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Introduction: Chronic health effects following acute COVID-19 are increasingly observed as the pandemic continues and are grouped under long COVID. Although the acute course of the COVID disease is often milder, long COVID also affects children and adolescents. As the symptoms present in Long-COVID often seem to be non-specific and not limited to organ systems, clarification of the causes and the creation of a meaningful, efficient and targeted diagnostic algorithm is urgently needed. Methods: Therefore, in this prospective observational study, we examined 30 children with long COVID using lung ultrasound and compared the results with those of 15 lung-healthy children. Results: In our study, no significant difference was found between the two groups in the morphological criteria of lung ultrasound of the pleura or pleural lung structures. There was no significant correlation between the lung ultrasound findings and clinical Data. Discussion: Our findings are congruent with the current, albeit sparse, data. It is possible that the causes of persistent thoracic symptoms in long COVID might be more likely to be present in functional examinations, but not morphologically imageable. Nonspecific symptoms do not appear to be due to changes in the lung parenchyma. In conclusion, lung ultrasound alone and without baseline in acute disease is not suitable as a standard in the follow-up of long COVID patients. Further investigations on the morphological and functional changes in patient with long COVID is needed.
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BACKGROUND: In hospitals, it is crucial to rule out coronavirus disease 2019 (COVID-19) timely and reliably. Artificial intelligence (AI) provides sufficient accuracy to identify chest computed tomography (CT) scans with signs of COVID-19. PURPOSE: To compare the diagnostic accuracy of radiologists with different levels of experience with and without assistance of AI in CT evaluation for COVID-19 pneumonia and to develop an optimized diagnostic pathway. MATERIAL AND METHODS: The retrospective, single-center, comparative case-control study included 160 consecutive participants who had undergone chest CT scan between March 2020 and May 2021 without or with confirmed diagnosis of COVID-19 pneumonia in a ratio of 1:3. Index tests were chest CT evaluation by five radiological senior residents, five junior residents, and an AI software. Based on the diagnostic accuracy in every group and on comparison of groups, a sequential CT assessment pathway was developed. RESULTS: Areas under receiver operating curves were 0.95 (95% confidence interval [CI]=0.88-0.99), 0.96 (95% CI=0.92-1.0), 0.77 (95% CI=0.68-0.86), and 0.95 (95% CI=0.9-1.0) for junior residents, senior residents, AI, and sequential CT assessment, respectively. Proportions of false negatives were 9%, 3%, 17%, and 2%, respectively. With the developed diagnostic pathway, junior residents evaluated all CT scans with the support of AI. Senior residents were only required as second readers in 26% (41/160) of the CT scans. CONCLUSION: AI can support junior residents with chest CT evaluation for COVID-19 and reduce the workload of senior residents. A review of selected CT scans by senior residents is mandatory.