Background: Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. In this study, we determined the ultrasound manifestations of the lung associated with COVID-19 pneumonia, and obtained the ultrasound image changes of the patients from the initial diagnosis to rehabilitation. Methods: The purpose of this study is to establish a lung involvement assessment model based on deep learning. A channel attention classification method based on squeeze-and-excitation network combining with ResNeXt (SE_ResNeXt) is proposed, which can automatically learn the importance of different channel features, so as to achieve selective learning of channels and further achieve more accurate classification results. Results and conclusion: Among 104 patients' data from multicenter and multi-mode ultrasound, the diagnostic model can achieve 97.11% accuracy. The lung involvement severity of COVID-19 pneumonia and the trend of lesion were evaluated quantitatively.

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.

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.

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.

oronavirus disease 2019 (COVID-19) is an acute infectious disease caused by a novel coronavirus previously unknown in humans before, SARS-CoV-2 [1]. The initial symptoms of the patients are mostly fever, fatigue, and dry cough, with the gradual appearance of breathing difficulties and other serious manifestations [2]. Most patients have a good prognosis, although some severe cases may develop acute respiratory distress syndrome or septic shock, or even death. At present, there has been no effective antiviral drugs against the pathogen, and treatment has primarily involved isolation and symptomatic alleviation [3].At present, the imaging evidence of this disease is mainly based on high-resolution computed tomography (HRCT). Pulmonary ultrasound has been used in clinical practice in the differentiation and diagnosis of pulmonary effusion lesions such as pleural effusion, pneumothorax, as well as in evaluation of dyspnea and acute respiratory failure. It has also been used in the guidance and monitoring of treatment [4]. As a rapid, radiation-free and convenient method for the visual examination of lung diseases, pulmonary ultrasound plays a certain role in the