Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography

• Published in: BMC pulmonary medicine Vol. 25; no. 1; pp. 339 - 12
• Main Authors: Jia, Ruiting, Liu, Baozhi, Ali, Mohsin
• Format: Journal Article
• Language: English
• Published: London BioMed Central 12.07.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Algorithms; Analysis; Annotations; Artificial Intelligence; Artificial intelligence and machine learning: applications in pulmonary medicine; Care and treatment; Classification; Clinical medicine; Computed tomography; Convolutional neural networks; Critical Care Medicine; CT imaging; Datasets; Deep Learning; Diagnosis; Diagnostic framework; General Data Protection Regulation; Health aspects; Humans; Intensive; Internal Medicine; Lung diseases; Lung Neoplasms - diagnostic imaging; Lung nodules; Machine learning; Medical imaging; Medical research; Medicine; Medicine & Public Health; Multiple Pulmonary Nodules - diagnostic imaging; Neural networks; Neural Networks, Computer; Patients; Pneumology/Respiratory System; Pulmonary nodules; Radiographic Image Interpretation, Computer-Assisted - methods; Sensitivity and Specificity; Solitary Pulmonary Nodule - diagnostic imaging; Support Vector Machine; Tomography, X-Ray Computed - methods; Pakistan; Computed tomography; Convolutional neural networks; Diagnostic framework; Artificial intelligence; Pulmonary nodules
• ISSN: 1471-2466; 1471-2466
• DOI: 10.1186/s12890-025-03806-7

Background 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. Objective This study aims to develop an Artificial Intelligence (AI) diagnostic scheme that improves the performance of identifying and categorizing pulmonary nodules using CT scans. Method 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. Results 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. Conclusion 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.