Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data

Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data

BackgroundCentral precocious puberty (CPP) is a common endocrine disorder in children, and its diagnosis primarily relies on the gonadotropin-releasing hormone (GnRH) stimulation test, which is expensive and time-consuming. With the widespread application of artificial intelligence in medicine, some...

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Bibliographic Details
Main Authors: Yilin Chen, Xueqin Huang, Lu Tian
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-03-01
Series:Frontiers in Endocrinology
Subjects:
machine learning
central precocious puberty
meta-analysis
ML
CPP
Online Access:https://www.frontiersin.org/articles/10.3389/fendo.2024.1353023/full

Internet

https://www.frontiersin.org/articles/10.3389/fendo.2024.1353023/full

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