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Exploring the Utility of Cardiovascular Magnetic Resonance Radiomic Feature Extraction for Evaluation of Cardiac Sarcoidosis.

Nouf A MushariGeorgios SoultanidisLisa DuffMaria Giovanna TrivieriZahi A FayadPhilip M RobsonCharalampos Tsoumpas
Published in: Diagnostics (Basel, Switzerland) (2023)
patients. With signature A, support vector machine and k-neighbors showed good performance with AUC (0.77 and 0.73) and accuracy (0.67 and 0.72), respectively. With signature B, decision tree demonstrated AUC and accuracy around 0.7; Conclusion: CMR radiomic analysis in CS provides promising results to distinguish patients with active and inactive disease.
Keyphrases
  • magnetic resonance
  • deep learning
  • machine learning
  • left ventricular
  • decision making
  • computed tomography
  • magnetic resonance imaging
  • atrial fibrillation