Machine learning evaluation of pneumonia severity: subgroup performance in the Medical Imaging and Data Resource Center modified radiographic assessment of lung edema mastermind challenge

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Abstract
As COVID-19 transitions to an endemic phase, assessing lung involvement in affected patients remains vital for guiding treatment decisions and managing healthcare resources. Machine learning (ML) algorithms have proven invaluable for tasks such as image segmentation, registration, and computer-aided diagnosis, enabling the identification of patterns in complex medical imaging data that are not easily discernible through human analysis.1 These capabilities enhance the accuracy and efficiency of radiologists and other medical professionals.2
Funding Information
  • National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (75N92020D00021)
  • Advanced Research Projects Agency for Health (5N92023F00002)

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