TY - T1的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JF -欧洲呼吸杂志》乔和J - 10.1183/13993003.00603 -2020欧元六世- 56 - 6 SP - 2000603 AU - Das, Nilakash盟——是肯尼斯盟——Stanojevic Sanja盟——Topalovic Marko盟,Aerts虽然美国胸科学会(ATS)/欧洲呼吸学会(ERS)的肺活量测定质量控制标准包括几个定量限制，但它也需要人工目视检查。188bet官网地址目前的方法是耗时的，并导致高的内部技术变化。我们提出了一种名为卷积神经网络(CNN)的深度学习方法，以标准化肺活量操纵的可接受性和可用性。在2011-2012年美国国家健康和营养检查调查(National Health and nutrition Examination Survey USA)的36873条曲线中，技术人员将54%的曲线标记为符合ATS/ERS 2005可接受标准，测试开始和结束均令人满意，但检测出93%的曲线在1秒内具有可用的强制呼气量。我们将原始数据处理成最大呼气流量-容积曲线(MEFVC)图像，计算ATS/ERS可量化标准，并开发cnn以确定90%曲线上的动作可接受性和可用性。模型在剩余10%的曲线上进行了测试。我们计算Shapley值来解释模型。在测试集(n=3738)中，CNN显示了87%的可接受性和92%的可用性，后者显示了高灵敏度(92%)和特异性(96%)。它们明显优于ATS/ERS基于规则的可量化模型(p<0.0001)。 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 ER -