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,Jason C. Y1 - 2022/03/01 UR - //www.qdcxjkg.com/content/59/3/2100844.abstract N2 -背景胸部计算机断层扫描(CT)仍然是体内显示囊性纤维化(CF)气道结构疾病的成像标准。然而，视觉评分系统作为一种结果测量是耗时的，需要培训和缺乏高再现性。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年至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(23530张CT切片)训练了一种使用三个二维卷积神经网络的算法，用于支气管扩张、支气管周围增厚、支气管粘液、细支气管粘液和塌陷/实变的语义标记。使用36例患者的CT扫描(11 435张CT片)与真实标签进行测试。该方法的临床有效性在70例接受或不接受lumacaftor/ivacaftor治疗的患者(n=10和n=60)中进行了评估，并进行了重复检查。相似性和再现性采用Dice系数进行评估，相关性采用Spearman检验，配对比较采用Wilcoxon秩检验。结果ai驱动标签与ground-truth标签的整体像素相似性良好(Dice 0.71)。所有人工智能驱动的容量量化与视觉成像评分有中度到非常好的相关性(p<0.001)，在肺功能测试预测的1%的用力呼气量中有良好的相关性(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 -