Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography
Abstract 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 de...
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2025-07-01
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| Series: | BMC Pulmonary Medicine |
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| Online Access: | https://doi.org/10.1186/s12890-025-03806-7 |
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| author | Ruiting Jia Baozhi Liu Mohsin Ali |
| author_facet | Ruiting Jia Baozhi Liu Mohsin Ali |
| author_sort | Ruiting Jia |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-0590e3c2b676416d9fc5decadbdce8d6 |
| institution | Kabale University |
| issn | 1471-2466 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj-art-0590e3c2b676416d9fc5decadbdce8d62025-08-20T03:42:43ZengBMCBMC Pulmonary Medicine1471-24662025-07-0125111210.1186/s12890-025-03806-7Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomographyRuiting Jia0Baozhi Liu1Mohsin Ali2Image center, Affiliated Hospital of Inner Mongolia Minzu UniversityImage center, Affiliated Hospital of Inner Mongolia Minzu UniversityDepartment of Chemistry, Hazara UniversityAbstract 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.https://doi.org/10.1186/s12890-025-03806-7Artificial intelligencePulmonary nodulesComputed tomographyConvolutional neural networksDiagnostic framework |
| spellingShingle | Ruiting Jia Baozhi Liu Mohsin Ali Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography BMC Pulmonary Medicine Artificial intelligence Pulmonary nodules Computed tomography Convolutional neural networks Diagnostic framework |
| title | Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography |
| title_full | Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography |
| title_fullStr | Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography |
| title_full_unstemmed | Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography |
| title_short | Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography |
| title_sort | establishing an ai based diagnostic framework for pulmonary nodules in computed tomography |
| topic | Artificial intelligence Pulmonary nodules Computed tomography Convolutional neural networks Diagnostic framework |
| url | https://doi.org/10.1186/s12890-025-03806-7 |
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