Background: Bioabsorbable screws for anterior cruciate ligament reconstruction (ACLR) have been a popular choice, with theoretical advantages in imaging and surgery. Titanium and poly-L-lactic acid with hydroxyapatite (PLLA-HA) screws have been compared, but with less than a decade of follow-up. Purpose/Hypothesis: The purpose was to compare long-term outcomes of hamstring autograft ACLR using either PLLA-HA screws or titanium screws. We hypothesized there would be no difference at 13 years in clinical scores or tunnel widening between PLLA-HA and titanium screw types, along with high-grade resorption and ossification of PLLA-HA screws. Study Design: Randomized controlled trial; Level of evidence, 1. Methods: Forty patients undergoing ACLR were randomized to receive either a PLLA-HA screw or a titanium screw for ACL hamstring autograft fixation. Blinded evaluation was performed at 2, 5, and 13 years using the International Knee Documentation Committee score, Lysholm knee score, and KT-1000 arthrometer. Magnetic resonance imaging (MRI) was performed at 2 or 5 years and 13 years to evaluate tunnel volumes, ossification around the screw, graft integration, and cyst formation. Computed tomography (CT) of patients with PLLA-HA was performed at 13 years to evaluate tunnel volumes and intratunnel ossification. Results: No differences were seen in clinical outcomes at 2, 5, or 13 years between the 2 groups. At 13 years, tibial tunnel volumes were smaller for the PLLA-HA group (2.17 cm3) compared with the titanium group (3.33 cm3; P = .004). By 13 years, the PLLA-HA group had complete or nearly complete resorption on MRI or CT scan. Conclusion: Equivalent clinical results were found between PLLA-HA and titanium groups at 2, 5, and 13 years. Although PLLA-HA screws had complete or nearly complete resorption by 13 years, tunnel volumes remained largely unchanged, with minimal ossification.

Virtual knees, with specimen-specific anatomy and mechanics, require heterogeneous data collected on the same knee. Specimen-specific data such as the specimen geometry, physiological joint kinematics-kinetics and contact mechanics are necessary in the development of virtual knee specimens for clinical and scientific simulations. These data are also required to capture or evaluate the predictive capacity of the model to represent joint and tissue mechanical response. This document details the collection of magnetic resonance imaging data and, tibiofemoral joint and patellofemoral joint mechanical testing data . These data were acquired for a cohort of eight knee specimens representing different populations with varying gender, age and perceived health of the joint. These data were collected as part of the Open Knee(s) initiative. Imaging data when combined with joint mechanics data, may enable development and assessment of authentic specimen-specific finite element models of the knee. The data may also guide prospective studies for association of anatomical and biomechanical markers in a specimen-specific manner.

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Background: This study aimed to build a deep learning model to automatically segment heterogeneous clinical MRI scans by optimizing a pre-trained model built from a homogeneous research dataset with transfer learning. Methods: Conditional generative adversarial networks pretrained on the Osteoarthritis Initiative MR images was transferred to 30 sets of heterogenous MR images collected from clinical routines. Two trained radiologists manually segmented the 30 sets of clinical MR images for model training, validation and test. The model performance was compared to models trained from scratch with different datasets, as well as two radiologists. A 5-fold cross validation was performed. Results: The transfer learning model obtained an overall averaged Dice coefficient of 0.819, an averaged 95 percentile Hausdorff distance of 1.463 mm, and an averaged average symmetric surface distance of 0.350 mm on the 5 random holdout test sets. A 5-fold cross validation had a mean Dice coefficient of 0.801, mean 95 percentile Hausdorff distance of 1.746 mm, and mean average symmetric surface distance of 0.364 mm. It outperformed other models and performed similarly as the radiologists.Conclusions: A transfer learning model was able to automatically segment knee cartilage, with performance comparable to human, using heterogeneous clinical MR images with a small training data size.In addition, the model proved robust when tested through cross validation and on images from a different vendor. We found it feasible to perform fully automated cartilage segmentation of clinical knee MR images, which would facilitate the clinical application of quantitative MRI techniques and other prediction models for improved patient treatment planning.

The degree of mastoid pneumatization did not affect the success rate of cartilage type 1 tympanoplasty. Further studies with larger numbers of patients are needed to evaluate the relationship between the degree of the mastoid pneumatization and anatomical outcomes after placement of various graft types.