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Diagnostic performance of artificial intelligence model for pneumonia from chest radiography.

TaeWoo KwonSang Pyo LeeDongmin KimJinseong JangMyungjae LeeShin Uk KangHee Jin KimKeunyoung OhJinhee OnYoung Jae KimSo Jeong YunKwang Nam JinEun Young KimKwang Gi Kim
Published in: PloS one (2021)
The ensemble model combined two different classification models for pneumonia that performed at 0.983 AUC for an external test dataset from a completely different data source. Furthermore, AI probability scores showed significant changes between cases of different clinical prognosis, which suggest the possibility of increased efficiency and performance of the CXR reading at the diagnosis and follow-up evaluation for pneumonia.
Keyphrases
  • artificial intelligence
  • machine learning
  • big data
  • deep learning
  • convolutional neural network
  • respiratory failure
  • community acquired pneumonia
  • computed tomography
  • magnetic resonance imaging
  • intensive care unit