ABSTRACT
Pneumomediastinum is a rare, life-threatening condition in which air leaks into the mediastinum. Usually, it results from a traumatic event that leads to the escape of air from the airway, lungs, or bowel into the chest cavity. Patients with underlying lung pathology or a history of invasive mechanical ventilation have an increased risk of developing a pneumomediastinum. A spontaneous pneumomediastinum (SPM) occurs in the absence of these risk factors. Patients with coronavirus disease 2019 (COVID-19) pneumonia tend to have a higher risk of developing an SPM, however, this is usually linked to mechanical ventilator use. Although rare, cases of healthy young patients with no history of underlying lung pathology or mechanical ventilator use developing an SPM are increasingly being reported. In efforts to bring more attention to this complication, we present the case of an SPM in a 40-year-old female patient with COVID-19 pneumonia and highlight the importance of close follow-up.
ABSTRACT
The front-line imaging modalities computed tomography (CT) and X-ray play important roles for triaging COVID patients. Thoracic CT has been accepted to have higher sensitivity than a chest X-ray for COVID diagnosis. Considering the limited access to resources (both hardware and trained personnel) and issues related to decontamination, CT may not be ideal for triaging suspected subjects. Artificial intelligence (AI) assisted X-ray based application for triaging and monitoring require experienced radiologists to identify COVID patients in a timely manner with the additional ability to delineate and quantify the disease region is seen as a promising solution for widespread clinical use. Our proposed solution differs from existing solutions presented by industry and academic communities. We demonstrate a functional AI model to triage by classifying and segmenting a single chest X-ray image, while the AI model is trained using both X-ray and CT data. We report on how such a multi-modal training process improves the solution compared to single modality (X-ray only) training. The multi-modal solution increases the AUC (area under the receiver operating characteristic curve) from 0.89 to 0.93 for a binary classification between COVID-19 and non-COVID-19 cases. It also positively impacts the Dice coefficient (0.59 to 0.62) for localizing the COVID-19 pathology. To compare the performance of experienced readers to the AI model, a reader study is also conducted. The AI model showed good consistency with respect to radiologists. The DICE score between two radiologists on the COVID group was 0.53 while the AI had a DICE value of 0.52 and 0.55 when compared to the segmentation done by the two radiologists separately. From a classification perspective, the AUCs of two readers was 0.87 and 0.81 while the AUC of the AI is 0.93 based on the reader study dataset. We also conducted a generalization study by comparing our method to the-state-art methods on independent datasets. The results show better performance from the proposed method. Leveraging multi-modal information for the development benefits the single-modal inferencing.
ABSTRACT
Thirty-two Head and Neck cancer patients were operated by surgical team of the Indian Institute of Head and Neck Oncology (IIHNO) in a period ranging from May 2020 to the first week of December 2020. Surgical procedures ranged from surgery for tongue cancer, resection of cancers of the oral mucosa/cheek (with or without reconstruction), as well as surgery for paranasal cancers and thyroid cancers, with an average duration of 3 h for the procedures. This article reviews this experience during the peak of covid pandemic regarding the approaches adopted by the team of the IIHNO, a flagship project of the Indore Cancer Foundation, a public charitable trust.