ArticleJournal of medical imaging (Bellingham, Wash.)2025
MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs.
Article in Journal of medical imaging (Bellingham, Wash.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Machine learning evaluation of pneumonia severity: subgroup performance in the Medical Imaging and Data Resource Center modified radiographic assessment of lung edema mastermind challenge.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation.New microbes and new infections · 2024Article
- COVID-19 PNEUMONIA CHEST X-RAY PATTERN SYNTHESIS BY STABLE DIFFUSION.Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation · 2024Article
- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.Medical image learning with limited and noisy data : second international workshop, MILLanD 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MILLanD (Workshop) : (2nd : 2023 : Vancouver, B... · 2023Article
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Abstract
Purpose: The Medical Imaging and Data Resource Center (MIDRC) mRALE Mastermind Grand Challenge fostered the development of artificial intelligence (AI) techniques for the automated assignment of mRALE (modified radiographic assessment of lung edema) scores to portable chest radiographs from patients known to have COVID-19. Approach: The challenge utilized 2079 training cases obtained from the publicly available MIDRC data commons, with validation and test cases sampled from not-yet-public MIDRC cases that were inaccessible to challenge participants. The reference standard mRALE scores for the challenge cases were established by a pool of 22 radiologist annotators. Using the MedICI challenge platform, participants submitted their trained algorithms encapsulated in Docker containers. Algorithms were evaluated by the challenge organizers on 814 test cases through two performance assessment metrics: quadratic-weighted kappa and prediction probability concordance. Results: Nine AI algorithms were submitted to the challenge for assessment against the test set cases. The algorithm that demonstrated the highest agreement with the reference standard had a quadratic-weighted kappa of 0.885 and a prediction probability concordance of 0.875. Substantial variability in mRALE scores assigned by the annotators and output by the AI algorithms was observed. Conclusions: The MIDRC mRALE Mastermind Grand Challenge revealed the potential of AI to assess COVID-19 severity from portable CXRs, demonstrating promising performance against the reference standard. The observed variability in mRALE scores highlights the challenges in standardizing severity assessment. These findings contribute to ongoing efforts to develop AI technologies for potential use in clinical practice and offer insights for the enhancement of COVID-19 severity assessment.
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