Evaluation of the Trachea and Bronchi in COVID-19 Patients Using the 3-dimensional Reconstruction Method.

OBJECTIVE: To evaluate changes in the trachea and bronchi of COVID-19 patients using the 3-dimensional reconstruction images obtained from chest CT (computed tomography) scans. STUDY DESIGN: An observational study. Place and Duration of the Study: Departments of Anatomy and Radiology, Faculty of Medicine, Lokman Hekim University, Ankara, Turkey, between March 2021 and January 2022. METHODOLOGY: There were 150 COVID-19 patients in the acute period and 150 individuals as the control group. The CT images were transferred to Mimics software, and a 3-dimensional reconstruction was performed. COVID-19 patients were grouped separately by gender, and their total lung severity score was classified as absent (Grade 0), mild (Grade 1), moderate (Grade 2), and severe (Grade 3). RESULTS: The cross-sectional area and diameter of the right upper lobar bronchus decreased as the grade increased (p<0.05 and p<0.001, respectively). The circumference of the right upper lobar bronchus and the cross-sectional area and circumference of the left lower lobar bronchus were found to be narrower in Grade 1-2-3 COVID-19 patients compared to those of the control group (p<0.01, p<0.05, and p<0.05, respectively). The cross-sectional area, circumference, and diameter of the middle lobar bronchus were found to be narrower in Grade 3 COVID-19 patients (p<0.05, p<0.05, and p<0.05, respectively). CONCLUSION: Although mostly independent of the grade increase, narrowing of the trachea and bronchi was observed in COVID-19 patients in the acute period. Further research is required with to reveal whether the narrowings are permanent. KEY WORDS: COVID-19, Trachea, Bronchus, 3-dimensional reconstruction.

Automatic detection of COVID-19 from chest radiographs using deep learning.

INTRODUCTION: The breakdown of a deadly infectious disease caused by a newly discovered coronavirus (named SARS n-CoV2) back in December 2019 has shown no respite to slow or stop in general. This contagious disease has spread across different lengths and breadths of the globe, taking a death toll to nearly 700 k by the start of August 2020. The number is well expected to rise even more significantly. In the absence of a thoroughly tested and approved vaccine, the onus primarily lies on obliging to standard operating procedures and timely detection and isolation of the infected persons. The detection of SARS n-CoV2 has been one of the core concerns during the fight against this pandemic. To keep up with the scale of the outbreak, testing needs to be scaled at par with it. With the conventional PCR testing, most of the countries have struggled to minimize the gap between the scale of outbreak and scale of testing. METHOD: One way of expediting the scale of testing is to shift to a rigorous computational model driven by deep neural networks, as proposed here in this paper. The proposed model is a non-contact process of determining whether a subject is infected or not and is achieved by using chest radiographs; one of the most widely used imaging technique for clinical diagnosis due to fast imaging and low cost. The dataset used in this work contains 1428 chest radiographs with confirmed COVID-19 positive, common bacterial pneumonia, and healthy cases (no infection). We explored the pre-trained VGG-16 model for classification tasks in this. Transfer learning with fine-tuning was used in this study to train the network on relatively small chest radiographs effectively. RESULTS: Initial experiments showed that the model achieved promising results and can be significantly used to expedite COVID-19 detection. The experimentation showed an accuracy of 96% and 92.5% in two and three output class cases, respectively. CONCLUSION: We believe that this study could be used as an initial screening, which can help healthcare professionals to treat the COVID patients by timely detecting better and screening the presence of disease. IMPLICATION FOR PRACTICE: Its simplicity drives the proposed deep neural network model, the capability to work on small image dataset, the non-contact method with acceptable accuracy is a potential alternative for rapid COVID-19 testing that can be adapted by the medical fraternity considering the criticality of the time along with the magnitudes of the outbreak.

Tracheal swab from front of neck airway for SARS-CoV-2; a bronchial foreign body.

We report the case of a bronchial foreign body, following a tracheostomy site swab for SARS-CoV-2, aiming to raise awareness and vigilance. A qualified nurse was performing a routine SARS-CoV-2 swab on a 51-year-old woman, fitted with a tracheostomy in the recent past following a craniotomy. This was part of the discharging protocol to a nursing home. During the sampling, part of the swab stylet snapped and was inadvertently dropped through the tracheostomy site. Initial CT imaging was reported as showing no signs of a foreign body but some inflammatory changes. Bedside flexible endoscopy through the tracheostomy site revealed the swab in a right lobar bronchus. This was subsequently removed by flexible bronchoscopy. This case highlights the need for clear guidance on how samples for SARS-CoV-2 are taken from patients with front of neck airways (laryngectomy/tracheοstomy) and the potential pitfalls involved.

COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings.

OBJECTIVES: CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients. METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases. RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier. CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis. KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.

The pulmonary sequalae in discharged patients with COVID-19: a short-term observational study.

BACKGROUND: A cluster of patients with coronavirus disease 2019 (COVID-19) pneumonia were discharged from hospitals in Wuhan, China. We aimed to determine the cumulative percentage of complete radiological resolution at each time point, to explore the relevant affecting factors, and to describe the chest CT findings at different time points after hospital discharge. METHODS: Patients with COVID-19 pneumonia confirmed by RT-PCR who were discharged consecutively from the hospital between 5 February 2020 and 10 March 2020 and who underwent serial chest CT scans on schedule were enrolled. The radiological characteristics of all patients were collected and analysed. The total CT score was the sum of non-GGO involvement determined at discharge. Afterwards, all patients underwent chest CT scans during the 1st, 2nd, and 3rd weeks after discharge. Imaging features and distributions were analysed across different time points. RESULTS: A total of 149 patients who completed all CT scans were evaluated; there were 67 (45.0%) men and 82 (55.0%) women, with a median age of 43 years old (IQR 36-56). The cumulative percentage of complete radiological resolution was 8.1% (12 patients), 41.6% (62), 50.3% (75), and 53.0% (79) at discharge and during the 1st, 2nd, and 3rd weeks after discharge, respectively. Patients ≤44 years old showed a significantly higher cumulative percentage of complete radiological resolution than patients > 44 years old at the 3-week follow-up. The predominant patterns of abnormalities observed at discharge were ground-glass opacity (GGO) (125 [83.9%]), fibrous stripe (81 [54.4%]), and thickening of the adjacent pleura (33 [22.1%]). The positive count of GGO, fibrous stripe and thickening of the adjacent pleura gradually decreased, while GGO and fibrous stripe showed obvious resolution during the first week and the third week after discharge, respectively. "Tinted" sign and bronchovascular bundle distortion as two special features were discovered during the evolution. CONCLUSION: Lung lesions in COVID-19 pneumonia patients can be absorbed completely during short-term follow-up with no sequelae. Two weeks after discharge might be the optimal time point for early radiological estimation.

Novel Coronavirus Pneumonia Outbreak in 2019: Computed Tomographic Findings in Two Cases.

Since the 2019 novel coronavirus (2019-nCoV or officially named by the World Health Organization as COVID-19) outbreak in Wuhan, Hubei Province, China in 2019, there have been a few reports of its imaging findings. Here, we report two confirmed cases of 2019-nCoV pneumonia with chest computed tomography findings of multiple regions of patchy consolidation and ground-glass opacities in both lungs. These findings were characteristically located along the bronchial bundle or subpleural lungs.