TY -的T1 -人工智能的计算机断层扫描定量肺CFTR时代的变化检测调节器JF -欧洲呼吸杂志》乔和J - 10.1183/13993003.00844 -2021欧元六世- 59 - 3 SP - 2100844 AU Dournes盖尔人非盟-霍尔,追逐美国非盟- Willmering,马修·m . AU -布罗迪,Alan s Macey AU -朱莉Bui,非盟-斯蒂芬妮盟——丹尼斯•德Senneville博杜安AU -伯杰,帕特里克AU -劳伦,弗朗索瓦•AU - Benlala Ilyes盟——森林,胸部计算机断层扫描(CT)仍然是体内诊断囊性纤维化(CF)气道结构性疾病的影像学标准。然而，视觉评分系统作为结果测量是耗时的，需要训练和缺乏高可重复性。我们的目标是验证一个完全自动化的人工智能(AI)驱动的CF肺部疾病严重程度评分系统。方法回顾性收集2008 - 2020年3个CF参考中心184例4-54岁患者的数据。利用78例患者的CT扫描图(23 530张CT切片)训练3种二维卷积神经网络算法，对支气管扩张、支气管周围增厚、支气管粘液、细支气管粘液和塌陷/实变进行语义标记。36例患者的CT扫描(11 435个CT切片)被用于测试与真实基线标签。该方法的临床有效性在70例接受或未接受lumacaftor/ivacaftor治疗的独立组(n=10和n=60)中进行评估，并进行重复检查。使用Dice系数评估相似性和可重复性，使用Spearman检验评估相关性，使用Wilcoxon秩次检验进行配对比较。结果人工智能驱动标签与地面真实标签的总体像素相似性很好(Dice 0.71)。所有人工智能驱动的体积量化与视觉成像评分有中等到非常好的相关性(p<0.001)，与肺功能测试预测的强制呼气量有中等到良好的相关性(p<0.001)。 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 ER -