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深度学习和多模式人工智能在整形外科手术中的应用.

Anthony Bozzo1, James M G Tsui, Sahir Bhatnagar

  • 1From the Division of Orthopaedic Surgery, McGill University, Canada (Bozzo), the Division of Radiation Oncology, McGill University, Canada (Tsui), the Department of Epidemiology and Biostatistics, Department of Diagnostic Radiology, McGill University, Canada (Bhatnagar), and the Memorial Sloan Kettering Cancer Center (Forsberg).

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科学领域:

  • 整形外科手术 整形外科手术
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 深度学习,包括神经网络和多式人工智能,越来越多地应用于骨科.
  • 人工智能在整形外科的整合为增强诊断和治疗规划提供了潜力.

研究的目的:

  • 审查骨科领域的深度学习应用,重点关注神经网络和多模式人工智能.
  • 为整形外科医生提供工具来评估AI文献,并考虑其临床或研究潜力.

主要方法:

  • 对临床变量标准深度神经网络的审查.
  • 用于图像数据分析的卷积神经网络的描述.
  • 介绍用于多样化数据集成的多模式AI模型.

主要成果:

  • 人工智能在骨科手术中的实用例子,特别是在成像和临床数据方面.
  • 深度学习技术与放射学技术的比较.
  • 关于解释和启动深度学习研究的指导.

结论:

  • 赋予骨科外科医生深度学习知识,有助于研究翻译.
  • 人工智能有可能提高骨科患者护理的有效性和精度.