====== Chest computed tomography ======

Chest [[computed tomography]] (CT) has been the preferred [[imaging]] modality during the [[pandemic]] owing to its [[sensitivity]] in detecting [[COVID-19]] [[infection]]s. 

A large number of COVID-19 imaging [[dataset]]s have been deposited in [[public]] [[database]]s, leading to rapid [[advance]]s in [[COVID-19]] [[research]]. However, the application of these datasets beyond COVID-19-related research has been little explored. Yurasakpong et al. believed they could be used in anatomical [[research]] to elucidate the link between [[anatomy]] and [[disease]] and to study disease-related alterations to normal anatomy. Therefore, they designed a study to investigate the [[prevalence]] of six well-known anatomical variants in the [[thorax]] using open-access CT images obtained from over 1000 [[Iran]]ian COVID-19 patients aged between 6 and 89 years (60.9% male and 39.1% female). In brief, they found that the azygos lobe, tracheal bronchus, and cardiac bronchus were present in 0.8%, 0.2%, and 0% of the patients, respectively. Variations of the [[sternum]], including sternal foramen, episternal ossicles, and sternalis muscle, were observed in 9.6%, 2.9%, and 1.5%, respectively. They believed anatomists could benefit from using open-access [[dataset]]s as raw materials for research because these datasets are freely accessible and are abundant, though further research is needed to evaluate the uses of other datasets from different body regions and imaging modalities. [[Radiologist]]s should also be aware of these common anatomical variants when examining [[lung]] CTs, especially since the use of this imaging modality has increased during the pandemic
((Yurasakpong L, Asuvapongpatana S, Weerachatyanukul W, Meemon K, Jongkamonwiwat N, Kruepunga N, Chaiyamoon A, Sudsang T, Iwanaga J, Tubbs RS, Suwannakhan A. Anatomical variants identified on [[chest computed tomography]] of 1000+ [[COVID-19]] patients from an open-access dataset. Clin Anat. 2022 Apr 6. doi: 10.1002/ca.23873. Epub ahead of print. PMID: 35385153.)).