深度学习算法有助于标准化 ATS/ERS 肺活量可接受性和可用性标准,European Respiratory Journal

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深度学习算法有助于标准化 ATS/ERS 肺活量可接受性和可用性标准
European Respiratory Journal ( IF 16.6 ) Pub Date : 2020-06-11 , DOI: 10.1183/13993003.00603-2020
Nilakash Das ₁ , Kenneth Verstraete ₁ , Sanja Stanojevic ₂ , Marko Topalovic _{1,

3} , Jean-Marie Aerts ₄ , Wim Janssens ₅

Affiliation

基本原理虽然美国胸科学会 (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。当前的方法非常耗时，并且导致技术人员间的高度可变性。我们提出了一种称为卷积神经网络 (CNN) 的深度学习方法，以标准化肺活量操作的可接受性和可用性。方法和方法在 2011 年至 2012 年美国国家健康和营养检查调查的 36 873 条曲线中，技术人员将 54% 的曲线标记为符合 ATS/ERS 2005 可接受标准且测试开始和结束均令人满意，但确定 93% 的曲线具有1 秒内可用的用力呼气量。我们将原始数据处理成最大呼气流量-容积曲线 (MEFVC) 的图像，计算了 ATS/ERS 可量化标准并开发了 CNN 来确定 90% 曲线的机动可接受性和可用性。这些模型在其余 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%，后者显示出高灵敏度 (92%) 和特异性 (96%)。它们明显优于 (p<0.0001) 于 ATS/ERS 可量化的基于规则的模型。Shapley 解释显示 MEFVC<1 s（呼气第一秒内的 MEFVC 模式）和体积-时间的平台是确定可接受性的最重要因素，而 MEFVC<1 s 完全决定了可用性。结论 CNN 确定了肺活量曲线中的相关属性，以标准化 ATS/ERS 机动可接受性和可用性建议，并进一步提供个体机动反馈。我们的算法结合了熟练技术人员的视觉经验和 ATS/ERS 定量规则，使肺量测定质量控制的关键阶段自动化。开发了深度学习模型以标准化 ATS/ERS 肺活量可接受性和可用性标准。这种方法减少了技术人员的可变性，并向用户提供即时反馈 https://bit.ly/3dNFe1i 开发了深度学习模型以标准化 ATS/ERS 肺活量可接受性和可用性标准。这种方法减少了技术人员的可变性，并向用户提供即时反馈 https://bit.ly/3dNFe1i 开发了深度学习模型以标准化 ATS/ERS 肺活量可接受性和可用性标准。这种方法减少了技术人员的可变性，并向用户提供即时反馈 https://bit.ly/3dNFe1i

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Deep learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria

Rationale While American Thoracic Society (ATS)/European Respiratory Society (ERS) quality control criteria for spirometry include several quantitative limits, it also requires manual visual inspection. The current approach is time consuming and leads to high intertechnician variability. We propose a deep-learning approach called convolutional neural network (CNN), to standardise spirometric manoeuvre acceptability and usability. Methods and methods In 36 873 curves from the National Health and Nutritional Examination Survey USA 2011–2012, technicians labelled 54% of curves as meeting ATS/ERS 2005 acceptability criteria with satisfactory start and end of test, but identified 93% of curves with a usable forced expiratory volume in 1 s. We processed raw data into images of maximal expiratory flow–volume curve (MEFVC), calculated ATS/ERS quantifiable criteria and developed CNNs to determine manoeuvre acceptability and usability on 90% of the curves. The models were tested on the remaining 10% of curves. We calculated Shapley values to interpret the models. Results In the test set (n=3738), CNN showed an accuracy of 87% for acceptability and 92% for usability, with the latter demonstrating a high sensitivity (92%) and specificity (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

更新日期：2020-06-11

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