％0期刊文章％a dournes，Gael％a Hall，Chase S.％A Willmering，Matthew M.％A Brody，Alan S.％A Macey，Julie％A Bui，Stephanie％A Denis a Denis denis de senneville，Baudouin％a Berger a Berger，帕特里克％a lurent，françois％a benlala，iyes％a woods，jason C.％t人工智能在计算机断层扫描中用于量化CFTR调制器时代的肺变化％D 2022％R 10.1183/139993003.00844-2021％J欧洲呼吸器期刊％p 2100844％v 59％n 3％x背景计算机断层扫描（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 %U //www.qdcxjkg.com/content/erj/59/3/2100844.full.pdf