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Endoscopic Studies I: Bronchoscopy and Thoracoscopy01:30

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Endoscopy is a non-surgical medical technique used to examine a person's internal organs and vessels. This lesson will focus on two types of endoscopic studies: bronchoscopy and thoracoscopy.
Bronchoscopy
Description
Bronchoscopy is a procedure that involves direct visualization of the larynx, trachea, and bronchi for diagnostic and therapeutic purposes. A flexible fiber optic or rigid bronchoscope is used to carry out the procedure. The fiber-optic bronchoscope is more frequently used due...
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人类和深度学习预测外围肺癌使用1.3毫米视频内镜探头.

Edoardo Amante1,2, Robin Ghyselinck3, Luc Thiberville1

  • 1Department of Pneumology, Rouen University Hospital, Rouen, France.

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概括

经验丰富的医生准确地使用虹膜镜识别出恶性小肺结节. 人工智能在提高外围内镜检查经验较少的医生诊断准确度方面显示出前景.

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人工智能的人工智能是人工智能.支气管镜检查 (bronchoscopy) 是一种用来检查支气管的方法.深度学习是一种深度学习.影像成像技术 影像成像技术周围肺部结节 周围肺部结节辐射式-EBUS的使用.

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科学领域:

  • 肺部病理学 肺部病理学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 小外围肺结节 (PPNs) 带来了诊断上的挑战.
  • 虹膜镜是一种1.3毫米视频内镜探针,可通过放射探针内超声波 (r-EBUS) 导管直接可视化PPNs.
  • 评估PPN的恶性瘤风险对于患者管理至关重要.

研究的目的:

  • 评估具有不同经验水平的医生在解释PPN的虹膜镜图像方面的诊断性能.
  • 评估人工智能 (AI) 深度学习 (DL) 模型在预测使用Iriscope可视化的PPN恶性病的能力.
  • 为了比较人类解释器和人工智能的诊断准确度与PNS的最终诊断.

主要方法:

  • 对视频录制的虹膜镜序列的分析,来自接受PPN (<20毫米) 支气管镜检查的患者.
  • 由高级和初级医生独立解释图像,将病变分类为瘤或非瘤.
  • 在虹膜镜图像上训练和测试DL模型,以预测恶性瘤,将其性能与人类解释器进行比较.

主要成果:

  • 虹膜镜使所有61名被纳入患者的PPNs能够直接可视化.
  • 高级医生在识别恶性结节方面取得了85.4%的平衡准确率,超过了初级医生 (66.7%).
  • DL模型实现了71.5%的平衡精度,超过了初级医生,但没有超过高级医生.

结论:

  • 虹膜镜是管理PPN的宝贵工具,尤其是经验丰富的支气管镜师.
  • 人工智能,特别是对虹膜镜图像应用的DL模型,有可能增强缺乏经验的医生的诊断能力.
  • 进一步的研究可能会探索将人工智能集成到外围内镜工作流程中,以改善PPN诊断.