对 SpineNetv2 深度学习系统进行自动腰椎脊柱MRI分析的外部验证:一项多病理诊断协议研究
Xingkai Wu1, Qianbo Song1, Jiaxiang Zhou2
1The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.
概括
深度学习工具SpineNetv2在诊断腰椎疾病方面达到了高度一致. 虽然对几种病理有效,但其在分级磁盘退化方面的准确性需要进一步改进,特别是在老年患者中.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 脊柱外科和诊断 脊柱外科和诊断
背景情况:
- 磁共振成像 (MRI) 是腰椎评估的黄金标准.
- 目前的MRI解释是耗时的,容易引起观察者之间的变化.
- 脊柱Netv2为多种脊柱病理提供自动化分析.
研究的目的:
- 为了独立验证SpineNetv2.v的性能.
- 为了比较SpineNetv2的准确性与专家整形外科医生的评估.
- 评估SpineNetv2在诊断退行性腰椎脊椎疾病中的实用性.
主要方法:
- 对491名患者 (2455名腰椎盘) 的回顾性分析.
- 评估了六种病理:椎间盘退化 (Pfirrmann分级),中枢通道狭窄 (CCS),脊椎解脱,,双边孔腔狭窄 (FS).
- 性能指标包括灵敏度,特异性,PPV,NPV,F1得分,MCC,精确一致,加权卡帕和MAE.
主要成果:
- SpineNetv2获得了很高的总体同意率 (平均92.8%).
- 在CCS,脊髓缩和双边FS (p ≤ 0.001) 中表现优于初级外科医生.
- 皮尔曼分级显示,脊髓网v2的MAE较低 (0.213比0.254,p=0.001),但在老年患者/上盘患者下降.
结论:
- 脊柱Netv2显示了对五种双腰椎病理的高度一致性.
- 皮尔曼分级是一种局限性,特别是在老年患者和上腰椎盘中.
- 以特异性为导向的个人资料建议用作第二个读者,但负面的发现需要谨慎.
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