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.

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Rationale Chest radiography (CXR) is a noninvasive imaging approach commonly used to evaluate lower respiratory tract infections (LRTIs) in children. However, the specific imaging patterns of pediatric coronavirus disease 2019 (COVID‐19) on CXR, their relationship to clinical outcomes, and the possible differences from LRTIs caused by other viruses in children remain to be defined. Methods This is a cross‐sectional study of patients seen at a pediatric hospital with polymerase chain reaction (PCR)‐confirmed severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) (n = 95). Patients were subdivided in infants (0–2 years, n = 27), children (3–10 years, n = 27), and adolescents (11–19 years, n = 41). A sample of young children (0–2 years, n = 68) with other viral lower respiratory infections (LRTI) was included to compare their CXR features with the subset of infants (0–2 years) with COVID‐19. Results Forty‐five percent of pediatric patients with COVID‐19 were hospitalized and 20% required admission to intensive care unit (ICU). The most common abnormalities identified were ground‐glass opacifications (GGO)/consolidations (35%) and increased peribronchial markings/cuffing (33%). GGO/consolidations were more common in older individuals and perihilar markings were more common in younger subjects. Subjects requiring hospitalization or ICU admission had significantly more GGO/consolidations in CXR (p < .05). Typical CXR features of pediatric viral LRTI (e.g., hyperinflation) were more common in non‐COVID‐19 viral LRTI cases than in COVID‐19 cases (p < .05). Conclusions CXR may be a complemental exam in the evaluation of moderate or severe pediatric COVID‐19 cases. The severity of GGO/consolidations seen in CXR is predictive of clinically relevant outcomes. Hyperinflation could potentially aid clinical assessment in distinguishing COVID‐19 from other types of viral LRTI in young children.