PT -期刊文章盟Das Nilakash盟——是肯尼斯盟——Stanojevic Sanja盟——Topalovic Marko盟,Aerts - AU -詹森,Wim TI -深度学习算法有助于规范ATS /人肺活量的可接受性和可用性标准援助- 10.1183/13993003.00603 -2020 DP - 2020年12月01 TA -欧洲呼吸杂志》第六PG - 2000603 - 56 IP - 6 4099 - //www.qdcxjkg.com/content/56/6/2000603.short 4100 - //www.qdcxjkg.com/content/56/6/2000603.full所以欧元和J2020 12月01;56 AB -基本原理而美国胸科学会(ATS) /欧洲呼吸学会(ERS)质量控制标准肺量测定法包括多个定量限制,188bet官网地址它还需要人工目视检查。当前的方法是费时和导致高intertechnician可变性。我们提出一种深度学习的方法称为卷积神经网络(CNN),标准化肺活量的策略可接受性和可用性。873年36曲线方法和方法从美国全国健康和营养调查2011 - 2012年,技术人员贴上54%的曲线是ATS / 2005人队可接受性标准会见满意的开始和结束测试,但发现93%的曲线可用用力呼气量在1 s。我们处理原始数据到图像的最大呼气(MEFVC)煤层瓦斯曲线,计算ATS /人发达cnn来确定可量化的标准和策略可接受性和可用性在90%的曲线。剩下的10%的模型测试曲线。我们计算夏普利值解释模型。结果在测试组(n = 3738), CNN显示87%的准确性,可接受性为92%,可用性,后者展示高敏感性(92%)和特异性(96%)。他们明显的优越感(术中;0.0001)at /人可量化的基于规则的模型。 Shapley interpretation revealed MEFVC<1 s (MEFVC pattern within first second of exhalation) and plateau in volume–time were most important in determining acceptability, while MEFVC<1 s entirely determined usability.Conclusion The CNNs identified relevant attributes in spirometric curves to standardise ATS/ERS manoeuvre acceptability and usability recommendations, and further provides individual manoeuvre feedback. Our algorithm combines the visual experience of skilled technicians and ATS/ERS quantitative rules in automating the critical phase of spirometry quality control.Deep-learning models were developed to standardise ATS/ERS spirometric acceptability and usability criteria. This approach reduces the intertechnician variability and provides instant feedback to the user https://bit.ly/3dNFe1i