The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.

We describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including "typical," "indeterminate," and "atypical appearance" for COVID-19, or "negative for pneumonia," adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use.

Performance of a Severity Score on Admission Chest Radiography in Predicting Clinical Outcomes in Hospitalized Patients With Coronavirus Disease (COVID-19).

BACKGROUND. Chest radiographs (CXRs) are typically obtained early in patients admitted with coronavirus disease (COVID-19) and may help guide prognosis and initial management decisions. OBJECTIVE. The purpose of this study was to assess the performance of an admission CXR severity scoring system in predicting hospital outcomes in patients admitted with COVID-19. METHODS. This retrospective study included 240 patients (142 men, 98 women; median age, 65 [range, 50-80] years) admitted to the hospital from March 16 to April 13, 2020, with COVID-19 confirmed by real-time reverse-transcriptase polymerase chain reaction who underwent chest radiography within 24 hours of admission. Three attending chest radiologists and three radiology residents independently scored patients' admission CXRs using a 0- to 24-point composite scale (sum of scores that range from 0 to 3 for extent and severity of disease in upper and lower zones of left and right lungs). Interrater reliability of the score was assessed using the Kendall W coefficient. The mean score was obtained from the six readers' scores for further analyses. Demographic variables, clinical characteristics, and admission laboratory values were collected from electronic medical records. ROC analysis was performed to assess the association between CXR severity and mortality. Additional univariable and multivariable logistic regression models incorporating patient characteristics and laboratory values were tested for associations between CXR severity and clinical outcomes. RESULTS. Interrater reliability of CXR scores ranged from 0.687 to 0.737 for attending radiologists, from 0.653 to 0.762 for residents, and from 0.575 to 0.666 for all readers. A composite CXR score of 10 or higher on admission achieved 53.0% (35/66) sensitivity and 75.3% (131/174) specificity for predicting hospital mortality. Hospital mortality occurred in 44.9% (35/78) of patients with a high-risk admission CXR score (≥ 10) versus 19.1% (31/162) of patients with a low-risk CXR score (< 10) (p < .001). Admission composite CXR score was an independent predictor of death (odds ratio [OR], 1.17; 95% CI, 1.10-1.24; p < .001). composite CXR score was a univariable predictor of intubation (OR, 1.23; 95% CI, 1.12-1.34; p < .001) and continuous renal replacement therapy (CRRT) (OR, 1.15; 95% CI, 1.04-1.27; p = .007) but was not associated with these in multivariable models (p > .05). CONCLUSION. For patients admitted with COVID-19, an admission CXR severity score may help predict hospital mortality, intubation, and CRRT. CLINICAL IMPACT. CXR may assist risk assessment and clinical decision-making early in the course of COVID-19.

Interstitial lung abnormalities and pulmonary fibrosis in COVID-19 patients: a short-term follow-up case series.

Fibrotic lung changes are well-known complications of SARS, MERS, and ARDS from other causes and are anticipated in recovered COVID patients. However, there is limited data so far showing a temporal relationship between lung changes on imaging in the acute phase and follow-up imaging after recovery from the infection. We present 12 patients who demonstrate the development of interstitial lung changes and pulmonary fibrosis in the same distribution and pattern as the acute phase findings, up to 6 months after the acute infection, demonstrating a direct relationship between these changes and COVID-19 pneumonia.