一个机器学习算法预测了使用UTD分类的水缩婴儿扩张VUR的风险
Hsin-Hsiao Scott Wang1, Michael Li1, Dylan Cahill2
1Department of Urology, Boston Children's Hospital, Boston, MA, USA.
Journal of pediatric urology
|November 22, 2023
概括
一个机器学习模型可以预测使用早期超声波在产前水缩的婴儿中扩张的膀-尿道逆流 (VUR). 这种工具有助于个性化管理和选择性使用排空囊电图 (VCUG).
科学领域:
- 儿科泌尿外科 儿科泌尿外科
- 医疗成像医学成像
- 医疗保健中的机器学习
背景情况:
- 尿道扩张 (UTD) 的分类旨在客观地分类产前和产后的UTD.
- 目前的UTD分类与VUR等异常存在不清楚的关联,导致主观的管理建议.
研究的目的:
- 开发可靠的机器学习 (ML) 模型,从早期产后超声波中预测VUR.
- 提高产前水缩的婴儿的管理决策的客观性.
主要方法:
- 对患有产前UTD的婴儿 (0-90天) 的放射学和医疗记录的回顾性审查.
- 开发和验证一种ML模型,使用诸如性别,尿道扩张和体外观等特征.
- 主要结局被定义为扩展 (≥Gr3) VUR,通过VCUG证实.
主要成果:
- 该研究包括280名婴儿 (540个脏单位),其中66个单位被诊断为≥3级VUR.
- 最终的ML模型,包括临床和超声波特征,在VUR预测方面达到0.81的AUC.
- 关键预测因素包括性别,尿道扩张,体外观,体厚度和中央体扩张.
结论:
- 机器学习模型可以有效地预测患有水缩症的婴儿早期产后超声波的扩张VUR.
- 这种预测模型支持对患有产前水缩症的儿童的个性化管理策略.
- 该模型有助于更有选择性,更有效地使用排空囊电图 (VCUG).
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