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

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. METHODS: A total of 763 knee MRI slices from 95 patients were included in the study, and 3393 anatomical landmarks were annotated for measuring sulcus angle (SA), trochlear facet asymmetry (TFA), trochlear groove depth (TGD) and lateral trochlear inclination (LTI) to assess trochlear dysplasia, and Insall-Salvati index (ISI), modified Insall-Salvati index (MISI), Caton Deschamps index (CDI) and patellotrochlear index (PTI) to assess patellar height. A U-Net based network was implemented to predict the landmarks' locations. The successful detection rate (SDR) and the mean absolute error (MAE) evaluation metrics were used to evaluate the performance of the network. The intraclass correlation coefficient (ICC) was also used to evaluate the reliability of the proposed framework to measure the mentioned PFI indices. RESULTS: The developed models achieved good accuracy in predicting the landmarks' locations, with a maximum value for the MAE of 1.38 ± 0.76 mm. The results show that LTI, TGD, ISI, CDI and PTI can be measured with excellent reliability (ICC > 0.9), and SA, TFA and MISI can be measured with good reliability (ICC > 0.75), with the proposed framework. CONCLUSIONS: This 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 STATEMENT: The 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.

Imaging evaluation of patellofemoral joint instability: a review.

The multifactorial origin of anterior knee pain in patellofemoral joint disorders leads to a demanding diagnostic process. Patellofemoral misalignment is pointed out as one of the main causes of anterior knee pain. The main anatomical risk factors of patellofemoral instability addressed in the literature are trochlear dysplasia, abnormal patellar height, and excessive tibial tubercle-trochlear groove distance. Diagnostic imaging of the patellofemoral joint has a fundamental role in assessing these predisposing factors of instability. Extensive work is found in the literature regarding the assessment of patellofemoral instability, encompassing several metrics to quantify its severity. Nevertheless, this process is not well established and standardized, resulting in some variability and inconsistencies. The significant amount of scattered information regarding the patellofemoral indices to assess the instability has led to this issue. This review was conducted to collect all this information and describe the main insights of each patellofemoral index presented in the literature. Five distinct categories were created to organize the patellofemoral instability indices: trochlear dysplasia, patellar height, patellar lateralization, patellar tilt, and tibial tubercle lateralization.