Table_1_基于临床、激素（实验室）和影像数据的机器学习模型诊断中枢性性早熟Meta分析

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• 源站标题：Table_1_Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data.docx
• DOI：10.3389/fendo.2024.1353023.s001
• 发布日期：2024-03-25
• 文件数：1 个
• 文件总体积：31.50 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Meta-analysis_of_machine_learning_models_for_the_diagnosis_of_central_precocious_puberty_based_on_clinical_hormonal_laboratory_and_imaging_data_docx/25474879

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• Table_1_Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging .docx（31.50 KB）

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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 studies have utilized clinical, hormonal (laboratory) and imaging data-based machine learning (ML) models to identify CPP. However, the results of these studies varied widely and were challenging to directly compare, mainly due to diverse ML methods. Therefore, the diagnostic value of clinical, hormonal (laboratory) and imaging data-based ML models for CPP remains elusive. The aim of this study was to investigate the diagnostic value of ML models based on clinical, hormonal (laboratory) and imaging data for CPP through a meta-analysis of existing studies.
MethodsWe conducted a comprehensive search for relevant English articles on clinical, hormonal (laboratory) and imaging data-based ML models for diagnosing CPP, covering the period from the database creation date to December 2023. Pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) were calculated to assess the diagnostic value of clinical, hormonal (laboratory) and imaging data-based ML models for diagnosing CPP. The I2 test was employed to evaluate heterogeneity, and the source of heterogeneity was investigated through meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test.
ResultsSix studies met the eligibility criteria. The pooled sensitivity and specificity were 0.82 (95% confidence interval (CI) 0.62-0.93) and 0.85 (95% CI 0.80-0.90), respectively. The LR+ was 6.00, and the LR- was 0.21, indicating that clinical, hormonal (laboratory) and imaging data-based ML models exhibited an excellent ability to confirm or exclude CPP. Additionally, the SROC curve showed that the AUC of the clinical, hormonal (laboratory) and imaging data-based ML models in the diagnosis of CPP was 0.90 (95% CI 0.87-0.92), demonstrating good diagnostic value for C

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学术使用请引用 DOI 10.3389/fendo.2024.1353023.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Meta-analysis_of_machine_learning_models_for_the_diagnosis_of_central_precocious_puberty_based_on_clinical_hormonal_laboratory_and_imaging_data_docx/25474879）。