Using the DCJM deep-learning model to diagnosis drug-resistant lymph node tuberculosis based on ultrasound images: A Multicenter Study

Our objective was to develop a deep learning model based on grey-scale ultrasound (GUS) images for predicting whether lymph node tuberculosis (LNTB) of the neck is drug resistant. The GUS images of 297 cases of cervical LNTB confirmed to be drug-resistant or sensitive by laboratory examination in three hospitals were retrospectively collected. A target detection-image classification joint learning method (DCJM) model combining target detection and image classification was constructed from the training set, and the diagnostic efficacy of the DCJM model was evaluated by the data from the internal validation set, Test A and Test B. We used mean average precision (mAP) to assess the accuracy of target detection in the DCJM model, The mAP_0.5 and mAP_0.5:0.95 of the DCJM model for LNTB detection were 0.995 and 0.897, respectively. The area under the curve (AUC) of this model in the training set, validation set, Test A, and Test B were 0.992 (95% CI, 0.972-1.000), 0.851 (95% CI, 0.733–0.948), 0.727 (95% CI, 0.488–0.924), and 0.777 (95% CI, 0.644-0.900), respectively. The DCJM model has a strong detection function as well as a good predictive value for drug-resistant LNTB, providing valuable information for individualized treatment decisions.