Objective The relationship between in vivo knee load predictions and longitudinal cartilage changes has not been investigated. We undertook this study to develop an equation to predict the medial tibiofemoral contact force (MCF) peak during walking in persons with instrumented knee implants, and to apply this equation to determine the relationship between the predicted MCF peak and cartilage loss in patients with knee osteoarthritis (OA). Methods In adults with knee OA (39 women, 8 men; mean ± SD age 61.1 ± 6.8 years), baseline biomechanical gait analyses were performed, and annualized change in medial tibial cartilage volume (mm 3 /year) over 2.5 years was determined using magnetic resonance imaging. In a separate sample of patients with force‐measuring tibial prostheses (3 women, 6 men; mean ± SD age 70.3 ± 5.2 years), gait data plus in vivo knee loads were used to develop an equation to predict the MCF peak using machine learning. This equation was then applied to the knee OA group, and the relationship between the predicted MCF peak and annualized cartilage volume change was determined. Results The MCF peak was best predicted using gait speed, the knee adduction moment peak, and the vertical knee reaction force peak (root mean square error 132.88N; R 2 = 0.81, P < 0.001). In participants with knee OA, the predicted MCF peak was related to cartilage volume change (R 2 = 0.35, β = −0.119, P < 0.001). Conclusion Machine learning was used to develop a novel equation for predicting the MCF peak from external biomechanical parameters. The predicted MCF peak was positively related to medial tibial cartilage volume loss in patients with knee OA.
Meniscectomies are common but not always successful in treating degenerative meniscus tears. Preoperative patient reported outcomes (PROs) and quantitative MRI could help surgeons decide candidacy for meniscectomy. This study demonstrated that pre-operative T2 relaxation times in the medial central femoral cartilage are correlated to pre-operative PROs in patients with degenerative meniscal tears and are predictive of 1 and 2-year changes in PROs post-operation. This study suggests patients who have longer T2 relaxation times pre-operation might not benefit as much from a meniscectomy.
Abstract Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Many ACL-injured subjects develop osteoarthritis within a decade of injury, a major cause of disability without cure. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to a majority of people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes for biomechanical assessment. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for ACL injury prevention training, evaluation of ACL reconstructions, and return-to-sport decision making. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units (IMUs), depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for using sophisticated modeling techniques to enable more accurate assessment along with standardization of data collection and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms.
Cumulative load reflects the total accumulated load across a loading exposure. Estimated cumulative load can identify individuals with or at risk for pathology. However, there is no research into the accuracy of the estimated cumulative load. This study determined: (1) which impulses, from a 500 revolution bicycling activity, accurately estimate cumulative pedal reaction force; and (2) how many impulses are required to accurately estimate cumulative pedal reaction force over 500 revolutions. Twenty-four healthy adults (mean 23.4 [SD 3.1] years; 11 men) participated. Participants performed three bicycling bouts of 10-min in duration and were randomized to one of two groups (group 1 = self-selected power and prescribed cadence of 80 revolutions per minute; group 2 = prescribed power of 100 W and self-selected cadence). The first 10 revolutions (2%) of the normal pedal reaction force (PRFN) and resultant pedal reaction force (PRFR), and the first five revolutions (1%) of the anterior-posterior reaction force (PRFAP) over-estimated cumulative load. The PRFN, PRFAP, and PRFR required 80 revolutions (16%), 320 revolutions (64%) and 65 revolutions (13%), respectively, to accurately estimate cumulative load across 500 cycles. These findings highlight that the context and amount of data collected are important in producing accurate estimates of cumulative load.
