Automatic recognition and differentiation of pulmonary contusion and bacterial pneumonia based on deep learning and radiomics.
Authors
Affiliations (4)
Affiliations (4)
- Medical Imaging Department, Chongqing Emergency Medical Center, School of Medicine, Chongqing University Central Hospital, Chongqing University, No. 1, Jiankang Road, Yuzhong District, Chongqing, 400014, China.
- Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, 200030, China.
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, 404100, China. [email protected].
- Medical Imaging Department, Chongqing Emergency Medical Center, School of Medicine, Chongqing University Central Hospital, Chongqing University, No. 1, Jiankang Road, Yuzhong District, Chongqing, 400014, China. [email protected].
Abstract
In clinical work, there are difficulties in distinguishing pulmonary contusion(PC) from bacterial pneumonia(BP) on CT images by the naked eye alone when the history of trauma is unknown. Artificial intelligence is widely used in medical imaging, but its diagnostic performance for pulmonary contusion is unclear. In this study, artificial intelligence was used for the first time to identify lung contusion and bacterial pneumonia, and its diagnostic performance was compared with that of manual. In this retrospective study, 2179 patients between April 2016 and July 2022 from two hospitals were collected and divided into a training set, an internal validation set, an external validation set. PC and BP were automatically recognized, segmented using VB-net and radiomics features were automatically extracted. Four machine learning algorithms including Decision Trees, Logistic Regression, Random Forests and Support Vector Machines(SVM) were using to built the models. De-long test was used to compare the performance among models. The best performing model and four radiologists diagnosed the external validation set, and compare the diagnostic efficacy of human and artificial intelligence. VB-net automatically detected and segmented PC and BP. Among the four machine learning models we've built, De-long test showed that SVM model had the best performance, with AUC, accuracy, sensitivity, and specificity of 0.998 (95% CI: 0.995-1), 0.980, 0.979, 0.982 in the training set, 0.891 (95% CI: 0.854-0.928), 0.979, 0.750, 0.860 in the internal validation set, 0.885 (95% CI: 0.850-0.920), 0.903, 0.976, 0.794 in the external validation set. The diagnostic ability of the SVM model was superior to that of human (P < 0.05). Our VB-net automatically recognizes and segments PC and BP in chest CT images. SVM model based on radiomics features can quickly and accurately differentiate between them with higher accuracy than experienced radiologist.