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Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence.

Eleonora CrociHanspeter HessFabian WarmuthMarina KünzlerSean BörlinDaniel BaumgartnerAndreas Marc MüllerKate GerberAnnegret Mündermann
Published in: European radiology (2023)
• Anatomical configuration and glenohumeral joint stability are often a concern after rotator cuff tears. • Artificial intelligence applied to fluoroscopic images helps to identify and track anatomical landmarks during dynamic movements. • The developed automatic landmark detection algorithm optimised the labelling procedures and is suitable for clinical application.
Keyphrases
  • artificial intelligence
  • deep learning
  • rotator cuff
  • machine learning
  • convolutional neural network
  • big data
  • loop mediated isothermal amplification
  • real time pcr
  • catheter ablation