Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images.

BACKGROUND: Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. Ultrasound sonograms have been confirmed to illustrate damage to a person's lungs, which means that the correct classification and scoring of a patient's sonogram can be used to assess lung involvement. METHODS: The purpose of this study was to establish a lung involvement assessment model based on deep learning. A novel multimodal channel and receptive field attention network combined with ResNeXt (MCRFNet) was proposed to classify sonograms, and the network can automatically fuse shallow features and determine the importance of different channels and respective fields. Finally, sonogram classes were transformed into scores to evaluate lung involvement from the initial diagnosis to rehabilitation. RESULTS AND CONCLUSION: Using multicenter and multimodal ultrasound data from 104 patients, the diagnostic model achieved 94.39% accuracy, 82.28% precision, 76.27% sensitivity, and 96.44% specificity. The lung involvement severity and the trend of COVID-19 pneumonia were evaluated quantitatively.

Clinical study on the evaluation of liver fibrosis by ultrasound elastography combined with platelet count model.

OBJECTIVES: To establish the prediction model of liver fibrosis by combining ultrasound elastography and platelet count and evaluates its clinical value. METHODS: 146 patients with chronic liver diseases(CLD) admitted to our hospital from July 2020 to July 2022 were collected for liver biopsy pathological examination, and the results of ultrasound elastography (liver hardness value) and serological indicators were collected. Based on the results of Spearman correlation test and multiple linear regression model, the prediction model of liver fibrosis using ultrasound elastography combined with platelet count was constructed and verified. RESULTS: The AUC of transient elastography combined with platelet count model (FSP) in the diagnosis of S2, S3 and S4 phases of liver fibrosis was 0.665, 0.835 and 0.909, with specificity of 81.5%, 90.0% and 100%. The AUC of sound touch elastography combined with platelet count model (STEP) in diagnosing S2, S3 and S4 phases of liver fibrosis was 0.685, 0.810 and 0884, with specificity of 96.3%, 90.0% and 83.3%, which are higher than APRI, FIB-4, FORNS, AAR and other models. CONCLUSION: Ultrasound elastography combined with platelet count model has good diagnostic efficacy for liver fibrosis.