Application of deep learning to automated diagnosis of computed tomography images of pulmonary infections

Background: Currently, clinical pathogen diagnosis primarily consists of two methods: high-throughput sequencing and traditional culture. However, considering the evident limitations in terms of time and cost associated with the diagnostic process, there is an urgent need for a highly sensitive diagnostic and detection system to complement traditional methods. Therefore, this study proposes a Mix-inResNet model based on Convolutional Neural Network (CNN) aiming to enhance the accuracy of pulmonary infection diagnosis and effectively detect various types of lung diseases. Purpose : This study aimed to develop a deep learning model, Mix-inResNet, based on computed tomography (CT) scannings for the preclassification of pneumonia-related pathologies. Methods : A total of 273 cases of patients with pulmonary infections were included in this study, and both metagenomic next-generation sequencing (mNGS) and traditional culture methods were employed to comfirm the pathology. We possess a substantial collection of 2,858 imaging datasets with notable levels of infection were included to compare the diagnostic efficacy of the Mix-inResNet model against other models (ResNet 101, Inception V3, and VGG 16), as well as clinicians and imaging specialists. Results :The Mix-inResNet model achieved precision, accuracy, F1 score, and Gwet's kappa values of 94.61%, 94.41%, 94.22%, and 93.09%, respectively. For bacterial, fungal, viral, and tuberculous pneumonia, as well as normal chest CT tests, the sensitivities were 88.14%, 92.11%, 91.94%, 98.00%, and 100%, respectively. The specificities were 98.68%, 100.00%, 97.77%, 97.46%, and 99.04%, respectively, with 95% confidence intervals ranging from 0.88 to 0.97. Compared to other models or manual identification, the Mix-inResNet model demonstrated the highest area under the curve (AUC) values and achieved more accurate classification. Confusion matrix results indicate that the Mix-inResNet model provides an excellent understanding and accurate identification of pneumonia. Additionally, Grad-CAM technology was utilized to localize the site of pneumonia, offering more intuitive and clear results. Conclusions : The Mix-inResNet model performs at a level comparable to imaging specialists in distinguishing pneumonia classifications. It effectively reduces the misdiagnosis risk and can promptly deliver relevant pathogenic information. This provides clinicians with rapid and accurate diagnostic support, aiding the implementation of early therapeutic interventions.