期刊文章，Nilakash %A Verstraete, Kenneth %A Stanojevic, Sanja %A Topalovic, Marko %A Aerts, Jean-Marie %A Janssens，Wim %T深度学习算法有助于标准化ATS/ERS肺活量测定的可接受性和可用性标准188bet官网地址定量限制，也需要人工目视检查。目前的方法是费时的，并导致高技术人员之间的可变性。我们提出了一种称为卷积神经网络(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。方法在2011-2012年美国国家健康和营养检查调查(National Health and nutrition Examination Survey USA)的36 873条曲线中，技术人员标记54%的曲线符合ATS/ERS 2005可接受标准，测试的开始和结束均令人满意，但识别出93%的曲线在1秒内有可用的用力呼气量。我们将原始数据处理成最大呼气流量-容积曲线(MEFVC)的图像，计算ATS/ERS可量化标准，并开发cnn，以确定90%的曲线上的操作可接受性和可用性。模型在剩下的10%的曲线上进行了测试。我们计算了Shapley值来解释模型。结果在测试集(n=3738)中，CNN的可接受性准确率为87%，可用性准确率为92%，可用性敏感度为92%，特异性为96%。 They were significantly superior (p<0.0001) to ATS/ERS quantifiable rule-based models. 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 %U //www.qdcxjkg.com/content/erj/56/6/2000603.full.pdf