Document Type
Article
Publication Date
3-1-2025
Original Citation
Armato S,
Drukker K,
Hadjiiski L,
Wu C,
Kalpathy-Cramer J,
Shih G,
Giger M,
Baughan N,
Bearce B,
Flanders A,
Ball R,
Myers K,
Whitney H,
Midrc Grand Challenge Working Group T.
MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs. J Med Imaging (Bellingham). 2025;12(2):024505.
Keywords
JMG
JAX Source
J Med Imaging (Bellingham). 2025;12(2):024505.
ISSN
2329-4302
PMID
40276098
DOI
https://doi.org/10.1117/1.Jmi.12.2.024505
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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