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Automatic measurement of the patellofemoral joint parameters in the Laurin view: a deep learning-based approach.

Tuya ERile NaiXiang LiuCen WangJing LiuShijia LiJiahao HuangJunhua YuYaofeng ZhangWeipeng LiuXiaodong ZhangXiaoying Wang
Published in: European radiology (2022)
• The U-Net model could be used to segment the landmarks of the PFJ and calculate the SA, CA, PFR, and LPT, which could be used to evaluate the patellar instability. • In the hold-out test, the automatic measurement model yielded comparable performance with reference standard. • The automatic measurement model could still accurately predict SA, CA, PFR, and LPT in patients with PI and/or PFOA.
Keyphrases
  • deep learning
  • machine learning
  • total knee arthroplasty
  • artificial intelligence
  • protein kinase