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美国的序列算法集成人工智能模型,用于高级肝纤维化查.

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  • 1From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.).

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结合超声波深度学习 (DL) 和FIB-4的序列算法改善了晚期肝纤维化检测. 这种方法提高了慢性肝病患者的诊断准确性和转诊管理.

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 肝病学和胃肠病学 肝病学和胃肠学
  • 诊断精度和预测建模的预测模型.

背景情况:

  • 非侵入性测试对于查慢性肝病患者晚期肝纤维化至关重要.
  • 依赖单一的非侵入性测试可能会限制诊断的充分性.
  • 需要改进的算法,整合多种非侵入性模式.

研究的目的:

  • 开发一系列的临床算法,包括超声波 (美国) 深度学习 (DL) 模型 (FIB-Net).
  • 将这些算法的诊断性能与现有的高级肝纤维化非侵入性测试进行比较.
  • 评估序列算法的有效性,以改善推管理.

主要方法:

  • 对患有慢性肝病或肝功能异常的成年患者进行了回顾性研究,接受了美国的肝功能测试.
  • 开发和验证美国DL网络 (FIB-Net) 以预测晚期纤维化 (剪切波弹性学[SWE]≥8.7kPa).
  • 构建和评估两步 (FIB-4,然后FIB-Net) 和三步 (FIB-4,FIB-Net,然后SWE) 算法.

主要成果:

  • 与SWE相比,FIB-Net表现出非劣质的特异性 (80%与82%相比).
  • 两步算法 (FIB-4 + FIB-Net) 显著提高了比单独使用FIB-4的特异性 (79% vs 57%) 和正预测值 (PPV) (44% vs 32%),减少了42%的不必要转诊.
  • 三步算法 (FIB-4 + FIB-Net + SWE) 的特异性 (94% vs 88%) 和PPV (73% vs 64%) 比EASL准则更高,减少了35%的不必要转诊.

结论:

  • 集成FIB-4和美国DL模型的序列算法提高了晚期肝纤维化诊断的准确性.
  • 与单个非侵入性测试或单独的DL模型相比,这些结合方法提供了更高的性能.
  • 开发的算法改善了转诊管理,可能减少不必要的患者转诊和医疗保健成本.