Jia Ruiting, Liu Baozhi, Ali Mohsin
Image center, Affiliated Hospital of Inner Mongolia Minzu University, Tongliao, 028000, China.
Department of Chemistry, Hazara University, Mansehra, 21300, Pakistan.
BMC Pulm Med. 2025 Jul 12;25(1):339. doi: 10.1186/s12890-025-03806-7.
Pulmonary nodules seen by computed tomography (CT) can be benign or malignant, and early detection is important for optimal management. The existing manual methods of identifying nodules have limitations, such as being time-consuming and erroneous.
This study aims to develop an Artificial Intelligence (AI) diagnostic scheme that improves the performance of identifying and categorizing pulmonary nodules using CT scans.
The proposed deep learning framework used convolutional neural networks, and the image database totaled 1,056 3D-DICOM CT images. The framework was initially preprocessing, including lung segmentation, nodule detection, and classification. Nodule detection was done using the Retina-UNet model, while the features were classified using a Support Vector Machine (SVM). Performance measures, including accreditation, sensitivity, specificity, and the AUROC, were used to evaluate the model's performance during training and validation.
Overall, the developed AI model received an AUROC of 0.9058. The diagnostic accuracy was 90.58%, with an overall positive predictive value of 89% and an overall negative predictive value of 86%. The algorithm effectively handled the CT images at the preprocessing stage, and the deep learning model performed well in detecting and classifying nodules.
The application of the new diagnostic framework based on AI algorithms increased the accuracy of the diagnosis compared with the traditional approach. It also provides high reliability for detecting pulmonary nodules and classifying the lesions, thus minimizing intra-observer differences and improving the clinical outcome. In perspective, the advancements may include increasing the size of the annotated data-set and fine-tuning the model due to detection issues of non-solitary nodules.
计算机断层扫描（CT）发现的肺结节可能是良性或恶性的，早期检测对于优化治疗至关重要。现有的手动识别结节的方法存在局限性，例如耗时且容易出错。
本研究旨在开发一种人工智能（AI）诊断方案，以提高使用CT扫描识别和分类肺结节的性能。
所提出的深度学习框架使用卷积神经网络，图像数据库共有1056张3D-DICOM CT图像。该框架首先进行预处理，包括肺部分割、结节检测和分类。使用Retina-UNet模型进行结节检测，同时使用支持向量机（SVM）对特征进行分类。在训练和验证过程中，使用包括准确率、灵敏度、特异性和曲线下面积（AUROC）在内的性能指标来评估模型的性能。
总体而言，所开发的AI模型的AUROC为0.9058。诊断准确率为90.58%，总体阳性预测值为89%，总体阴性预测值为86%。该算法在预处理阶段有效地处理了CT图像，深度学习模型在检测和分类结节方面表现良好。
与传统方法相比，基于AI算法的新诊断框架的应用提高了诊断的准确性。它还为检测肺结节和对病变进行分类提供了高可靠性，从而最大限度地减少观察者内部差异并改善临床结果。从长远来看，由于非孤立结节的检测问题，进展可能包括增加注释数据集的大小并对模型进行微调。
