深度学习算法用于使用纳什 - 莫方法自动评估脊柱脊柱轴旋转在患有脊椎的患者
Jeoung Kun Kim1, Ming Xing Wang2, Donghwi Park3
1Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea.
本研究引入了一种自动化的深度学习 (DL) 模型,用于评估在异常脊椎病患者的轴性脊椎旋转 (AVR),使用脊柱放射图. DL模型显著加快了测量速度,并显示出令人满意的准确性,改进了手动方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 整形外科 整形外科 整形外科
背景情况:
- 准确评估轴性脊椎旋转 (AVR) 对于管理异常学脊椎病至关重要.
- 传统的纳什-莫分类是主观的,导致测量变化.
- 需要自动化方法来提高AVR评估的准确性和效率.
研究的目的:
- 开发和验证深度学习 (DL) 框架,用于使用前后脊柱放射图进行自动化的AVR评估.
- 将DL模型的效率和准确性与手动纳什-莫分类进行比较.
- 改进纳什-莫分类方法,以便更精确地评估脚.
主要方法:
- 使用MMRotate工具箱开发了一个两阶段的DL框架.
- 该框架包括在1080脊柱放射图上训练的脊椎和足骨检测模型.
- 一个改进的纳什-莫分类结合了12个细分的划分和宽度比度.
主要成果:
- DL模型实现了高平均精度 (0.909为脊椎,0.905为脚检测).
- 整体分类准确度为0.74,分级特定精度在0.70-1.00之间,回忆在0.33-0.93.
- 该DL框架在5秒内处理了X线图,比手动测量 (114秒) 快得多.
结论:
- 纳什-莫分类的自动化DL框架表现出令人满意的准确性和卓越的效率.
- 轻度至中度旋转病例的表现最好;严重病例需要进一步的数据.
- 限制包括3级的回忆力低,无法对4级进行分类,需要增强数据集.
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