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人工智能在肌肉骨骼疾病患者诊断与预后评估中的应用

Artificial Intelligence in the Diagnosis and Prognostication of the Musculoskeletal Patient.

作者信息

Girod Miguel M, Saniei Sami, Ulrich Marisa N, Bukowiec Lainey G, Mulford Kellen L, Taunton Michael J, Wyles Cody C

机构信息

Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.

Mayo Clinic Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.

出版信息

HSS J. 2025 May 28:15563316251339660. doi: 10.1177/15563316251339660.

DOI:10.1177/15563316251339660
PMID:40454292
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12119539/
Abstract

As artificial intelligence (AI) advances in healthcare, encompassing robust applications for the diagnosis and prognostication of musculoskeletal diseases, clinicians must increasingly understand the implications of machine learning and deep learning in their practice. This review article explores computer vision algorithms and patient-specific, multimodal prediction models; provides a simple framework to guide discussion on the limitations of AI model development; and introduces the field of generative AI.

摘要

随着人工智能(AI)在医疗保健领域的发展,涵盖了用于肌肉骨骼疾病诊断和预后的强大应用,临床医生必须越来越了解机器学习和深度学习在其实践中的影响。这篇综述文章探讨了计算机视觉算法和针对特定患者的多模态预测模型;提供了一个简单的框架来指导关于AI模型开发局限性的讨论;并介绍了生成式AI领域。

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本文引用的文献

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J Hip Preserv Surg. 2024 Dec 12;12(1):27-32. doi: 10.1093/jhps/hnae041. eCollection 2025 Jan.
2
A Deep Learning Tool for Minimum Joint Space Width Calculation on Antero-posterior Knee Radiographs.一种用于计算膝关节前后位X线片最小关节间隙宽度的深度学习工具。
J Arthroplasty. 2025 Aug;40(8):2001-2006. doi: 10.1016/j.arth.2025.01.038. Epub 2025 Jan 27.
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TKA-AID: An Uncertainty-Aware Deep Learning Classifier to Identify Total Knee Arthroplasty Implants.TKA-AID:一种用于识别全膝关节置换植入物的不确定性感知深度学习分类器。
J Arthroplasty. 2025 Aug;40(8):2007-2014. doi: 10.1016/j.arth.2025.01.019. Epub 2025 Jan 19.
4
Exploring prospects, hurdles, and road ahead for generative artificial intelligence in orthopedic education and training.探索生成式人工智能在骨科教育与培训中的前景、障碍及未来之路。
BMC Med Educ. 2024 Dec 28;24(1):1544. doi: 10.1186/s12909-024-06592-8.
5
Leveraging AI models for lesion detection in osteonecrosis of the femoral head and T1-weighted MRI generation from radiographs.利用人工智能模型进行股骨头坏死的病变检测以及从X线片生成T1加权磁共振成像。
J Orthop Res. 2025 Mar;43(3):650-659. doi: 10.1002/jor.26026. Epub 2024 Nov 23.
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Uncertainty-Aware Deep Learning Characterization of Knee Radiographs for Large-Scale Registry Creation.用于大规模注册创建的膝关节X线片的不确定性感知深度学习特征分析
J Arthroplasty. 2025 May;40(5):1232-1238. doi: 10.1016/j.arth.2024.10.103. Epub 2024 Oct 29.
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Analyzing Racial Differences in Imaging Joint Replacement Registries Using Generative Artificial Intelligence: Advancing Orthopaedic Data Equity.使用生成式人工智能分析关节置换登记影像中的种族差异:推进骨科数据公平性。
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Radiother Oncol. 2024 Aug;197:110338. doi: 10.1016/j.radonc.2024.110338. Epub 2024 May 22.
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Generative models improve fairness of medical classifiers under distribution shifts.生成式模型可提高分布偏移下医学分类器的公平性。
Nat Med. 2024 Apr;30(4):1166-1173. doi: 10.1038/s41591-024-02838-6. Epub 2024 Apr 10.
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