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Objective: The outbreak of coronavirus disease (COVID‐19) coincided with the season of influenza A pneumonia, a common respiratory infectious disease. Therefore, this study compared ultrasonography and computed tomography (CT) for the diagnosis of the two diseases. Methods: Patients with COVID‐19 or influenza A infection hospitalized at our hospital were included. The patients were examined by ultrasonography every day. The CT examination results within 1 day before and after the day of the highest ultrasonography score were selected as the controls. The similarities and differences between the ultrasonography and CT results in the two groups were compared. Results: There was no difference between the ultrasonography and CT scores (P =.307) for COVID‐19, while there was a difference between ultrasonography and CT scores for influenza A pneumonia (P =.024). The ultrasonography score for COVID‐19 was higher than that for influenza A pneumonia (P =.000), but there was no difference between the CT scores (P =.830). For both diseases, there was no difference in ultrasonography and CT scores between the left and right lungs; there were differences between the CT scores of the upper and middle lobes, as well as between the upper and lower lobes of the lungs; however, there was no difference between the lower and middle lobes of the lungs. Conclusion: Ultrasonography is equivalent to the gold standard CT for diagnosing and monitoring the progression of COVID‐19. Because of its convenience, ultrasonography has important application value. Furthermore, the diagnostic value of ultrasonography for COVID‐19 is higher than that for influenza A pneumonia. [ABSTRACT FROM AUTHOR]

Objective: The non-invasive discrimination of significant fibrosis (≥ F2) in patients with chronic liver disease (CLD) is clinically critical but technically challenging. We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.0) based on our previous DLRE model to achieve significantly improved performance in ≥ F2 evaluation. Methods: This was a retrospective multicenter study with 807 CLD patients and 4842 images from three hospitals. All of these patients have liver biopsy results as referenced standard. Multichannel deep learning radiomics models were developed. Elastography images, gray-scale images of the liver capsule, gray-scale images of the liver parenchyma, and serological results were gradually integrated to establish different diagnosis models, and the optimal model was selected for assessing ≥ F2. Its accuracy was thoroughly investigated by applying different F0–1 prevalence cohorts and independent external test cohorts. Analysis of receiver operating characteristic (ROC) curves was performed to calculate the area under the ROC curve (AUC) for significance of fibrosis (≥ F2) and cirrhosis (F4). Results: The AUC of the DLRE2.0 model significantly increased to 0.91 compared with the DLRE model (AUC 0.83) when evaluating ≥ F2 (p = 0.0167). However, it did not show statistically significant differences as integrating gray-scale images and serological data into the DLRE2.0 model. AUCs of DLRE and DLRE2.0 increased, when there was higher F0–1 prevalence. All radiomics models had good robustness in the independent external test cohort. Conclusions: DLRE2.0 was the most suitable model for staging significant fibrosis while considering the balance of diagnostic accuracy and clinical practicability. Key Points: • The non-invasive discrimination of significant fibrosis (≥ F2) in patients with chronic liver disease (CLD) is clinically critical but technically challenging. • We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.0) based on our previous DLRE model to achieve significantly improved performance in ≥ F2 evaluation. • Our study based on 807 CLD patients and 4842 images with liver biopsy found that DLRE2.0 was the most suitable model for staging significant fibrosis while considering the balance of diagnostic accuracy and clinical practicability. [ABSTRACT FROM AUTHOR]