Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a wellequipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups:

Carpal tunnel syndrome (CTS) is the most commonly diagnosed disabling condition of the upper extremities. It is the most commonly known and prevalent type of peripheral entrapment neuropathy that accounts for about 90% of all entrapment neuropathies. This review aims to provide an outline of CTS by considering anatomy, pathophysiology, clinical manifestation, diagnostic modalities and management of this common condition, with an emphasis on the diagnostic imaging evaluation.

Elective coiling of unruptured intracranial aneurysms is associated with fewer deaths and perioperative complications compared with elective clipping. The trend of hospital use of the coiling procedures has increased during recent years.

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