RT期刊文章SR电子T1计算机断层扫描中的人工智能，用于量化CFTR调制器时代的肺变化JF欧洲呼吸杂志JO EUR RESSIR J FD欧洲呼吸协会SP 2100844 DO 10.1183/13993003.003003.00844-2021 VO 59188bet官网地址Hall，Chase S. A1 Willmering，Matthew M. A1 Brody，Alan S. A1 Macey，Julie A1 Bui，Stephanie A1 Denis denis denis de Senneville，Baudouin A1 Berger，Patrick A1 Laurent，FrançoisA1Benlala，Ilyes A1 Woods，Jason C. Yrs，Jason C. Yrs，Jason C. yr2022 ul //www.qdcxjkg.com/content/59/3/3/3/2100844.abstract ab背景胸部计算机断层扫描（CT）仍然是体内证明囊性纤维化（CF）气道结构疾病的成像标准。但是，视觉评分系统作为结果度量是耗时的，需要培训并且缺乏高可重复性。我们的目标是验证CF肺部疾病严重程度的全自动人工智力（AI）驱动的评分系统。在2008年至2020年之间，在三个CF参考中心中回顾性收集的数据，在184名4-54岁年龄的患者中。使用三个2D卷积神经网络的算法进行了78例患者的CT扫描（23 530 CT切片），用于支气管张舒张的语义标记，支气管粘膜，支气管粘膜，支气管粘液，支气管粘液粘液和塌陷/合并。36例患者的CT扫描（11 435 CT切片）用于测试与地面真相标签。该方法的临床有效性在70例患者/不接受Lumacaftor/ivacaftor治疗（分别为n = 10和n = 60）的独立组中进行了评估，并进行了重复检查。使用骰子系数，使用Spearman测试对相似性和可重复性进行了评估，并使用Wilcoxon Rank测试进行了配对比较。恢复AI驱动与地面真实标签的总体PixelWise相似性是不错的（DICE 0.71）。所有AI驱动的体积量化都与视觉成像评分（P <0.001）具有中等至良好的相关性，并且在肺功能测试中预测的1 s％的强迫呼气量（P <0.001）中都具有公平相关性。 Significant decreases in peribronchial thickening (p=0.005), bronchial mucus (p=0.005) and bronchiolar mucus (p=0.007) volumes were measured in patients with lumacaftor/ivacaftor. Conversely, bronchiectasis (p=0.002) and peribronchial thickening (p=0.008) volumes increased in patients without lumacaftor/ivacaftor. The reproducibility was almost perfect (Dice >0.99).Conclusion AI allows fully automated volumetric quantification of CF-related modifications over an entire lung. The novel scoring system could provide a robust disease outcome in the era of effective CF transmembrane conductance regulator modulator therapy.Artificial intelligence allows a fully automated volumetric scoring system of lung structural abnormalities in CF using computed tomography. It could be used as a robust quantitative outcome to assess disease changes in the era of CFTR modulators. https://bit.ly/3hlXmnc