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Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia

PURPOSE: To develop and validate a 3D-convolutional neural network (3D-CNN) model based on chest CT for differentiating active pulmonary tuberculosis (APTB) from community-acquired pneumonia (CAP). MATERIALS AND METHODS: Chest CT images of APTB and CAP patients diagnosed in two imaging centers (n = ...

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Detalles Bibliográficos
Autores principales: Han, Dong, Chen, Yibing, Li, Xuechao, Li, Wen, Zhang, Xirong, He, Taiping, Yu, Yong, Dou, Yuequn, Duan, Haifeng, Yu, Nan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Milan 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9793822/
https://www.ncbi.nlm.nih.gov/pubmed/36574111
http://dx.doi.org/10.1007/s11547-022-01580-8
Descripción
Sumario:PURPOSE: To develop and validate a 3D-convolutional neural network (3D-CNN) model based on chest CT for differentiating active pulmonary tuberculosis (APTB) from community-acquired pneumonia (CAP). MATERIALS AND METHODS: Chest CT images of APTB and CAP patients diagnosed in two imaging centers (n = 432 in center A and n = 61 in center B) were collected retrospectively. The data in center A were divided into training, validation and internal test sets, and the data in center B were used as an external test set. A 3D-CNN was built using Keras deep learning framework. After the training, the 3D-CNN selected the model with the highest accuracy in the validation set as the optimal model, which was applied to the two test sets in centers A and B. In addition, the two test sets were independently diagnosed by two radiologists. The 3D-CNN optimal model was compared with the discrimination, calibration and net benefit of the two radiologists in differentiating APTB from CAP using chest CT images. RESULTS: The accuracy of the 3D-CNN optimal model was 0.989 and 0.934 with the internal and external test set, respectively. The area-under-the-curve values with the 3D-CNN model in the two test sets were statistically higher than that of the two radiologists (all P < 0.05), and there was a high calibration degree. The decision curve analysis showed that the 3D-CNN optimal model had significantly higher net benefit for patients than the two radiologists. CONCLUSIONS: 3D-CNN has high classification performance in differentiating APTB from CAP using chest CT images. The application of 3D-CNN provides a new automatic and rapid diagnosis method for identifying patients with APTB from CAP using chest CT images.