多中心验证使用多序MRI对腰椎侧脊肌的自动细分和组成分析
Zhongyi Zhang1, Julie A Hides2, Enrico De Martino3
1School of Information and Communication Technology, Griffith University, 170 Kessels Road, Nathan, QLD 4111, Australia.
Radiology. Artificial intelligence
|August 20, 2025
概括
一种新的深度学习方法在MRI扫描中准确分段腰椎侧脊肌 (LPM). 这种自动化方法精确量化肌肉体积和脂肪透, 有助于慢性腰部疼痛的评估.
科学领域:
- 放射学
- 医学成像
- 医学的人工智能
背景情况:
- 慢性腰部疼痛是全球重要的健康问题,
- 腰部副脊髓肌肉 (LPM) 的变化与慢性腰部疼痛有关.
- 在MRI上手动评估LPM需要很长时间,并且可能会发生变化.
研究的目的:
- 开发和验证深度学习 (DL) 方法,用于LPM的自动细分和定量分析.
- 通过多序,多中心的MRI来评估LPM的肌肉体积和脂肪透.
- 将自动化DL方法的性能与手动细分和测量进行比较.
主要方法:
- 这是一项回顾性研究,在5个中心对641名参与者进行了1302次核磁共振.
- 一个DL模型被训练并进行外部验证,用于LPM细分,体积定量和脂肪透评估.
- 使用子相似系数 (DSC) 进行细分,并使用类内相关系数 (ICC) 进行测量.
主要成果:
- DL模型实现了高细分性能,总体DSC值为0.98 (内部) 和0.930.97 (外部).
- 自动和手动测量肌肉体积和脂肪透率之间的统计等价性得到证实 (P < .05).
- 在自动测量和手动测量之间观察到很高的一致性 (ICC > 0. 92).
结论:
- 开发的自动化DL方法在多序,多中心MRI上准确地细分LPM.
- 自动化方法证明了肌肉体积和脂肪透的统计等效.
- 这种由人工智能驱动的方法为评估慢性腰部疼痛的LPM变化提供了可靠和有效的工具.
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