Publications

Accélérer l'adoption grâce aux preuves scientifiques et cliniques

BoneView
  • Daichi Hayashi,
  • Automated detection of acute appendicular skeletal fractures in pediatric patients using deep learning
BoneView
  • Urgences 2022 (congrès de la SFMU)

Amélioration de la détection des fractures sur des radiographies traumatiques et réduction du temps de lecture grâce à l’intelligence artificielle

ChestView
  • Bennani et. al,
  • ESTI 2022

Evaluation of radiologists’ performance compared to a deep learning algorithm for the detection of thoracic abnormalities on chest X-ray

  • Bennani et. al,
  • ECR 2022

Evaluation of radiologists’ performance compared to a deep learning algorithm for the detection of thoracic abnormalities on chest X-ray

BoneView
  • Maarek et. al,
  • ECR 2022

Assessment of an AI aid in detection of pediatric appendicular skeletal fractures by senior and junior radiologists.

BoneView
  • ECR 2022

Artificial Intelligence (AI) support for pelvic fracture detection on plain radiographs: a preliminary study of AI integration in the clinical workflow

BoneView
  • Ali Guermazi,
  • Radiology

Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence

BoneView
  • Hayashi et. al,
  • RSNA

Improving Radiographic Fracture Detection and Reducing Reading Time Using Artificial Intelligence: A Multi-Center Study with Radiologists and Non-Radiologists in The United States

  • Regnard et. al,
  • RSNA

Evaluation of the medical impact of artificial intelligence for limb and pelvic bone fracture detection

  • Regnard et. al,
  • EuSoMII 2021

Performances of a deep learning algorithm for the detection of fracture, dislocation, elbow joint effusion, focal bone lesions on trauma X-rays

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