基于深度学习算法的男性神经性下尿路功能障碍的辅助诊断研究
Xin Liu1,2, Ping Zhong1,2, Tianyu Xiang1,2
1School of Rehabilitation, Capital Medical University, Beijing, China.
International journal of surgery (London, England)
|September 2, 2025
概括
深度学习模型有助于诊断男性神经性下尿路功能障碍 (NLUTD). 在评估膀出口阻塞和排泄器收缩性方面,yolov10x和yolov5x模型具有很高的准确性.
科学领域:
- 尿道病学
- 人工智能
- 医疗诊断
背景情况:
- 神经性下尿路功能障碍 (NLUTD) 的诊断是具有挑战性的,并且容易被缺乏经验的医生误诊.
- 准确的诊断对于有效的患者管理和治疗至关重要.
- 目前的诊断方法可能缺乏复杂病例所需的精度.
研究的目的:
- 开发和验证深度学习算法模型用于自动诊断男性NLUTD.
- 整合视频尿动力学与人工智能以提高诊断准确度.
- 建立NLUTD诊断和治疗的自动化过程.
主要方法:
- 使用了384名男性NLUTD患者的泌尿动力学数据 (2009-2021年).
- 训练并验证的深度学习模型 (yolov5,yolov10) 在压力流和囊回路图像上.
- 为男性NLUTD建立了定性和局部评估模型.
主要成果:
- 对于膀出口阻塞和阻塞器收缩性的定性评估,yolov10x模型的平均精度 (mAP) 为0. 89.
- 通过yolov5x模型实现了0. 83的平均mAP,以评估膀出口阻塞的局部性.
- 这两种模型在各自的诊断任务中都表现出很高的性能.
结论:
- 在男性NLUTD中,yolov10x模型有效地对膀出口阻塞和排尿器收缩性进行了定性评估.
- yolov5x模型适用于男性NLUTD患者膀出口阻塞的局部评估.
- 基于人工智能的视频尿动力学为准确和自动化的NLUTD诊断提供了一个有希望的方法.
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