深度学习模型用于在磁共振成像上对腰椎间板的分级和定位
Yefu Xu1, Shijie Zheng1, Qingyi Tian1
1Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.
Journal of magnetic resonance imaging : JMRI
|April 27, 2024
概括
一个深度学习 (DL) 模型有效地评级和定位腰椎间盘 (LDH),其性能与脊柱外科医生相美. 进一步的病例多样性可以提高这一复杂的诊断任务的概括性.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 脊柱外科手术 脊柱外科手术
背景情况:
- 腰椎间盘 (LDH) 在MRI上的分级和定位是复杂的,主观的和耗时的.
- 深度学习 (DL) 为提高效率和客观性提供了一个潜在的解决方案.
研究的目的:
- 开发一种可解释的深度学习 (DL) 模型,用于分级和定位腰椎间盘 (LDH).
主要方法:
- 对1496名患者的1.5T MRI轴向T2加权序列的回顾性分析.
- 在外科医生注释的数据 (密歇根州立大学分类) 上训练DL模型.
- 使用交叉与结合 (IoU),Gwet的AC1,灵敏度和内部和外部测试集的特异性来评估性能.
主要成果:
- DL模型表现出高的内部一致性 (分级 IoU 0.84,定位 IoU 0.82) 和灵敏度/特异性.
- 内部测试显示DL模型的性能与脊柱外科医生相比.
- 外部验证显示性能有所下降,但仍然显著 (评级 IoU 0.72,本地化 IoU 0.71).
结论:
- 开发的DL模型具有类似于脊椎外科医生LDH的分类能力.
- 建议加强培训数据的多样性,以提高模型的通用性.
- 可解释的DL模型对协助诊断腰椎间盘的诊断具有前途.
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