基于膀形态特征的完全端到端的自动膀细分和风险评估,用于预测上部泌尿道功能障碍
Zhonghan Zhou1,2,3,4, Hang Tianyang5, Juan Wu1,2,3,4
1School of Rehabilitation of Capital Medical University; Department of Urology, China Rehabilitation Research Center, Beijing, China.
International journal of surgery (London, England)
|September 11, 2025
概括
膀形态特征准确地预测上泌尿道功能障碍 (UUTD) 风险. 一个用于膀细分和UUTD风险识别的自动化系统比传统方法表现优越.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 神经性膀患者经常面临上游泌尿道功能障碍 (UUTD) 的风险.
- 准确的UUTD风险评估对于及时干预至关重要.
- 当前的方法可能缺乏客观性和效率.
研究的目的:
- 分析膀形态特征,用于UUTD风险预测.
- 开发一个完全端到端 (E2E) 的自动化算法,用于膀细分和UUTD风险识别.
主要方法:
- 对604名神经性膀患者进行视频尿动力学 (VUDS) 的回顾性分析.
- 从手动和自动细分的膀感兴趣区域 (ROI) 提取36个形态特征 (形状,LoG图谱,分形特征).
- 开发和评估机器学习模型 (如SVM),使用选定的功能和FCN-ResNet101进行自动细分.
主要成果:
- 28个形态特征在膀等级之间有显著差异,形状_周周_面_比率是UUTD的一个关键预测指标 (AUC=0.722).
- 该SVM模型实现了高性能 (AUC=0.860训练,0.830测试),优于单个特征.
- 自动细分 (Dice=0.9848,mIoU=0.9705) 与手动细分 (r>0.90) 有很强的相关性,使得E2E方法具有可比的ML模型性能 (AUC=0.843训练,0.815测试).
结论:
- 膀形态特征是UUTD的强有力的预测因素.
- 开发的E2E自动化模型为早期UUTD风险评估提供了定量和客观的工具,其性能优于传统的临床参数.
- 建议在大型多中心前性研究中进一步验证,以确认临床适用性.
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