The rapid spread of COVID-19 increased the need for speedy diagnostic tools, which led scientists to conduct extensive research on deep learning (DL) applications that use chest imaging, such as chest X-ray (CXR) and computed tomography (CT). This review examines the development and performance of DL architectures, notably convolutional neural networks (CNNs) and emerging vision transformers (ViTs), in identifying COVID-19-related lung abnormalities. Individual ResNet architectures, along with CNN models, demonstrate strong diagnostic performance through the transfer protocol; however, ViTs provide better performance, with improved readability and reduced data requirements. Multimodal diagnostic systems now incorporate alternative methods, in addition to imaging, which use lung ultrasounds, clinical data, and cough sound evaluation. Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. However, progress in COVID-19 detection is hindered by ongoing issues stemming from restricted and non-uniform datasets, as well as domain differences in image standards and complications with both diagnostic overfitting and poor generalization capabilities. Recent developments in COVID-19 diagnosis involve constructing expansive multi-noise information sets while creating clinical process-oriented AI algorithms and implementing distributed learning protocols for securing information security and system stability. While deep learning-based COVID-19 detection systems show strong potential for clinical application, broader validation, regulatory approvals, and continuous adaptation remain essential for their successful deployment and for preparing future pandemic response strategies.

Acute appendicitis (AA) is one of the most common surgical emergencies in children. Some reports have suggested that the COVID-19 pandemic was responsible for delays in the diagnostic and proper treatment of AA in pediatric patients. The aim of our study was to perform a retrospective study of cases of AA in children with SARS-CoV-2 infection treated in a highly endemic area for COVID-19 in Romania during a 2-year time interval. The SARS-CoV-2 infection had no unfavorable impact on children who presented with AA. Further data analysis should clarify the overall influence of COVID-19 on the management of surgical pediatric patients in such endemic areas.

Spontaneous pneumomediastinum (SPM) associated with SARS-CoV-2 infection is a rare condition but can represent a medical emergency. It is probably related to alveolar damage secondary to SARS-CoV-2 infection, which allows air to escape in the surrounding lung tissue. Cough and airways’ barotrauma are also mentioned as contributing mechanisms. Treatment is generally conservative, but surgery may be required in severe cases. This paper presents the case of a 16-year-old girl with COVID-19-associated SPM who was treated conservatively in our department. The clinical course was favorable with resolution of respiratory symptoms and radiological (chest CT scan) image of pneumomediastinum. The patient was discharged 7 days after the confirmation of the initial SP diagnosis with appropriate treatment and recommendations for isolation. The sudden occurrence of chest pain and dyspnea should raise the suspicion of SPM in COVID-19 patients. Close surveillance and proper radiological monitoring are required in such cases. Treatment should be strictly individualized based on clinical course and radiological appearance.