@article {Dournes2100844作者= {Dournes,爱尔兰统一党和大厅,追逐美国Willmering,马修·m·布罗迪,Macey Alan s和Bui,朱莉和斯蒂芬妮和丹尼斯•德Senneville博杜安和伯杰,帕特里克和劳伦,弗兰{\ c c} ois Benlala, Ilyes和森林,杰森·c·},title ={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},体积= {59}= {3},elocation-id = {2100844} = {2022}, doi = {10.1183/13993003.00844 -2021},背景胸部计算机断层扫描(C188bet官网地址T)仍然是证明体内囊性纤维化(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，需要训练和缺乏高重复性。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。使用三种二维卷积神经网络的算法对78例患者的CT扫描(23530片CT)进行训练，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。36名患者的CT扫描(11435片CT)用于测试与地面真实标签。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。使用Dice系数评估相似度和再现性，使用Spearman检验评估相关性，使用Wilcoxon秩检验评估配对比较。结果人工智能驱动与地面真实标签的整体像素相似性较好(Dice 0.71)。所有人工智能驱动的容量量化与视觉成像评分具有中等至非常好的相关性(p\<0.001)，与肺功能测试预测的1 s \%用力呼气量具有良好相关性(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}, issn = {0903-1936}, URL = {//www.qdcxjkg.com/content/59/3/2100844}, eprint = {//www.qdcxjkg.com/content/59/3/2100844.full.pdf}, journal = {European Respiratory Journal} }