一种新的深度学习方法,用于预测ESWL后无结晶率在无结晶CT上
Ozgur Efiloglu1, Muhammed Yildirim2, Kadir Yildirim3
1Urology, Istanbul Medeniyet University, Istanbul, Turkey.
PeerJ. Computer science
|September 24, 2025
概括
人工智能准确地预测结石治疗的成功使用非对比CT扫描. 这种人工智能模型通过预测无石头率,指导临床决策来增强体外冲击波石术 (ESWL) 的结果.
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:
- 身体外冲击波石 (ESWL) 是对结石的主要治疗方法.
- 预测治疗成功对于有效的患者管理至关重要.
研究的目的:
- 评估一种人工智能 (AI) 模型,用于预测ESWL后无石头率.
- 开发基于人工智能的模型,使用非对比计算机断层扫描 (CT) 图像来预测ESWL治疗的成功.
主要方法:
- 对接受ESWL的910名患者的回顾性分析.
- 使用本地二进制模式 (LBP) 和面向梯度的直方图 (HOG) 进行特征提取.
- 用邻近组件分析 (NCA) 来进行尺寸缩小和基于纹理的模型进行分类.
主要成果:
- 拟议的人工智能模型的平均预测准确率为94%.
- 与七个卷积神经网络 (CNN) 相比,基于纹理的模型显示出更高的性能.
- 混合人工智能模型的表现优于其他9个评估模型.
结论:
- 人工智能模型,特别是基于纹理的模型,在预测ESWL成功方面表现出很高的有效性.
- 开发的AI模型可以帮助专家指导结石治疗决策.
- 人工智能整合提供了一种有前途的方法来提高ESWL治疗结果.
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