Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height.
Roberto M BarbosaLuís SerradorManuel Vieira da SilvaCarlos Sampaio MacedoCristina Peixoto SantosPublished in: European radiology (2024)
• Imaging evaluation of patellofemoral instability is subjective and vulnerable to substantial intra and interobserver variability. • Patellar height and trochlear dysplasia are reliably assessed in MRI by means of artificial intelligence (AI). • The developed AI framework provides an objective evaluation of patellar height and trochlear dysplasia enhancing the clinical practice of the radiologists.
Keyphrases
- artificial intelligence
- deep learning
- total knee arthroplasty
- machine learning
- body mass index
- big data
- anterior cruciate ligament reconstruction
- anterior cruciate ligament
- high resolution
- clinical practice
- convolutional neural network
- magnetic resonance imaging
- depressive symptoms
- loop mediated isothermal amplification
- real time pcr