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

ConclusionsThis study proposes a reliable approach with promising applicability for automatic patellar height and trochlear dysplasia assessment, assisting the radiologists in their clinical practice.Clinical relevance statementThe objective knee landmarks detection on MRI images provided by artificial intelligence may improve the reproducibility and reliability of the imaging evaluation of trochlear anatomy and patellar height, assisting radiologists in their clinical practice in the patellofemoral instability assessment.Key Points• 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.
Source: European Radiology - Category: Radiology Source Type: research