@Article {NAM2003061，作者= {Nam，Ju Gang和Kim，Minchul和Park，Jongchan和Hwang，Jongchan和Hwang，Eui Jin和Lee，Jong Hyuk和Jong Hyuk和Hong，Jung Hee和Goo，Jin Mo和Park，Jin Mo and Park，Chang Min}，Chang Min}，title = {深度学习算法的开发和验证检测胸部X光片的10种常见异常}，Elocation-id = {2003061}，Year = {2020}，doi = {10.1183/13993003.0303003.03061-2020}188bet官网地址= {我们旨在开发一种深度学习算法，以检测到胸部X光片上的10种常见异常（DLAD-10），并评估其在诊断准确性，报告的及时性和Workflad-100中的影响。108 053名患者使用基于RESNET34的神经网络具有病变特异性通道，可用于10种常见的放射学异常（肺炎，纵隔延伸，肺炎，肺炎，结节/质量，巩固，巩固，心形，固结，固结，线性静脉内，纤维质，纤维化，纤维化，纤维化，Calcification，Calcification和CardiomeMome）。对于外部验证，DLAD-10在当天CT确认的数据集（正常：异常，53：147）和开源数据集（Padchest; Padchest;正常：异常：339：334）的性能与三个放射科医生。在急诊科的另一个数据集中进行了单独的模拟阅读测试，该数据集调整为现实世界中的疾病患病率，包括四个关键，52例紧急和146例非紧急病例。六位放射科医生参加了有或没有DLAD-10.DLAD-10的模拟阅读会议，在接收器操作特征曲线（AUROC）下展示了0.895 {\ textEndEndash} 1.00在CT-CONFIRMED DATASET中的区域。在Padchest数据集中。DLAD-10正确分类的临界异常（95.0 \％[57/60]）比合并放射学家（84.4 \％[152/180]; p = 0.01）。 In simulated reading tests for emergency department patients, pooled readers detected significantly more critical (70.8\% [17/24] versus 29.2\% [7/24]; p=0.006) and urgent (82.7\% [258/312] versus 78.2\% [244/312]; p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean time-to-report critical and urgent radiographs (640.5{\textpm}466.3 versus 3371.0{\textpm}1352.5 s and 1840.3{\textpm}1141.1 versus 2127.1{\textpm}1468.2, respectively; p-values\<0.01) and reduced the mean interpretation time (20.5{\textpm}22.8 versus 23.5{\textpm}23.7 s; p\<0.001).DLAD-10 showed excellent performance, improving radiologists{\textquoteright} performance and shortening the reporting time for critical and urgent cases.FootnotesThis manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.Conflict of interest: Dr. NAM reports grants from National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (grant number: NRF-2018R1A5A1060031), grants from Seoul National University Hospital Research Fund (grant number: 03-2019-0190), during the conduct of the study.Conflict of interest: Dr. Kim reports other from Employee of Lunit Incorporated, during the conduct of the study.Conflict of interest: Dr. Park reports grants from National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (grant number: NRF-2018R1A5A1060031), grants from Seoul National University Hospital Research Fund (grant number: 03-2019-0190), during the conduct of the study.Conflict of interest: Dr. Hwang has nothing to disclose.Conflict of interest: Dr. Lee has nothing to disclose.Conflict of interest: Dr. Hong has nothing to disclose.Conflict of interest: Dr. Goo has nothing to disclose.Conflict of interest: Dr. Park reports grants from National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (grant number: NRF-2018R1A5A1060031), grants from Seoul National University Hospital Research Fund (grant number: 03-2019-0190), during the conduct of the study.}, issn = {0903-1936}, URL = {//www.qdcxjkg.com/content/early/2020/11/05/13993003.03061-2020}, eprint = {//www.qdcxjkg.com/content/early/2020/11/05/13993003.03061-2020.full.pdf}, journal = {European Respiratory Journal} }