人工智能在尿病感染风险中的应用:一个范围审查
Davide Campobasso1, Matteo Panizzi2, Valentina Bellini2
1Urology Unit, University Hospital of Parma, Parma, Italy - d.campobasso@virgilio.it.
Minerva urology and nephrology
|June 26, 2024
概括
人工智能 (AI) 在预测尿病患者的传染性并发症方面表现有前途,优于传统方法. 需要进一步验证,以将AI纳入临床实践,以更好地评估风险.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 感染性并发症,包括败血症,对于尿病 (结石) 患者来说是显著的风险.
- 人工智能 (AI) 和机器学习 (ML) 为泌尿病学中的诊断和预后提供了先进的工具,可能会超越当前的诺姆图.
- 本综述侧重于人工智能的应用,用于预测尿病的传染性并发症.
研究的目的:
- 进行对当前人工智能应用进行范围审查,以预测尿病患者的传染性并发症.
- 提供AI模型在这个领域的方法和性能概述.
主要方法:
- 一个系统的范围审查,遵守PRISMA-ScR指南.
- 在Medline,PubMed和Embase数据库中进行文献搜索.
- 包括2021年至2023年间发表的9项相关回顾性研究.
主要成果:
- 四项研究专注于预测尿道感染,两项研究专注于尿路感染,三项研究专注于诊断受感染的结石.
- 使用了各种AI模型,随机森林和人工神经网络与传统的统计分析相比,显示出更高的性能 (更高的AUC,特异性,灵敏度).
- 在这九项研究中,只有两项包括对人工智能模型的外部验证.
结论:
- 人工智能模型在预测尿病的传染性并发症方面表现良好.
- 具有强大的外部验证的前性,多机构研究是必不可少的.
- 需要进一步的研究来确定最佳的AI变量和临床集成模型.
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