Patellar tilt calculation utilizing artificial intelligence on CT knee imaging.

Journal: The Knee
Published Date: Mar 13, 2025

Abstract

BACKGROUND: In the diagnosis of patellar instability, three-dimensional (3D) imaging enables measurement of a wide range of metrics. However, measuring these metrics can be time-consuming and prone to error due to conducting 2D measurements on 3D objects. This study aims to measure patellar tilt in 3D and automate it by utilizing a commercial AI algorithm for landmark placement.

Authors

  • Johannes Sieberer
    Yale School of Medicine - Orthopaedics & Rehabilitation, 47 College Street, New Haven, CT, USA; Yale School of Engineering and Applied Science - Department of Mechanical Engineering and Material Science, 17 Hillhouse, New Haven, CT, USA. Electronic address: johannes.sieberer@yale.edu.
  • Albert Rancu
    Yale School of Medicine - Orthopaedics & Rehabilitation, 47 College Street, New Haven, CT, USA. Electronic address: albert.rancu@yale.edu.
  • Nancy Park
    Yale School of Medicine - Orthopaedics & Rehabilitation, 47 College Street, New Haven, CT, USA. Electronic address: nancy.park@yale.edu.
  • Shelby Desroches
    Yale School of Medicine - Orthopaedics & Rehabilitation, 47 College Street, New Haven, CT, USA. Electronic address: shelby.desroches@yale.edu.
  • Armita R Manafzadeh
    Yale School of Medicine - Orthopaedics & Rehabilitation, 47 College Street, New Haven, CT, USA. Electronic address: armita.manafzadeh@yale.edu.
  • Steven Tommasini
    Yale School of Medicine - Orthopaedics & Rehabilitation, 47 College Street, New Haven, CT, USA. Electronic address: steven.tommasini@yale.edu.
  • Daniel H Wiznia
  • John Fulkerson
    Yale School of Medicine - Orthopaedics & Rehabilitation, 47 College Street, New Haven, CT, USA. Electronic address: john.fulkerson@yale.edu.

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