The patellofemoral joint (PFJ) is a complex articulation between the patella and the femur, in-volved in the extensor mechanism of the knee. Patellofemoral disorders can be classified into ob-jective patellar instability, potential patellar instability, and patellofemoral pain syndrome. An-atomical factors such as trochlear dysplasia, patella alta, and tibial tuberosity-trochlear groove (TT-TG) distance contribute to instability. Patellofemoral instability can result in various types of dislocations, and the frequency of dislocation can be categorized as recurrent, habitual, or per-manent. Primary patellar dislocation requires diagnostic framing, including physical examina-tion and imaging. Magnetic resonance imaging (MRI) is essential for assessing the extent of damage, such as bone bruises, osteochondral fractures, and medial patellofemoral ligament (MPFL) rupture. Treatment options for primary dislocation include urgent surgery for oste-ochondral fragments or conservative treatment for cases without lesions. Follow-up after treat-ment involves imaging screening and assessing principal and secondary factors of instability. Detecting and addressing these factors is crucial for preventing recurrent dislocations and opti-mizing patient outcomes.

(1) Background: The mediolateral patella displacement is of interest for diagnostics and clinically relevant research questions. Apart from manual testing, no standardized method is currently available. Proper quantification of patella mobility is necessary to understand pathologies at the patellofemoral joint better; (2) Methods: Patella mobility was assessed in 25 healthy individuals using a Patellostabilometer, a new prototype instrument for quantification of the mediolateral patella displacement. The participants underwent measurements of the mediolateral displacement three times using the Patellostabilometer. A maximal force of 10N was applied for patella movement. Additionally, leg length, circumference of the knee, upper- and lower leg was measured. (3) Results: Lateral patella displacement of 18.27±3.76 mm (range 15.85-20.64mm, interquartile range (IQR) of 4.79) was measured. The medial patella displacement showed 24.47±6.59mm (range 19.29-29.76mm, IQR of 10.47). The test-retest measurement error was 2.32±1.76mm (IQR of 2.38mm), with five outliers. There was greater test-retest variability between the measurements of the medial displacement comparing to the lateral one (4) Conclusions: The test-retest variability reached 7% of the patella displacement. Other parameters provided no significant correlations. Based on the natural patellofemoral mobility a precise and clinically relevant quantification of patella mobility is allowed.

Whether resurfacing or not resurfacing the patella during total knee arthroplasty (TKA) still is a challenge to orthopedic surgeons. A significant reason for this controversy is the far from perfect outcomes of both techniques, resulting from inadequate knowledge of normal patellar dimensions in a diseased one. The primary purpose of the current study is to find the pre-diseased patellar dimensions and the ethnic differences in patellar dimensions. We measured the patella's dimensions on 927 normal young adult knee MRIs from seven different ethnicities. Besides comparing the dimensions between sexes, ages, and sides, we analyzed the differences among ethnic groups. The average thickness was 25.12±2.33 mm; the average width was 44.57±4.32 mm, the average articular surface length was 32.69±3.75 mm, with significant gender, age, and ethnic differences. There were also significant differences in dimensions among ethnic groups, except for between the Indians and Far Eastern Asians and between the Arabs and North Africans. We could also find a robust mathematical relationship between the patella's width, length, and thickness. The ethnic differences in patellar dimensions found in this study can help optimize surgical technique and implant designs for patellar resurfacing. The mathematical equation will help the surgeons find the normal, pre-diseased patella thickness to prevent over-or under-stuffing during the patellar resurfacing.

Background: Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries. Methods: We investigate a combined physics-based simulation and data-based method. 285 running trials from 76 real runners are subjected to physics-based simulation to recover forces in the Achilles tendon and patella ligament, and the collected data are used to train and test a data-based model using elastic net and gradient boosting methods. Results: Correlations up to 0.95 and 0.71 for the patella ligament and Achilles tendon forces, respectively, are obtained, but no single best predictive algorithm can be identified. Conclusions: Prediction of tissues loads based on body-mounted IMUs appears promising but requires further investigation before deployment as a general option for users of running watches to reduce running-related injuries.