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Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.

Tomohiro WatayaMasahiro YanagawaMitsuko TsubamotoTomoharu SatoDaiki NishigakiKosuke KitaKazuki YamagataYuki SuzukiAkinori HataShoji KidoNoriyuki Tomiyamanull null
Published in: European radiology (2022)
• Deep learning-based computer-aided diagnosis improves the accuracy of characterizing nodules/masses and diagnosing malignancy, particularly by radiologists with < 5 years of experience. • Computer-aided diagnosis increases not only the accuracy but also the reproducibility of the findings across radiologists.
Keyphrases
  • deep learning
  • artificial intelligence
  • machine learning
  • pulmonary hypertension
  • convolutional neural network
  • contrast enhanced
  • ultrasound guided
  • contrast enhanced ultrasound