Louis E. DeFrate’s research while affiliated with Duke University and other places

Background The anterior cruciate ligament (ACL) is loaded under tension when the tibia translates anteriorly relative to the femur. The shape of the articular surfaces of the tibiofemoral joint may influence the amount of anterior tibial translation under compressive loading. Thus, a steep lateral tibial plateau and a shallow medial plateau are thought to be risk factors for ACL injury. Purpose/Hypothesis The purpose of this study was to evaluate whether tibial plateau slope and depth influence peak ACL strain during a single-leg jump. We hypothesized that there would be a significant correlation between tibial plateau slope and depth with ACL strain. Study Design Descriptive laboratory study. Methods A total of 17 healthy participants (8 male, 9 female) were assessed using magnetic resonance imaging (MRI) and high-speed biplanar radiography to obtain peak ACL strain during a single-leg jump. Two orthopaedic surgeons used the sagittal plane MRI scans to measure the medial and lateral tibial plateau slopes and the medial tibial plateau depth. The intraclass correlation coefficient was used to assess measurement reliability, and the Spearman rank correlation was used to evaluate the relationship between measurements of tibial morphology and peak ACL strain during the single-leg jump. Results The overall range of intraclass correlation coefficients for intra- and interrater reliability of the medial and lateral tibial plateau slopes and medial plateau depth was 0.59 to 0.97. No significant correlations were found between peak ACL strain and any of the slope or depth measurements. Conclusion In this cohort of healthy participants, correlations between any of the tibial plateau measurements with peak ACL strain during a single-leg jump were not detected. These findings are consistent with prior work, suggesting that tibial plateau slope and depth may not be linked to risk for ACL rupture. However, it is possible that tibial plateau morphology may interact with other factors to increase ACL injury risk or that individuals with extreme slope angles may produce differing results. Clinical Relevance This study enhances the knowledge of the loading mechanisms for the ACL and thus improves the understanding of risk factors for ACL injury.

Objective We sought to measure the deformation of tibiofemoral cartilage immediately following a 3-mile treadmill run, as well as the recovery of cartilage thickness the following day. To enable these measurements, we developed and validated deep learning models to automate tibiofemoral cartilage and bone segmentation from double-echo steady-state magnetic resonance imaging (MRI) scans. Design Eight asymptomatic male participants arrived at 7 a.m., rested supine for 45 min, underwent pre-exercise MRI, ran 3 miles on a treadmill, and finally underwent post-exercise MRI. To assess whether cartilage recovered to its baseline thickness, participants returned the following morning at 7 a.m., rested supine for 45 min, and underwent a final MRI session. These images were used to generate 3D models of the tibia, femur, and cartilage surfaces at each time point. Site-specific tibial and femoral cartilage thicknesses were measured from each 3D model. To aid in these measurements, deep learning segmentation models were developed. Results All trained deep learning models demonstrated repeatability within 0.03 mm or approximately 1 % of cartilage thickness. The 3-mile run induced mean compressive strains of 5.4 % (95 % CI = 4.1 to 6.7) and 2.3 % (95 % CI = 0.6 to 4.0) for the tibial and femoral cartilage, respectively. Furthermore, both tibial and femoral cartilage thicknesses returned to within 1 % of baseline thickness the following day. Conclusions The 3-mile treadmill run induced a significant decrease in both tibial and femoral cartilage thickness; however, this was largely ameliorated the following morning.

There is limited data quantifying the influence of running on hip cartilage mechanics. The goal of this investigation was to quantify changes in hip joint bone‐to‐bone distance in response to a 3‐mile treadmill run. We acquired magnetic resonance (MR) images of the dominant hip of eight young, asymptomatic runners (5 males, 3 females) before and immediately after they ran 3 miles at a self‐selected pace on a level treadmill. The femoral heads and acetabula were semi‐automatically segmented from the pre‐ and post‐exercise MR images to generate three‐dimensional models of each participant's hip that were used to compute changes in the bone‐to‐bone distances incurred by the running exercise. We observed a significant 3% decrease in bone‐to‐bone distance from 3.47±0.20 mm to 3.36±0.22 mm between the femoral head and acetabulum after a 3‐mile treadmill run (mean ± 95% confidence interval; p=0.03). These findings provide new baseline data describing how running impacts the hip joint in young, asymptomatic runners. This article is protected by copyright. All rights reserved.

Objective: The measurement of in vivo intervertebral disc (IVD) mechanics may be used to understand the etiology of IVD degeneration and low back pain (LBP). To this end, our lab has developed methods to measure IVD morphology and uniaxial compressive deformation (% change in IVD height) resulting from dynamic activity, in vivo, using magnetic resonance images (MRI). However, due to the time-intensive nature of manual image segmentation, we sought to validate an image segmentation algorithm that could accurately and reliably reproduce models of in vivo tissue mechanics. Design: Therefore, we developed and evaluated two commonly employed deep learning architectures (2D and 3D U-Net) for the segmentation of IVDs from MRI. The performance of these models was evaluated for morphological accuracy by comparing predicted IVD segmentations (Dice similarity coefficient, mDSC; average surface distance, ASD) to manual (ground truth) measures. Likewise, functional reliability and precision were assessed by evaluating the intraclass correlation coefficient (ICC) and standard error of measurement (SEm) of predicted and manually derived deformation measures. Results: Peak model performance was obtained using the 3D U-net architecture, yielding a maximum mDSC = 0.9824 and component-wise ASDx = 0.0683 mm; ASDy = 0.0335 mm; ASDz = 0.0329 mm. Functional model performance demonstrated excellent reliability ICC = 0.926 and precision SEm = 0.42%. Conclusions: This study demonstrated that a deep learning framework can precisely and reliably automate measures of IVD function, drastically improving the throughput of these time-intensive methods.

Objective To examine the effects of a 6-month weight loss intervention on physical function, inflammatory biomarkers, and metabolic biomarkers in both those with and without osteoarthritis (OA). Design 59 individuals ≥60 years old with obesity and a functional impairment were enrolled into this IRB approved clinical trial and randomized into one of two 6-month weight loss arms: a higher protein hypocaloric diet or a standard protein hypocaloric diet. All participants were prescribed individualized 500-kcal daily-deficit diets, with a goal of 10% weight loss. Additionally, participants participated in three, low-intensity, exercise sessions per week. Physical function, serum biomarkers and body composition data were assessed at the baseline and 6-month timepoints. Statistical analyses assessed the relationships between biomarkers, physical function, body composition, and OA status as a result of the intervention. Results No group effects of dietary intervention were detected on any outcome measures (multiple p > 0.05). During the 6-month trial, participants lost 6.2 ± 4.0% of their bodyweight (p < 0.0001) and experienced improved physical function on the Short-Performance-Physical-Battery (p < 0.0001), 8-foot-up-and-go (p < 0.0001), and time to complete 10-chair-stands (p < 0.0001). Adiponectin concentrations (p = 0.0480) were elevated, and cartilage oligomeric matrix protein (COMP) concentrations (p < 0.0001) were reduced; further analysis revealed that reductions in serum COMP concentrations were greater in OA-negative individuals. Conclusions These results suggest that weight loss in older adults with and without OA may provide a protective effect to cartilage and OA. In particular, OA-negative individuals may be able to mitigate changes associated with OA through weight loss.