Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning neural networks have a great potential for building COVID-19 triage systems and detecting COVID-19 patients, especially patients with low severity. Unfortunately, current databases do not allow building such systems as they are highly heterogeneous and biased towards severe cases. This article is threefold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Clínico San Cecilio, Granada, Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includes all levels Manuscript

Bochdalek hernias (BHs) are produced in the posterolateral area of the diaphragm. They are generally congenital, appearing in childhood, but are also detected in asymptomatic adult patients seeking medical attention for other reasons. Computed tomography (CT) or magnetic resonance imaging (MRI) is used for the correct diagnosis of the hernia type and for its localization, facilitating its management and the choice of treatment. We describe three cases of Bochdalek hernia, two on the right side and one bilateral, which was larger on the right than left side. All of these hernias contained only omental fat. In one patient, the right kidney was adjacent to the diaphragmatic defect but remained within the abdomen. The patients showed no symptoms and were not surgically treated. Examination by multislice CT with the possibility of coronal and sagittal reconstruction should be considered the standard method for diagnosing this entity. MRI in T1 is highly valuable to evaluate fat-containing chest lesions. The incidental finding of BH in asymptomatic adults is increasing, thanks to the wider application of new imaging techniques.

Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning neural networks have a great potential for building triage systems for detecting COVID-19 patients, especially patients with low severity. Unfortunately, current databases do not allow building such systems as they are highly heterogeneous and biased towards severe cases. This paper is three-fold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Clnico San Cecilio, Granada, Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includes all levels of severity, from Normal with positive RT-PCR, Mild, Moderate to Severe. COVIDGR-1.0 contains 377 positive and 377 negative PA (PosteroAnterior) CXR views and (iii) we propose COVID Smart Data based Network (COVID-SDNet) methodology for improving the generalization capacity of COVID-classification models. Our approach reaches good and stable results with an accuracy of 97.37% ± 1.86%, 88.14%±2.02%, 66.5%±8.04% in severe, moderate and mild COVID severity levels. Our approach could help in the early detection of COVID-19. COVIDGR-1.0 dataset will be made available after the review process.

La perforación esofágica es la más letal de todas las perforaciones del aparato digestivo. Se presenta el caso de un varón de 65 años que acude a urgencias por un cuadro clínico de dolor torácico, vómitos e hipotensión. Se le realizó tomografía computarizada por sospecha de síndrome aórtico agudo, con hallazgos sugerentes de perforación esofágica. El síndrome de Boerhaave consiste en la rotura longitudinal del esófago sobre una pared macroscópicamente sana. Su tratamiento definitivo se realiza con cirugía durante las primeras 24 horas. El síndrome de Boerhaave debe considerarse como complicación posible en los pacientes con dolor epigástrico y vómitos, ya que es una emergencia quirúrgica con alta morbimortalidad.