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相关概念视频

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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使用自然语言处理预测腹部和盆腔MRI协议.

Joshua D Warner1, Robert P Hartman2, Daniel J Blezek2

  • 1Department of Radiology, University of Wisconsin-Madison School of Medicine & Public Health, 600 Highland Ave, Madison, WI, 53792-3252, USA. jwarner@uwhealth.org.

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此摘要是机器生成的。

这项研究开发了一个使用自然语言处理 (NLP) 来自动化MRI检查协议的AI解决方案. 人工智能准确地预测成像协议,减少放射科医生的工作量并提高效率.

关键词:
人工智能 (人工/增强型智能)BERT (来自变压器的双向编码器表示 (NLP模型))磁共振成像 (MRI) 是一种磁共振成像技术.自然语言处理 (NLP) 是一种自然语言处理.通过议定书,制定了议定书.

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科学领域:

  • 人工智能的人工智能
  • 医疗成像医学成像
  • 放射学 信息学 信息学

背景情况:

  • 放射科医生面临着大量的非解释性任务的工作负担,例如检查记录.
  • 自动化记录可以提高部门的效率,减少放射科医生的负担.

研究的目的:

  • 开发和评估一种自然语言处理 (NLP) 人工智能 (AI) 解决方案,用于自动记录腹部和骨盆MRI检查.
  • 用历史患者元数据和订单信息来评估AI模型的性能.

主要方法:

  • 一项回顾性研究,使用了大约46,000名成年人腹部和骨盆MRI检查 (2019-2021) 的非识别元数据.
  • 在序列分类模式中微调一个双向编码器表示从变压器 (BERT) NLP模型.
  • 排除COVID大流行期间12个月的数据以减轻偏见.

主要成果:

  • 训练有素的人工智能模型的准确度达到88.5%,马修斯相关系数为0.874.4.
  • 专家对模型错误的审查显示,81.9%是正确或合理的替代协议,产生了97.9%的真实世界的准确性.

结论:

  • 包括基于BERT的模型在内的NLP算法可以有效地预测腹部和骨盆的MRI成像协议.
  • 这种人工智能解决方案有可能显著减少放射科医生的非解释性任务负载,并提高部门的整体效率.