Medialization of trochlear groove was correlated with extended lateral trochlear in trochlear dysplasia: a transverse CT analysis

Application of a machine learning and optimization method to predict patellofemoral instability risk factors in children and adolescents

PURPOSE

Conservative treatment remains the standard approach for first-time patellar dislocations. While risk factors for patellofemoral instability, a common paediatric injury, are well-established in adults, data concerning the progression of paediatric recurrent patellar dislocation remain scarce. A reproducible method was developed to quantitatively assess the patellofemoral morphology and anatomic risk factors in paediatric patients using magnetic resonance imaging (MRI) and machine learning analysis.

METHODS

Data were analyzed from a retrospective review (2005-2022) of paediatric patients diagnosed with acute lateral patellar dislocation (54 patients) who underwent MRI and were compared with an age-based control group (54 patients). Patellofemoral, tibial, tibiofemoral and patellar height parameters were measured. Differences between groups were analyzed with respect to MRI parameters. The potential diagnostic utility of the parameters was assessed via machine learning and genetic algorithm analyses.

RESULTS

Significant differences were observed between the two groups in six patellofemoral morphological parameters. Regarding patellar height morphological parameters, all methods exhibited significant between-group differences. Among the tibia and tibiofemoral morphological parameters, only the tibial tubercle-trochlear groove distance exhibited significant differences between the two groups. No sex-related differences were present. Significant variations were observed in patellar height parameters, particularly in the Koshino-Sugimoto (KS) index, which had the highest area under the curve (AUC: 0.87). Using genetic algorithms and logistic regression, our model excelled with seven key independent variables.

CONCLUSION

KS index and Wiberg index had the strongest association with lateral patellar dislocation. An optimized logistic regression model achieved an AUC of 0.934. Such performance is considered clinically relevant, indicating the model's effectiveness for the intended application.

LEVEL OF EVIDENCE

Level Ⅲ.

Automated personalization of biomechanical knee model

PURPOSE

Patient-specific biomechanical models of the knee joint can effectively aid in understanding the reasons for pathologies and improve diagnostic methods and treatment procedures. For deeper research of knee diseases, the development of biomechanical models with appropriate configurations is essential. In this study, we mainly focus on the development of a personalized biomechanical model for the investigation of knee joint pathologies related to patellar motion using automated methods.

METHODS

This study presents a biomechanical model created for patellar motion pathologies research and some techniques for automating the generation of the biomechanical model. To generate geometric models of bones, the U-Net neural network was adapted for 3D input datasets. The method uses the same neural network for segmentation of femur, tibia, patella and fibula. The total size of the train/validation (75/25%) dataset is 18,183 3D volumes of size 512 × 512 × 4 voxels. The configuration of the biomechanical knee model proposed in the paper includes six degrees of freedom for the tibiofemoral and patellofemoral joints, lateral and medial contact surfaces for femur and tibia, and ligaments, representing, among other things, the medial and lateral stabilizers of the knee cap. The development of the personalized biomechanical model was carried out using the OpenSim software system. The automated model generation was implemented using OpenSim Python scripting commands.

RESULTS

The neural network for bones segmentation achieves mean DICE 0.9838. A biomechanical model for realistic simulation of patellar movement within the trochlear groove was proposed. Generation of personalized biomechanical models was automated.

CONCLUSIONS

In this paper, we have implemented a neural network for the segmentation of 3D CT scans of the knee joint to produce a biomechanical model for the study of knee cap motion pathologies. Most stages of the generation process have been automated and can be used to generate patient-specific models.

Deep Learning for Automated Measurement of Patellofemoral Anatomic Landmarks

BACKGROUND

Patellofemoral anatomy has not been well characterized. Applying deep learning to automatically measure knee anatomy can provide a better understanding of anatomy, which can be a key factor in improving outcomes.

METHODS

483 total patients with knee CT imaging (April 2017-May 2022) from 6 centers were selected from a cohort scheduled for knee arthroplasty and a cohort with healthy knee anatomy. A total of 7 patellofemoral landmarks were annotated on 14,652 images and approved by a senior musculoskeletal radiologist. A two-stage deep learning model was trained to predict landmark coordinates using a modified ResNet50 architecture initialized with self-supervised learning pretrained weights on RadImageNet. Landmark predictions were evaluated with mean absolute error, and derived patellofemoral measurements were analyzed with Bland-Altman plots. Statistical significance of measurements was assessed by paired t-tests.

RESULTS

Mean absolute error between predicted and ground truth landmark coordinates was 0.20/0.26 cm in the healthy/arthroplasty cohort. Four knee parameters were calculated, including transepicondylar axis length, transepicondylar-posterior femur axis angle, trochlear medial asymmetry, and sulcus angle. There were no statistically significant parameter differences (p > 0.05) between predicted and ground truth measurements in both cohorts, except for the healthy cohort sulcus angle.

CONCLUSION

Our model accurately identifies key trochlear landmarks with ~0.20-0.26 cm accuracy and produces human-comparable measurements on both healthy and pathological knees. This work represents the first deep learning regression model for automated patellofemoral annotation trained on both physiologic and pathologic CT imaging at this scale. This novel model can enhance our ability to analyze the anatomy of the patellofemoral compartment at scale.

Association of Trochlear Length on Sagittal MRI to Trochlear Dysplasia in Knees With Patellar Instability

Background

Trochlear dysplasia is a primary risk factor for patellar instability and leads to loss of the osteochondral constraint of the patella. Trochleoplasty techniques include the Peterson grooveplasty, which alters the length of the trochlea; however, a radiographic measurement of trochlear length to support this has not been described.

Purpose

To describe measurements to quantify trochlear length on sagittal magnetic resonance imaging in patients with and without patellar instability and to correlate trochlear length with measurements of trochlear dysplasia.

Study Design

Cross-sectional study; Level of evidence, 3.

Methods

A total of 66 age- and sex-matched knees (36 female and 30 male; mean age, 20.8 ± 4.8 years) were included in this study, of which 33 had patellar instability. Trochlear extension length (TEL) and trochlear alpha angle (TAA) were measured on 3 sagittal magnetic resonance imaging scans (center of the knee, center of the medial condyle, and center of the lateral condyle), and measurements were compared between symptomatic and control knees. Receiver operating characteristic curve analysis was performed, and the area under the curve (AUC) was calculated to describe the accuracy of each measurement to distinguish between knees with and without patellar instability. Linear and multivariate regression analyses were performed to assess the relationship between sagittal measurements and axial measurements of trochlear dysplasia, including lateral trochlear inclination, sulcus angle, and trochlear depth, as well as patient size reflected by the epicondylar distance.

Results

In symptomatic knees, the central trochlea extended more proximally than in control knees, as determined by the TEL (14.0 ± 3.0 vs 11.5 ± 2.3 mm, respectively; P < .001) and TAA (68.4° ± 3.8° vs 70.5° ± 3.4°, respectively; P = .017). AUC calculations showed that a TEL ≥11 mm at the central trochlea was predictive of patellar instability in both male and female knees (AUC = 0.83 and 0.77, respectively), as was a TAA ≤67° in female knees (AUC = 0.72). An independent association between the central TEL and sulcus angle was found. The central TEL showed a weak correlation with patient size, as measured by the epicondylar distance, while the TAA did not.

Conclusion

In knees with symptomatic patellar instability, the central trochlea was found to extend 2.5 mm more proximally than in control knees, and this increase in length correlated with severity of trochlear dysplasia. As radiographic examinations of the trochlea and grooveplasty procedures are often based on the proximal extent of the cartilaginous trochlea, further studies are needed to identify the role of trochlear length in the assessment and treatment of trochlear dysplasia in the setting of patellar instability.