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基于人工智能的腰部中央通道狭窄症分类在松式MRI图像上,与使用轴向图像的经验丰富的放射科医生相似

Jasper W van der Graaf^1,2, Liron Brundel³, Miranda L van Hooff^3,4

¹Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands. jasper.vandergraaf@radboudumc.nl.

European radiology

|September 19, 2024

概括

人工智能模型现在可以有效地分类腰部中心通道狭窄症 (LCCS),仅使用腰部MRI扫描. 这种人工智能方法与经验丰富的放射科医生的诊断准确度相匹配,提高了医学成像的效率.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

腰部中央通道狭窄 (LCCS) 评估对于腰部疼痛诊断和治疗计划至关重要.
手动LCCS评估是耗时的,主观的,通常需要额外的轴向MRI序列.
需要更高效,更准确的方法来进行LCCS分类.

研究的目的:

开发和验证基于人工智能的模型,用于自动化的LCCS分类.
在LCCS评估中使用斜视T2加权的MRI,减少对轴向图像的依赖.
将AI模型的性能与经验丰富的放射科医生进行比较.

主要方法:

一个3D人工智能算法对脊柱通道和椎间盘进行了细分,以进行定量测量.
肌肉骨放射科医生使用李分级系统对186名LCCS患者的683个IVD水平进行了分级.
一个随机的森林分类器被训练使用多类 (等级0-3) 和二进制 (等级0-1vs2-3) 方法与十倍交叉验证.

主要成果:

多类人工智能模型实现了0.86的科恩加权卡帕,相当于放射科医生的表现.
二元AI模型的AUC为0.98,具有93%的灵敏度和91%的特异性.
人工智能模型的性能与经验丰富的放射科医生相匹配或超过,特别是在灵敏度方面.

结论:

人工智能模型只使用斜面MRI可以准确地分类LCCS.
开发的AI算法提高了LCCS评估的诊断准确性和效率.
这种方法为自动化LCCS评估提供了一个有前途的工具,可能减少对轴向成像的需求.

关键词:

深度学习是一种深度学习.腰部中央通道狭窄症这就是为什么MRI是MRI.机器学习是机器学习.脊柱脊柱脊柱脊柱脊柱脊柱

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