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深度学习框架用于在脏超声波中自动选择
Amirali Seraj1, Seyed Pedram Monazami1, Raheleh Davoodi2
1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
Scientific reports
|November 25, 2025
概括
自动化的深度学习有效地选择最佳的脏超声波框架,提高诊断一致性. YOLO11x-cls模型实现了优质框架的完美分类,增强了临床工作流程.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 超声波技术 超声波技术 超声波技术
背景情况:
- 手动选择脏超声波是耗时和主观的.
- 这种变化可能会影响临床评估的可靠性.
研究的目的:
- 开发和评估一种自动化的深度学习框架,用于从脏超声波视频中选择最佳.
- 为了提高脏超声波解释的效率和一致性.
主要方法:
- 来自211名患者的1,203个脏超声波的数据集被策划和注释.
- 训练了包括YOLOv11x-cls在内的几个卷积神经网络模型,并将它们与框架分类进行比较.
- 优化了YOLO11x-cls模型,并使用5倍的患者级交叉验证进行了评估.
主要成果:
- YOLO11x-cls模型的性能优于基线架构,在优质中获得100%的F1分数.
- 该框架实现了90%的平均交叉验证准确性,性能差异最小.
- 拟议的方法证明了强大的和高效的自动化最佳框架选择.
结论:
- 开发的基于YOLO的深度学习管道为脏超声波中自动选择最佳提供了一个有希望的解决方案.
- 这项技术可以减少手工劳动,提高诊断可靠性和临床环境中的可重复性.
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