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Thoracic, aortic arch and abdominal aneurysms are significant vascular conditions that can present with various clinical manifestations and lead to serious complications. Understanding these manifestations and the appropriate diagnostic studies is essential for effective management and treatment.Thoracic Aortic AneurysmsThoracic aortic aneurysms often remain asymptomatic until they reach a size that impinges on adjacent structures. They typically cause deep, diffuse chest pain that radiates to...

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知识增强的深度学习,用CT血管学对脑动脉瘤进行细分和检测:一项多中心研究

Jianyong Wei1, Xinyu Song1, Xiaoer Wei1

  • 1From the Clinical Research Center (J.W.) and Institute of Diagnostic and Interventional Radiology, Department of Radiology (X.S., X.W., L.D., Z.S., Y. Zhu, Y.L.), Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600 Yi Shan Rd, Shanghai 200233, China; School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China (J.W., M.W., Y.J.); Shukun (Beijing) Network Technology, Beijing, China (Zhiwen Yang, C.M.); Department of Radiology, The First Affiliated Hospital of Soochow University, Jiangsu, China (C.H.); Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (X.X.); Department of Cardiology, Beijing Friendship Hospital of Capital Medical University, Beijing, China (Zhenghan Yang); Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (Y. Zhang); Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (F.L.); and Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China (J.L.).

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|August 20, 2024
PubMed
概括

一种新的深度学习模型在CT血管图 (CTA) 扫描上准确地检测大脑动脉瘤,与放射科医生的性能相匹配. 这种人工智能工具可以简化对这种具有挑战性的疾病的诊断.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 深度学习 (DL) 具有改善劳动密集型脑动脉瘤诊断的潜力.
  • 准确的诊断需要大型的多中心数据集,用于DL模型开发.
  • 目前用于脑动脉瘤检测和细分的方法具有挑战性.

研究的目的:

  • 开发一个DL模型,准确地对CT血管图像进行脑动脉瘤细分和检测.
  • 将DL模型的性能与放射学报告进行比较.
  • 用多中心数据集来进行稳健的模型构建.

主要方法:

  • 来自八家医院 (n=6060) 的头部/头部和部CTA图像的回顾性收集,用于模型开发.
  • 外部验证使用数字减去血管学 (DSA) 扫描 (n=118) 作为参考标准.
  • 使用子相似系数 (DSC) 进行细分和灵敏度/AUC用于检测的性能评估,与放射科医生相比.

主要成果:

  • 在内部测试组中,DL模型在动脉瘤细分方面实现了0.87的DSC.
  • 在外部验证中,该模型在动脉瘤检测方面显示了85.7%的灵敏度 (每血管分析).
  • 在DL模型 (AUC=0.93) 和放射学报告 (AUC=0.91) 之间,检测性能没有显著差异.

结论:

  • 开发的DL模型准确地在CTA上细分并检测大脑动脉瘤.
  • 该模型的诊断性能与放射学报告的诊断性能相当.
  • 这个人工智能工具快速处理扫描,可能有助于临床工作流程.