Rationale Pediatric COVID‐19 studies have been mostly restricted to case reports and small case series, which have prevented the identification of specific pediatric lung disease patterns in COVID‐19. The overarching goal of this systematic review and meta‐analysis is to provide the first comprehensive summary of the findings of published studies thus far describing COVID‐19 lung imaging data in the pediatric population. Methods A systematic literature search of PubMed was performed to identify studies assessing lung‐imaging features of COVID‐19 pediatric patients (0–18 years). A single‐arm meta‐analysis was conducted to obtain the pooled prevalence and 95% confidence interval (95% CI). Results A total of 29 articles (n = 1026 children) based on chest computerized tomography (CT) images were included. The main results of this comprehensive analysis are as follows: (1) Over a third of pediatric patients with COVID‐19 (35.7%, 95% CI: 27.5%–44%) had normal chest CT scans and only 27.7% (95% CI: 19.9%–35.6%) had bilateral lesions. (2) The most typical pediatric chest CT findings of COVID‐19 were ground‐glass opacities (GGO) (37.2%, 95% CI: 29.3%–45%) and the presence of consolidations or pneumonic infiltrates (22.3%, 95% CI: 17.8%–26.9%). (3) The lung imaging findings in children with COVID‐19 were overall less frequent and less severe than in adult patients. (4) Typical lung imaging features of viral respiratory infections in the pediatric population such as increased perihilar markings and hyperinflation were not reported in children with COVID‐19. Conclusion Chest CT manifestations in children with COVID‐19 could potentially be used for early identification and prompt intervention in the pediatric population.

A growing number of magnetic resonance (MR) imaging studies of the shoulder are being performed as a result of greater and earlier participation of children and adolescents in competitive sports such as softball and baseball. However, scant information is available regarding the MR imaging features of the normal sequential development of the shoulder. The authors discuss the radiographic and MR imaging appearances of the normal musculoskeletal maturation patterns of the shoulder, with emphasis on (a) development of secondary ossification centers of the glenoid (including the subcoracoid and peripheral glenoid ossification centers); (b) development of preossification and secondary ossification centers of the humeral head and the variable appearance and number of the secondary ossification centers of the distal acromion, with emphasis on the formation of the os acromiale; (c) development of the growth plates, glenoid bone plates, glenoid bare area, and proximal humeral metaphyseal stripe; and (d) marrow signal alterations in the distal humerus, acromion, and clavicle. In addition, the authors discuss various imaging interpretation pitfalls inherent to the normal skeletal maturation of the shoulder, examining clues that may help distinguish normal development from true disease (eg, osteochondral lesions, labral tears, abscesses, fractures, infection, tendon disease, acromioclavicular widening, and os acromiale). Familiarity with the timing, location, and appearance of maturation patterns in the pediatric shoulder is crucial for correct image interpretation.

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Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.