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深度学习-放射学评估椎间盘和副脊柱肌肉异质性,用于预测术后复发性腰椎间盘

Guangdong Zhang¹, Ziqian Zhu^1,2, Haiyan Zheng³

¹Department of Interventional Vascular Medicine, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China.

Frontiers in artificial intelligence

|February 20, 2026

概括

深度学习-放射学准确地使用椎间盘纹理预测术后复发性腰椎椎间盘 (PRLDH). 这种方法为预测腰椎间盘 (LDH) 患者的PRLDH提供了一个有希望的工具.

科学领域:

脊柱外科手术脊柱外科手术
放射学放射学是指放射学
人工智能在医学中的应用

背景情况:

手术后复发性腰椎间盘 (PRLDH) 影响患者的治疗结果.
现有的成像和副脊髓肌肉参数对微观层面的纹理特征提供了有限的洞察力.
深度学习 (DL) 和放射学为详细的组织分析提供了新的途径.

研究的目的:

应用DL-放射学来量化椎间盘和副脊柱肌肉中的微观结构异质性.
开发和评估一个用于预测PRLDH的联合盘肌模型.
评估DL-放射学特征对PRLDH的预测性能.

主要方法:

170名腰椎间盘 (LDH) 患者进行手术后期分析.
从椎间盘和副脊椎肌肉 (L3-S1) 中提取DL和放射性特征.
构建和内部验证DL-辐射学Radscore模型,使用离开一次,十倍交叉验证和启动.

主要成果:

椎间盘Radscore显示了PRLDH的强大预测能力 (AUC为0.857).
副脊髓肌肉Radscore显示了中等的预测性表现 (AUC为0.718).
皮尔曼分级是复发的不良预测指标 (AUC 0.506),而联合盘肌分析的稳定性不如单独的盘Radscore.

结论:

由DL-放射学衍生的椎间盘Radscore是PRLDH的可靠预测器.
虽然结合盘肌分析显示一致性较低,但了解它们的相互作用可以帮助进行术后风险分层.
这项研究强调了先进的成像技术在管理LDH患者中的潜力.

关键词:

深度学习是一种深度学习.椎间盘之间的椎间盘.腰椎椎间盘的发生副脊椎肌肉的肌肉.术后复发的腰椎间盘椎间盘.

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