PDF(Pubmed)
Abstract：
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients.
METHODS: In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads.
RESULTS: The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively.
CONCLUSIONS: The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging.
METHODS: In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads.
RESULTS: The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively.
CONCLUSIONS: The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging.
摘要：
背景：慢性阻塞性肺疾病（COPD）在当前的金标准测量肺功能测试（PFT）中被低估。对于COPD的早期检测和严重程度评估，一个更敏感和简单的选择可以使医生和患者受益。
方法：在这项多中心回顾性研究中，收集和处理1055名参与者的正面胸部X线（CXR）图像和相关临床信息。对不同的深度学习算法和迁移学习模型进行了训练，以根据666名受试者的临床数据和CXR图像对COPD进行分类，并在基于284名参与者的内部测试集中进行了验证。还进行了包括105名参与者的外部测试，以验证学习算法在诊断COPD中的泛化能力。同时,该模型被进一步用于通过预测不同分级来评估COPD的疾病严重程度.
结果：Ensemble模型通过在内部测试中同时提取临床参数和CXR图像的融合特征，在区分COPD方面显示AUC为0.969，优于仅使用临床参数（AUC=0.963）或图像（AUC=0.946）的模型。对于外部测试集，在根据临床参数和CXR图像预测COPD时,AUC略微下降至0.934.当应用Ensemble模型来确定COPD的疾病严重程度时，三分类和五分类的AUC分别达到0.894和0.852。
结论：本研究使用DL算法筛查COPD并根据CXR成像和临床参数预测疾病严重程度。模型表现出良好的性能，该方法可能是一种有效的病例发现工具，辐射剂量低，用于COPD的诊断和分期。
方法：在这项多中心回顾性研究中，收集和处理1055名参与者的正面胸部X线（CXR）图像和相关临床信息。对不同的深度学习算法和迁移学习模型进行了训练，以根据666名受试者的临床数据和CXR图像对COPD进行分类，并在基于284名参与者的内部测试集中进行了验证。还进行了包括105名参与者的外部测试，以验证学习算法在诊断COPD中的泛化能力。同时,该模型被进一步用于通过预测不同分级来评估COPD的疾病严重程度.
结果：Ensemble模型通过在内部测试中同时提取临床参数和CXR图像的融合特征，在区分COPD方面显示AUC为0.969，优于仅使用临床参数（AUC=0.963）或图像（AUC=0.946）的模型。对于外部测试集，在根据临床参数和CXR图像预测COPD时,AUC略微下降至0.934.当应用Ensemble模型来确定COPD的疾病严重程度时，三分类和五分类的AUC分别达到0.894和0.852。
结论：本研究使用DL算法筛查COPD并根据CXR成像和临床参数预测疾病严重程度。模型表现出良好的性能，该方法可能是一种有效的病例发现工具，辐射剂量低，用于COPD的诊断和分期。
