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从传统的分数到可解释的AI:一个六种方法的比较框架,用于通过皮肤进行利切除术的失败预测

Ferhat Çoban^1,2, Hüseyin Kutlu³, Bedreddin Kalyenci⁴

¹Faculty of Medicine, Department of Urology, Adıyaman University, Adıyaman, Turkey. coban_ferhat@hotmail.com.

World journal of urology

|November 2, 2025

概括

一个新的机器学习模型准确地预测了穿皮瘤切除术的失败,超过了传统方法. 可解释的AI增强了结石手术的风险分层.

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:

穿皮骨切除术 (PCNL) 是大型结石的标准治疗方法.
现有的预测模型由于线性假设存在局限性.
准确预测PCNL失败对于患者的治疗结果至关重要.

研究的目的:

开发和验证一个机器学习 (ML) 模型与可解释AI (XAI) 预测PCNL失败.
将ML/XAI模型的性能与传统的物流回归和评分系统进行比较.
改善PCNL手术前风险分层.

主要方法:

对287名PCNL患者的回顾性分析.
利用了人口统计,实验室和成像数据.
采用多个特征选择技术和ML算法 (投票分类器).
应用了SHAP和LIME来实现模型的可解释性.
与后勤回归,GSS和CROES评分系统进行性能比较.

主要成果:

与后勤回归 (AUC=0.812) 和评分系统 (AUC=0.615-0.653) 相比,ML投票分类器实现了更高的性能 (AUC=0.839,准确度=84.5%).
通过SHAP分析发现的关键预测因素包括石头到皮肤的距离和石头的长度.
后勤回归确定了多个石头作为风险因素,而石头与皮肤之间的距离作为保护因素.

结论:

开发的ML/XAI模型为预测PCNL故障提供了更高的准确性和可解释性.
这种临床适用的模型增强了泌尿病学中手术前风险分层.
建议在多中心前性研究中进一步验证,以确保可通用性.

关键词:

可解释的人工智能多种方法的多种方法.通过皮肤进行神经质切除术.石头皮肤距离距离的距离

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