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深度学习分类模型在关节塑性临床途径的放射解读中显示出高精度和临床潜力:系统性审查和元分析

Omar Musbahi^1,2, Amal Ahmed¹, Thomas Hall³

¹Imperial College London, MSk Lab London UK.

Journal of experimental orthopaedics

|July 14, 2025

概括

深度学习算法在解释关节质整形途径的骨关节炎成像方面表现有前途,有可能提高诊断和预后的效率. 需要进一步的研究来探索它们在患者护理中的作用.

科学领域:

整形医生整形医生整形医生
放射学放射学是指放射学
人工智能的人工智能

背景情况:

影像对骨关节炎 (OA) - 关节塑形临床途径至关重要,包括诊断,手术和随访.
深度学习 (DL) 算法越来越多地被用于解释骨科医学图像.
这一元分析研究了在关节整形护理连续过程中OA成像中DL的表现.

研究的目的:

评估深度学习算法在解读放射和骨关节炎 (OA) 的横截面成像中的性能.
将DL算法与人类临床医生的诊断和预后能力进行比较.
评估DL在改善OA分类和预后方面的潜力.

主要方法:

系统搜索Ovid Medline,Embase,Scopus和Web of Science进行了从2012年1月到2024年4月的研究.
包括的研究评估了OA-关节质整术途径中的诊断和预后任务的DL算法,使用已建立的评估作为基准真相.
使用诊断准确性研究质量评估2工具评估研究质量,并计算了聚合灵敏度和特异性.

主要成果:

分析包括66项研究,人工智能 (AI) 解释显示了0.88的聚合灵敏度和0.91.91的特异性.
与截面成像相比,人工智能对放射图的解释显示出更高的灵敏度和特异性.
临床解释 (11项研究) 给出了0.76的综合灵敏度和0.79.7的特异性.

结论:

深度学习算法显示了在关节塑形途径内提高OA分类和预后效率的潜力.
虽然证据质量低至中等,并且不能完全泛化,但DL模型可能可以在OA治疗途径中采用.
需要进一步的研究来探索DL在优化骨关节炎患者护理中的作用.

关键词:

关节质复原关节质复原关节质复原关节质复原人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.放射学放射学是指放射学

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