: Protocol for a Systematic Review and Meta-Analysis
ABSTRACT
Background:
Hydronephrosis is a condition characterized by the swelling of one or both kidneys due to urine buildup, often resulting from an obstruction in the urinary tract. In pediatric patients, grading and assessing the severity of hydronephrosis are crucial for determining appropriate treatment and predicting outcomes.
Objective:
The objective of this study is to systematically review and analyze the use of AI-based models for grading and assessing hydronephrosis severity in pediatric patients, evaluating their methodologies, diagnostic performance, and potential for clinical integration.
Methods:
This systematic review and meta-analysis will systematically search for studies published up to 1 March 2025 in databases including MEDLINE, Cochrane, IEEE Xplore, Scopus, Google Scholar, and Taylor & Francis, as well as grey literature sources like ProQuest, OpenGrey, and conference proceedings. Eligible studies must involve AI-based models for segmentation, classification, or prediction of hydronephrosis severity in patients aged 0–18 years, utilizing imaging modalities such as ultrasound, CT, or MRI. Studies will be assessed for risk of bias using a modified version of QUADAS-2, and a narrative synthesis will be conducted. If sufficient data homogeneity exists, a meta-analysis will be performed using random-effects models.
Results:
The study began in March 2025 with completion expected by July 2025.
Conclusions:
This systematic review and meta-analysis will be the first review to provide insights into the potential of AI in pediatric hydronephrosis assessment, supporting its integration into clinical practice or identifying limitations in its application.
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Copyright
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