PT -期刊文章盟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 AU -伍兹,杰森·c·TI -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援助- 10.1183/13993003.00844 -2021 DP - 2022年3月01 TA -欧洲呼吸杂志》第六PG - 2100844 - 59 IP - 3 4099 - //www.qdcxjkg.com/content/59/3/2100844.short 4100 - //www.qdcxjkg.com/content/59/3/2100844.full所以欧元和J2022 3月01;59 AB -背景胸部计算机断层扫描(CT)仍然显示囊性纤维化(CF)的成像标准气道结构疾病的体内。然而,视觉评分系统作为一个结果测量是费时,需要培训和缺乏高重现性。我们的目标是验证一个完全自动化的人工智能(AI)简况CF肺部疾病严重程度评分系统。方法回顾性收集数据在三个CF参考中心,2008年至2020年,184年病人4-54岁。一个算法使用三个二维卷积神经网络训练的78名患者的CT扫描(23 530 CT片)的语义标签支气管扩张、支气管旁增厚,支气管粘液,细支气管粘液和崩溃/整合。36个患者的CT扫描(11 435 CT片)被用于测试和真实的标签。方法的临床有效性评估在一个独立的组70例患者有或没有lumacaftor / ivacaftor治疗(分别为n = 10 n = 60)和重复检查。使用骰子系数相似性和再现性进行评估,使用斯皮尔曼相关测试和成对比较使用Wilcoxon等级测试。真实结果的总体相似pixelwise AI-driven与标签很好(骰子0.71)。所有AI-driven体积量化了中度到很好的相关性视觉成像评分(术中,0.001)和公平良好的相关性,用力呼气量在1 s %预测肺功能测试(术中,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