人工智能用于客观评估儿科尿液流量计曲线
Ömer Barış Yücel1, Ali Tekin1, Sibel Tiryaki1
1Ege University, Faculty of Medicine, Department of Pediatric Surgery, Division of Pediatric Urology, Izmir, Turkey.
Urology
|August 15, 2025
概括
人工智能 (AI) 和机器学习 (ML) 可以准确地分类尿液流量计曲线,减少儿科泌尿病学的诊断变化. 这项技术提高了对,塔和高原曲线等条件的准确性.
科学领域:
- 儿科泌尿外科 儿科泌尿外科
- 医学成像分析 医学成像分析
- 人工智能在医学中的应用
背景情况:
- 尿流量计曲线的解释是主观的,容易引起观察者之间的变化.
- 尿流量计模式的客观分类对于小儿排泄功能障碍的准确诊断至关重要.
研究的目的:
- 评估AI和ML算法的有效性,客观地分类尿液流量计曲线.
- 评估人工智能的潜力,以减少诊断变异性,提高儿科泌尿流量计的准确性.
主要方法:
- 分析了586个儿科泌尿流量计曲线 (5-17岁) 的数据集.
- 曲线由儿科泌尿病专家标准化和分类.
- 在数据集上训练和验证了YOLOv5×6算法,其性能指标包括准确性,精度,回忆,F1得分和mAP.
主要成果:
- 在专家中观察到高的评价者间一致性 (Fleiss' kappa: 0.948).
- 人工智能模型实现了85.8%的整体准确性,在识别钟形曲线方面取得了96%的成功.
- 对于高原曲线的高精度 (1.00) 和~90%的平均平均精度 (mAP@0.5) 被记录下来.
结论:
- 用人工智能驱动的尿液流量计曲线的分类显示出高精度,并减少了观察者变化.
- 建议对多中心数据集和标准化报告进行进一步的研究,以加强临床整合.
- 人工智能对尿液流量计设备的实时分析具有前景.
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