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Cochrane COVID-19 Study Register
Study record
Lakhani 2022bFirst Published: 2022 Nov 23Updated Date: 2022 Nov 23

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

  1. Study Type
  2. Modelling
  1. Study Aim
  2. Diagnostic/Prognostic
  1. Study Design
  2. Other
  1. Intervention Assignment
  2. Not Applicable
Reference record

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

Lakhani P, Mongan J, Singhal C, Zhou Q, Andriole KP, Auffermann WF, Prasanna PM, Pham TX, Peterson M, Bergquist PJ, Cook TS, Ferraciolli SF, Corradi GCA, Takahashi MS, Workman CS, Parekh M, Kamel SI, Galant J, Mas-Sanchez A, Benitez EC, Sanchez-Valverde M, Jaques L, Panadero M, Vidal M, Culianez-Casas M, Angulo-Gonzalez D, Langer SG, de la Iglesia-Vaya M, Shih G
Journal article
Report Results
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