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Urodynamic Studies: Uroflowmetry01:19

Urodynamic Studies: Uroflowmetry

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Uroflowmetry is a non-invasive urodynamic test designed to measure various aspects of urination, including volume, flow rate, and the time to void. This test is crucial for diagnosing and assessing conditions such as bladder outlet obstruction, bladder dysfunction, incomplete bladder emptying, incontinence, and urinary tract blockages caused by benign prostatic hyperplasia (BPH) and urethral strictures.Pre-Test Instructions:Before a uroflowmetry test, patients are typically advised to drink...
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Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
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基于人工智能对儿童尿流量测定模式的分析:机器学习视角

Faruk Arslan1, Omer Algorabi2, Onur Can Ozkan3

  • 1Department of Urology, School of Medicine, Marmara University, Istanbul, Turkey.

Neurourology and urodynamics
|September 5, 2025
PubMed
概括

机器学习模型对患有下泌尿道症状的儿童解释尿流量测量模式具有前景,有可能提高诊断的一致性.

关键词:
人工智能解释上的差异机器学习尿流量计曲线排空模式

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科学领域:

  • 儿童泌尿病学
  • 医疗信息学
  • 医学中的机器学习

背景情况:

  • 尿流量测试 (UF) 是评估儿童下尿道症状 (LUTS) 的关键非侵入性测试.
  • 专家对 UF 排泄模式的解释显示了观察者之间的显著变化.
  • 机器学习 (ML) 为标准化 UF 分析提供了一个潜在的解决方案.

研究的目的:

  • 评估ML模型在解释儿科尿液流量计空气模式中的准确性.
  • 为了比较不同ML算法的UF模式分类性能.

主要方法:

  • 分析了500名患有LUTS的儿童 (4至17岁) 的尿流量测试.
  • 起初,三位儿科泌尿专家解释了排泄模式,并就差异达成共识.
  • 在80%的数据上训练了五个ML模型 (决策树,随机森林,CatBoost,XGBoost,LightGBM),并对20%的数据进行了测试.

主要成果:

  • 最初的专家对UF模式的共识是温和的 (Fleiss' κ = 0.608),37.8%的测试显示出差异.
  • 在分类空洞模式方面,XGBoost模型获得了最高的准确度 (85.00% ± 2.90%).
  • 准确性因模式而异,中断模式显示高准确性 (95%-100%) 和塔/平原模式显示较低准确性 (61.54%-73.08%).

结论:

  • ML模型在解释儿科尿液流量测量模式方面表现出可接受的准确性.
  • 人工智能有可能在儿科泌尿病学中标准化尿液流量测量空模式分析.
  • 进一步的研究可能会导致人工智能辅助的诊断工具用于LUTS评估.