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创伤学中的人工智能

Rosmarie Breu1,2,3, Carolina Avelar4, Zsolt Bertalan4

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人工智能 (AI) 软件提高了医生远半径骨折 (DRF) 检测准确度. 人工智能辅助增强了灵敏度和特异性,大大降低了骨折诊断中的整体错误率.

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科学领域:

  • 放射学 放射学是一门学科.
  • 人工智能的人工智能
  • 整形外科手术 整形外科手术

背景情况:

  • 距离半径骨折 (DRFs) 是常见的骨科损伤.
  • 准确检测DRF对于有效的患者管理至关重要.
  • 医生诊断的准确性可能受到各种因素的影响,需要支持工具.

研究的目的:

  • 开发人工智能 (AI) 软件,以帮助医生检测远半径骨折 (DRF).
  • 评估AI软件支持对DRF医生的诊断准确度的影响.
  • 为了比较医生在识别手腕骨折时的AI辅助和无AI辅助医生的表现.

主要方法:

  • 一个卷积神经网络 (CNN) 模型被训练在一个大数据集的手腕放射图 (26,121 图像).
  • 十一位医生 (六名实习生,五名手术医生) 评估了200对手腕X线影像两次:一次是无助,一次是人工智能支持.
  • 诊断准确度指标,包括灵敏度,特异性和错误率,在无助和人工智能辅助评估之间进行了比较.

主要成果:

  • 美国有线电视新闻网 (CNN) 模型在接收器操作曲线下的面积达到0.97.
  • 人工智能辅助提高了医生对DRF检测的敏感度,从80%提高到87%.
  • 人工智能支持将特异性从91%提高到95%,将整体错误率从14%降低到9%.

结论:

  • 用作第二意见的AI软件可以提高远半径骨折检测的诊断准确性.
  • 人工智能工具的整合在改善骨科诊断工作流程方面显示出前景.
  • 人工智能辅助诊断可能会导致在临床环境中更准确,更有效地识别骨折.