Sui He, Mo Zhanhao, Wei Ying, Shi Feng, Cheng Kailiang, Liu Lin
China-Japan Union Hospital of Jilin University, Changchun, People's Republic of China.
Shanghai United Imaging Intelligence Co., Ltd., Shanghai, People's Republic of China.
Int J Chron Obstruct Pulmon Dis. 2025 Aug 14;20:2853-2867. doi: 10.2147/COPD.S528988. eCollection 2025.
During the acute phase of obstructive pulmonary disease (COPD), completing a standard pulmonary function test may be challenging for some patients. The goal of this experiment is to develop a machine learning model that uses chest CT images for automated diagnosis and grading of COPD patients, aiming to enhance diagnostic efficiency and accuracy.
The study retrospectively included 173 COPD patients and 176 healthy controls from December 2017 to June 2023. Deep learning segmentation modules were used to automatically segment the obtained chest CT images for lung parenchyma, airway, pulmonary artery, and vein. Imaging features were extracted from these segmented regions. The most reliable and relevant features were selected using Mann-Whitney -test with a significant p-value of 0.05 and the least absolute shrinkage and selection operator (LASSO) method. Machine learning models were established through support vector machine (SVM) classifier in the training set and further tested in the internal testing set. Additional tests were performed on an external testing set with 68 individuals.
In the machine learning model for COPD diagnosis, the image model achieved AUC values of 0.981 and 0.977 in the training and testing sets, with corresponding accuracies of 0.949 and 0.956 respectively. For COPD severity grading, the image model obtained AUC values of 0.889 and 0.796 in the training and testing sets, along with accuracies of 0.784 and 0.719.
The machine learning model based on chest CT images can accurately predict lung function, which can assist in the diagnosis and severity grading of COPD.
在慢性阻塞性肺疾病（COPD）急性期，完成标准肺功能测试对一些患者而言可能具有挑战性。本实验的目的是开发一种机器学习模型，该模型利用胸部CT图像对COPD患者进行自动诊断和分级，旨在提高诊断效率和准确性。
本研究回顾性纳入了2017年12月至2023年6月期间的173例COPD患者和176例健康对照。使用深度学习分割模块对获取的胸部CT图像进行自动分割，以划分肺实质、气道、肺动脉和肺静脉。从这些分割区域中提取影像特征。使用p值为0.05的曼-惠特尼检验和最小绝对收缩和选择算子（LASSO）方法选择最可靠和相关的特征。通过支持向量机（SVM）分类器在训练集中建立机器学习模型，并在内部测试集中进一步测试。对68名个体的外部测试集进行了额外测试。
在COPD诊断的机器学习模型中，图像模型在训练集和测试集中的AUC值分别为0.981和0.977，相应的准确率分别为0.949和0.956。对于COPD严重程度分级，图像模型在训练集和测试集中的AUC值分别为0.889和0.796，准确率分别为0.784和0.719。
基于胸部CT图像的机器学习模型能够准确预测肺功能，可辅助COPD的诊断和严重程度分级。
