使用人工智能预测膀管外流的结果:使用APPRAISE-AI进行批判性评估
Adree Khondker1,2, Sanchit Kaushal3, Jeremy Wu2
1Division of Urology, The Hospital for Sick Children, Toronto, Ontario, Canada.
PLOS digital health
|February 13, 2026
概括
人工智能 (AI) 在诊断和预测患有膀管外反流 (VUR) 的儿童的结果方面表现有前途. 然而,目前用于VUR的AI模型需要提高质量和标准化,以便有效的临床使用.
科学领域:
- 儿科泌尿外科 儿科泌尿外科
- 人工智能在医学中的应用
- 机器学习应用 机器学习应用
背景情况:
- 膀回流 (VUR) 是儿童常见的一种先天性异常,与反复性尿路感染 (UTI) 和潜在的损伤有关.
- 人工智能 (AI) 为增强VUR诊断,预后和治疗分层提供了新的方法.
研究的目的:
- 审查和评估儿童泌尿器科人工智能应用的质量,特别是用于VUR和UTI管理.
- 确定常见的机器学习方法及其在预测VUR结果方面的有效性.
主要方法:
- 来自AI-PEDURO存储库 (更新于2024年6月) 的研究的叙述性综述,重点关注VUR/UTI的机器学习.
- 十七项研究符合纳入标准,分析了AI在VUR分级,尿路感染复发预测和治疗结果中的应用.
- 研究的质量使用APPRAISE-AI工具进行了评估.
主要成果:
- 常见的人工智能应用包括从成像中对VUR进行分级,预测尿路感染复发,自发VUR解析和治疗结果.
- 神经网络,基于树的算法和支持矢量机器经常被使用.
- 研究质量的中位数为中度,具有良好的临床相关性,但在方法,稳定性和可重复性方面存在弱点.
结论:
- 人工智能具有显著的潜力,可以提高诊断准确度,个性化治疗,并预测儿科VUR的结果.
- 目前大多数VUR人工智能模型质量低至中等.
- 标准化报告和多机构合作对于提高AI模型严谨性和临床翻译至关重要.
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