Journal of X-ray science and technology
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Comparative Study
A retrospective study of the initial chest CT imaging findings in 50 COVID-19 patients stratified by gender and age.
To retrospectively analyze and stratify the initial clinical features and chest CT imaging findings of patients with COVID-19 by gender and age. ⋯ COVID-19 has various clinical and imaging appearances. However, it has certain characteristics that can be stratified. CT plays an important role in disease diagnosis and early intervention.
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Case Reports
Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: three case reports.
In this article, we analyze and report cases of three patients who were admitted to Renmin Hospital, Wuhan University, China, for treating COVID-19 pneumonia in February 2020 and were unresponsive to initial treatment of steroids. They were then received titrated steroids treatment based on the assessment of computed tomography (CT) images augmented and analyzed with the artificial intelligence (AI) tool and output. Three patients were finally recovered and discharged. The result indicated that sufficient steroids may be effective in treating the COVID-19 patients after frequent evaluation and timely adjustment according to the disease severity assessed based on the quantitative analysis of the images of serial CT scans.
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To investigate the characterization of breast lesions using diffusion kurtosis model-based imaging. ⋯ MR-DKI parameters enable to improve breast lesion characterization and have diagnostic potential applying to different pathological subtypes of breast cancers.
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Comparative Study
Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms.
This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease. ⋯ This study demonstrated that a deep transfer learning is feasible to detect COVID-19 disease automatically from chest X-ray by training the learning model with chest X-ray images mixed with COVID-19 patients, other pneumonia affected patients and people with healthy lungs, which may help doctors more effectively make their clinical decisions. The study also gives an insight to how transfer learning was used to automatically detect the COVID-19 disease. In future studies, as the amount of available dataset increases, different convolution neutral network models could be designed to achieve the goal more efficiently.
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To evaluate the clinical and computed tomographic (CT) features in the patients with COVID-19 pneumonia confirmed by the real-time reverse transcriptase polymerase chain reaction (rRT-PCR) amplification of the viral DNA from a sputum sample. ⋯ There were some typical CT features for diagnosis of COVID-19 pneumonia. The radiologists should know these CT findings and clinical information, which could help for accurate analysis in the patients with 2019 novel coronavirus infection.