Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height.

Journal: European radiology

Published Date: Feb 10, 2024

Abstract

OBJECTIVES: To develop and validate a deep learning-based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans.

Authors

Roberto M Barbosa

Center of MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, Portugal. roberto.mbarbosa@dei.uminho.pt.
Luís Serrador

Center of MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, Portugal.
Manuel Vieira da Silva

Department of Orthopaedics, Trofa Saúde Braga Centro Hospital, Braga, Portugal.
Carlos Sampaio Macedo

Department of Radiology, Trofa Saúde Braga Centro Hospital, Braga, Portugal.
Cristina P Santos

Keywords

Adolescent Adult Anatomic Landmarks Deep Learning Female Humans Joint Instability Knee Joint Magnetic Resonance Imaging Male Middle Aged Patella Patellofemoral Joint Reproducibility of Results Young Adult

External Resources

View on PubMed Access via DOI PubMed (38337072)

Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height.

Abstract

Authors

Keywords

External Resources

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