深度学习和数值分析,以诊断男性膀外流阻塞和阻断器低活性:一种新的尿动力学评估方案
Haonan Mei1,2, Zhishun Wang3, Qingyuan Zheng1,2
1Department of Urology, Renmin Hospital of Wuhan University, Wuhan, China.
Neurourology and urodynamics
|January 13, 2025
概括
这项研究开发了一种使用深度学习和富里埃转换的AI模型,以从尿动力学数据中诊断膀外流阻塞和男性排泄器低活性. 人工智能显示出强大且可行的诊断性能,有助于临床决策.
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
- 人工智能的人工智能
背景情况:
- 男性下泌尿道症状 (LUTS) 通常需要尿动力学评估.
- 诊断膀外流阻塞 (BOO) 和排毒器低活性 (DUA) 可能是复杂的.
- 自动诊断工具可以提高效率和准确性.
研究的目的:
- 开发和验证人工智能模型,使用尿动力学来自动诊断BOO和DUA.
- 评估模型在大量男性患者中的表现.
- 探索AI模型在临床尿动力学中的可解释性.
主要方法:
- 1949年进行尿动力学研究的男性患者的回顾性审查.
- 开发一个深度卷积神经网络与短时间里埃变换 (STFT) 结合.
- 利用五通道泌尿动力学数据进行内部和外部数据集的培训和验证.
主要成果:
- 基于STFT的深度学习模型在内部数据集中实现了BOO (0.945±0.020) 和DUA (0.929±0.039) 的高AUC得分.
- 外部验证显示,BOO的AUC为0.881,DUA的AUC为0.850,表现强.
- 小组分析证实了人工智能模型的诊断效率和可解释性.
结论:
- 建议使用STFT的深度神经网络是解释男性泌尿动力学的强大且可行的方法.
- 这种人工智能方法显示出在现实世界临床环境中协助临床医生的潜力.
- 对BOO和DUA的自动诊断可以提高对LUTS的患者护理.
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