从激光启动时间到石治疗持续时间:通过"脏石头计算器"提高石治疗持续时间的预测,使用人工智能
Frédéric Panthier1,2,3,4,5, Laurent Berthe6, Chady Ghnatios7,8
1Department of Urology, Westmoreland Street Hospital, and Clinical Microbiology, UCLH NHS Foundation Trust, London, UK. fredericpanthier@gmail.com.
World journal of urology
|June 26, 2025
概括
机器学习显著改善了结石激光石化术的持续时间估计. 这种人工智能方法减少了估计和有效激光石化术持续时间之间的差异,有助于手术规划.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 结石计算器 (KSC) 估计了灵活尿路透镜的激光石治疗持续时间 (eLD).
- 目前的KSC准确性显示,估计和有效激光石术持续时间 (EfLD) 之间的平均差异为18.8%.
- 研究了机器学习 (ML),以提高KSC的eLD预测的准确性.
研究的目的:
- 通过机器学习模型来减少估计和有效激光石术持续时间 (eLD-EfLD) 之间的差异.
- 为了确定影响激光石术持续时间估计准确性的关键临床和外科变量.
主要方法:
- 分析了125名患者的前性多中心数据.
- 多重线性回归 (MLR) 确定了影响eLD-EfLD差异的显著变量.
- 训练和测试了六种机器学习模型,以最大限度地降低 eLD 和 EfLD 之间的平均绝对误差 (MAE).
主要成果:
- 包括石头位置,外科专业知识和激光设置在内的14个变量显著影响了eLD-EfLD差异.
- 贝叶斯的"自动相关性确定"模型在测试组中实现了最低的平均绝对误差 (3.9%).
- 与原来的KSC相比,机器学习模型将ELD-EfLD差异减少了五倍.
结论:
- 机器学习和人工智能可以显著提高激光石治疗中激光石持续时间估计的准确性.
- 结合外科技术和专业知识等临床因素,可以提高预测准确度.
- 建议进行进一步的临床验证,以支持这些人工智能驱动的发现的外部适用性.
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