Intelligent interpretation of four lung ultrasonographic features with split attention based deep learning model

Objectives: To develop and validate a deep learning (DL) model based on multi-scale features of Lung ultrasound (LUS) and attention mechanism to detect A-line, B-line, pulmonary consolidation, and pleural effusion caused by pulmonary gas–liquid ratio variations. Methods: A total of 6000 LUS images were prospectively collected from 3966 patients, of which 5545 images were selected. All the images were randomly divided into the training set (4,436 images) and the testing set (1,109 images) with a ratio of 4:1. Faced on multi-scale features of LUS, an end-to-end deep learning model based on multi-scale split attention and Mish function was proposed to automatically identify the four LUS features. Results: The overall prediction AUC, accuracy, specificity, and sensitivity of the independent test set were 99.76%, 98.20%, 99.41%, and 98.27%, respectively, and achieved significant and consistent improvement as compared to other deep learning baselines. Conclusions: Our proposed model could interpret the four important LUS features intelligently and be adopted as a support system in the routine diagnosis of an emergency clinician. Significance: This study can not only assist clinicians in recognizing common lung lesions but also provide a new method for the realization of high-quality intelligent diagnosis.