用于快速查疑似尿路感染的样本的人工神经网络
Cristiano Ialongo1, Marco Ciotti2, Alfredo Giovannelli3,4
1Department of Experimental Medicine, Policlinico Umberto I, 'Sapienza' University, 00161 Rome, Italy.
Antibiotics (Basel, Switzerland)
|August 28, 2025
概括
机器学习模型可以有效选阴性尿样,减少不必要的微生物培养. 人工神经网络在支持尿液分析的临床决策方面表现有前途.
科学领域:
- 临床诊断
- 医学中的人工智能
- 尿液分析
背景情况:
- 尿液微生物分析容易受到污染,导致错误阳性和延迟.
- 数字化和机器学习 (ML) 为临床决策提供了潜在的解决方案.
- 污染导致错误诊断和医疗资源分配效率低下.
研究的目的:
- 研究使用简单的人工神经网络 (ANN) 预先识别阴性和受污染的尿样.
- 开发和评估用于尿样分析的机器学习模型.
- 减少错误的阳性诊断和不必要的微生物培养.
主要方法:
- 使用8181个尿样 (细胞学,测量棒,培养) 开发了一种多层感知子 (MLP) 模型.
- 训练和测试的随机划分数据为 2:1,不包括不重要的变量.
- 使用微生物和尿液颜色/urobilinogen数据作为模型的主要输入.
主要成果:
- 该模型的负预测值 (NPV) 为96.5%,正预测值 (PPV) 为87.2%.
- 污染的样本经常被错误地归类为阴性.
- 确定了0.82%的培养为不必要的微生物培养 (UMC).
结论:
- ANN模型可靠地选阴性尿样,有助于检测显著的细菌尿.
- 这种模式在排除负面结果时有效,但在确认积极结果时不那么有效.
- 通过结合形态数据,可以进一步提高准确性和减少假阴性.
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