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Collagen organization of the anterior cruciate ligament (ACL) can be evaluated using T2* relaxometry. However, T2* mapping requires manual image segmentation, which is a time‐consuming process and prone to inter‐ and intra‐ segmenter variability. Automating segmentation would address these challenges. A model previously trained using Constructive Interference in Steady State (CISS) scans was applied to T2* segmentation via transfer learning. It was hypothesized that there would be no significant differences in the model's segmentation performance between T2* and CISS, structural measures versus ground truth manual segmentation, and reliability versus independent and retest manual segmentation. Transfer learning was conducted using 54 T2* scans of the ACL. Segmentation performance was assessed with Dice coefficient, precision, and sensitivity, and structurally with T2* value, volume, subvolume proportions, and cross‐sectional area. Model performance relative to independent manual segmentation and repeated segmentation by the ground truth segmenter (retest) were evaluated on a random subset. Segmentation performance was analyzed with Mann–Whitney U tests, structural measures with Wilcoxon signed‐rank tests, and performance relative to manual segmentation with repeated‐measures analysis of variance/Tukey tests (α = 0.05). T2* segmentation performance was not significantly different from CISS on all measures (p > 0.35). No significant differences were detected in structural measures (p > 0.50). Automatic segmentation performed as well as the retest on all segmentation measures, whereas independent segmentations were lower than retest and/or automatic segmentation (p < 0.023). Structural measures were not significantly different between segmenters. The automatic segmentation model performed as well on the T2* sequence as on CISS and outperformed independent manual segmentation while performing as well as retest segmentation. [ABSTRACT FROM AUTHOR]

Non-invasive methods to document healing anterior cruciate ligament (ACL) structural properties could potentially identify patients at risk for revision surgery. The objective was to evaluate machine learning models to predict ACL failure load from magnetic resonance images (MRI) and to determine if those predictions were related to revision surgery incidence. It was hypothesized that the optimal model would demonstrate a lower mean absolute error (MAE) than the benchmark linear regression model, and that patients with a lower estimated failure load would have higher revision incidence 2 years post-surgery. Support vector machine, random forest, AdaBoost, XGBoost, and linear regression models were trained using MRI T2* relaxometry and ACL tensile testing data from minipigs (n = 65). The lowest MAE model was used to estimate ACL failure load for surgical patients at 9 months post-surgery (n = 46) and dichotomized into low and high score groups via Youden's J statistic to compare revision incidence. Significance was set at alpha = 0.05. The random forest model decreased the failure load MAE by 55% (Wilcoxon signed-rank test: p = 0.01) versus the benchmark. The low score group had a higher revision incidence (21% vs. 5%; Chi-square test: p = 0.09). ACL structural property estimates via MRI may provide a biomarker for clinical decision making. [ABSTRACT FROM AUTHOR]

Background: Quantitative magnetic resonance imaging (qMRI) methods were developed to establish the integrity of healing anterior cruciate ligaments (ACLs) and grafts. Whether qMRI variables predict risk of reinjury is unknown. Purpose: To determine if qMRI measures at 6 to 9 months after bridge-enhanced ACL restoration (BEAR) can predict the risk of revision surgery within 2 years of the index procedure. Study Design: Cohort study; Level of evidence, 2. Methods: Originally, 124 patients underwent ACL restoration as part of the BEAR I, BEAR II, and BEAR III prospective trials and had consented to undergo an MRI of the surgical knee 6 to 9 months after surgery. Only 1 participant was lost to follow-up, and 4 did not undergo MRI, leaving a total of 119 patients for this study. qMRI techniques were used to determine the mean cross-sectional area; normalized signal intensity; and a qMRI-based predicted failure load, which was calculated using a prespecified equation based on cross-sectional area and normalized signal intensity. Patient-reported outcomes (International Knee Documentation Committee subjective score), clinical measures (hamstring strength, quadriceps strength, and side-to-side knee laxity), and functional outcomes (single-leg hop) were also measured at 6 to 9 months after surgery. Univariate and multivariable analyses were performed to determine the odds ratios (ORs) for revision surgery based on the qMRI and non-imaging variables. Patient age and medial posterior tibial slope values were included as covariates. Results: In total, 119 patients (97%), with a median age of 17.6 years, underwent MRI between 6 and 9 months postoperatively. Sixteen of 119 patients (13%) required revision ACL surgery. In univariate analyses, higher International Knee Documentation Committee subjective score at 6 to 9 months postoperatively (OR = 1.66 per 10-point increase; P =.035) and lower qMRI-based predicted failure load (OR = 0.66 per 100-N increase; P =.014) were associated with increased risk of revision surgery. In the multivariable model, when adjusted for age and posterior tibial slope, the qMRI-based predicted failure load was the only significant predictor of revision surgery (OR = 0.71 per 100 N; P =.044). Conclusion: Quantitative MRI-based predicted failure load of the healing ACL was a significant predictor of the risk of revision within 2 years after BEAR surgery. The current findings highlight the potential utility of early qMRI in the postoperative management of patients undergoing the BEAR procedure. [ABSTRACT FROM AUTHOR]

Objectives: Animal models are needed to reliably separate the effects of mechanical joint instability and inflammation on posttraumatic osteoarthritis (PTOA) pathogenesis. We hypothesized that our modified intra-articular drilling (mIAD) procedure induces cartilage damage and synovial changes through increased inflammation without causing changes in gait., Methods: Twenty-four Yucatan minipigs were randomized into the mIAD (n=12) or sham control group (n=12). mIAD animals had two osseous tunnels drilled into each of the tibia and femur adjacent to the anterior cruciate ligament (ACL) attachment sites on the left hind knee. Surgical and contralateral limbs were harvested 15 weeks post-surgery. Cartilage degeneration was evaluated macroscopically and histologically. Synovial changes were evaluated histologically. Interleukin-1 beta (IL-1β), nuclear factor kappa B (NF-κB), and tumor necrosis factor alpha (TNF-α) mRNA expression levels in the synovial membrane were measured using quantitative real-time polymerase chain reaction. IL-1β and NF-κB levels in chondrocytes were assessed using immunohistochemistry. Load asymmetry during gait was recorded by a pressure-sensing walkway system before and after surgery., Results: The mIAD surgical knees demonstrated greater gross and histological cartilage damage than contralateral (P<.01) and sham knees (P<.05). Synovitis was present only in the mIAD surgical knee. Synovial inflammatory marker (IL-1β, NF-κB, and TNF-α) expression was three times higher in the mIAD surgical knee than the contralateral (P<.05). Chondrocyte IL-1β and NF-κB levels were highest in the mIAD surgical knee. In general, there were no significant changes in gait., Conclusions: The mIAD model induced PTOA through inflammation without affecting gait mechanics. This large animal model has significant applications for evaluating the role of inflammation in PTOA and for developing therapies aimed at reducing inflammation following joint injury., Competing Interests: Braden C. Fleming is a founder of Miach Orthopaedics, receives royalties from Springer Publishing, and receives a stipend from the American Journal of Sports Medicine. Brett D. Owens is a paid consultant for Conmed, Miach, Vericel, and Mitek, and receives inventor royalties from Conmed and a stipend from the American Journal of Sports Medicine., (AJTR Copyright © 2023.)

Collagen organization of the anterior cruciate ligament (ACL) can be evaluated using T 2 * relaxometry. However, T 2 * mapping requires manual image segmentation, which is a time-consuming process and prone to inter- and intra- segmenter variability. Automating segmentation would address these challenges. A model previously trained using Constructive Interference in Steady State (CISS) scans was applied to T 2 * segmentation via transfer learning. It was hypothesized that there would be no significant differences in the model's segmentation performance between T 2 * and CISS, structural measures versus ground truth manual segmentation, and reliability versus independent and retest manual segmentation. Transfer learning was conducted using 54 T 2 * scans of the ACL. Segmentation performance was assessed with Dice coefficient, precision, and sensitivity, and structurally with T 2 * value, volume, subvolume proportions, and cross-sectional area. Model performance relative to independent manual segmentation and repeated segmentation by the ground truth segmenter (retest) were evaluated on a random subset. Segmentation performance was analyzed with Mann-Whitney U tests, structural measures with Wilcoxon signed-rank tests, and performance relative to manual segmentation with repeated-measures analysis of variance/Tukey tests (α = 0.05). T 2 * segmentation performance was not significantly different from CISS on all measures (p > 0.35). No significant differences were detected in structural measures (p > 0.50). Automatic segmentation performed as well as the retest on all segmentation measures, whereas independent segmentations were lower than retest and/or automatic segmentation (p < 0.023). Structural measures were not significantly different between segmenters. The automatic segmentation model performed as well on the T 2 * sequence as on CISS and outperformed independent manual segmentation while performing as well as retest segmentation., (© 2022 Orthopaedic Research Society. Published by Wiley Periodicals LLC.)

The roles that cytokines and matrix metalloproteinases play in the onset and progression of posttraumatic osteoarthritis (PTOA) remain a topic of debate. The study objective was to evaluate the concentrations of these inflammatory mediators during the development of mild to moderate PTOA in the porcine anterior cruciate ligament (ACL) surgical model. We hypothesized that there would be more animals with detectable mediators in the pigs that develop moderate PTOA (those receiving ACL reconstruction or untreated ACL transection) compared to those that develop mild PTOA (those receiving scaffold-enhanced ACL repair). 36 Yucatan minipigs underwent ACL transection and were randomized to: 1) no further treatment, 2) ACL reconstruction, or 3) scaffold-enhanced ACL repair. Synovial fluid samples were obtained pre-operatively, and at 1, 4, 12, 26 and 52 weeks post-operatively. The concentrations of inflammatory mediator in the synovial fluid samples were evaluated via multiplex assay. Macroscopic cartilage assessments were performed following euthanasia at 52 weeks. As found in prior studies, the repair group had significantly less cartilage damage than either the ACL transected or ACL reconstruction groups (P<.03). The presence and concentrations of the biomarkers were influenced by surgical group and time. In general, the concentrations of inflammatory mediators were higher in the repair group, which exhibited less cartilage damage than the other two treatment groups. While this finding disproved the hypotheses, these data suggest that the metabolic activity of the joints exhibiting less cartilage damage remained higher over the 52-week period than those that did not., Competing Interests: It should be noted that Dr. Murray is a founder, paid consultant and equity holder, Dr. Proffen is a paid consultant and equity holder, and Dr. Fleming is a founder of Miach Orthopaedics, Inc, which was formed to upscale production of a scaffold for ACL repair and is related to one of the ACL procedures described herein. Drs. Murray and Proffen maintain a conflict-of-interest management plan approved by Boston Children’s Hospital and Harvard Medical School. Dr. Fleming also maintains a conflict-of-interest management plan with Rhode Island Hospital with similar oversight., (AJTR Copyright © 2021.)