Dong Hantian, Zhu Biaokai, Kong Xiaomei, Su Xuesen, Liu Ting, Zhang Xinri
First Department of Geriatric Diseases, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China.
Department of Pulmonary and Critical Care Medicine, National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China.
Biomed Eng Online. 2025 Jan 27;24(1):7. doi: 10.1186/s12938-025-01333-4.
Coal workers' pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future.
All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers. We segmented regions of interest according to the diagnostic results, which were evaluated by radiologists. These regions were then classified regions into four categories. We employed an efficient deep learning model and various image augmentation techniques (DenseNet-ECA). The classification performance of the different deep learning models was assessed, and receiver operating characteristic (ROC) curves and accuracy (ACC) were used to determine the optimal algorithm for classifying CWP clinical imaging features obtained from HRCT images.
Four primary clinical imaging features in HRCT images, with a total of more than 1700 regions of interest (ROIs), were annotated, augmented, and used as a training set for tenfold cross-validation to generate the model. We selected DenseNet-Attention Net as the optimal model through assessing the performance of different classification algorithms, which yielded an average area under the ROC curve (AUC) of 0.98, and all clinical imaging features were classified with an AUC greater than 0.92. For the individual classifications, the AUCs were as follows: small miliary opacities, 0.99; nodular opacities, 1.0; interstitial changes, 0.92; and emphysema, 1.0.
We successfully applied a data augmentation strategy to develop a deep learning model by combining DenseNet with ECA-Net. We used our novel model to automatically classify CWP clinical imaging features from 2D HRCT images. This successful application of a deep learning-data augmentation algorithm can help clinical radiologists by providing reliable diagnostic information for classification.
Chinese Clinical Trial Registry, ChiCTR2100050379. Registered on 27 August 2021, https://www.chictr.org.cn/bin/project/edit?pid=132619 .
煤工尘肺是一种伴有相当多肺部并发症的慢性职业性肺病，包括一些不可逆的肺部疾病，这些疾病过于复杂，难以通过胸部X光准确识别。高分辨率计算机断层扫描的临床影像特征分类可能会成为未来诊断尘肺病的有力临床工具。
本研究中呈现的所有胸部高分辨率计算机断层扫描（HRCT）医学图像均来自217名煤工尘肺（CWP）患者和接触粉尘的工人。我们根据放射科医生评估的诊断结果对感兴趣区域进行分割。然后将这些区域分为四类。我们采用了一种高效的深度学习模型和各种图像增强技术（DenseNet-ECA）。评估了不同深度学习模型的分类性能，并使用受试者工作特征（ROC）曲线和准确率（ACC）来确定对从HRCT图像获得的CWP临床影像特征进行分类的最佳算法。
对HRCT图像中的四种主要临床影像特征进行了标注、增强，共获得1700多个感兴趣区域（ROI），并将其用作十折交叉验证的训练集以生成模型。通过评估不同分类算法的性能，我们选择DenseNet-注意力网络作为最佳模型，其ROC曲线下平均面积（AUC）为0.98，所有临床影像特征分类的AUC均大于0.92。对于各个分类，AUC如下：小结节状阴影为0.99；结节状阴影为1.0；间质改变为0.92；肺气肿为1.0。
我们成功应用数据增强策略，通过将DenseNet与ECA-Net相结合开发了一个深度学习模型。我们使用我们的新模型对二维HRCT图像中的CWP临床影像特征进行自动分类。深度学习-数据增强算法的这一成功应用可为临床放射科医生提供可靠的诊断信息以进行分类，从而提供帮助。
中国临床试验注册中心，ChiCTR2100050379。于2021年8月27日注册，https://www.chictr.org.cn/bin/project/edit?pid=132619 。
