Objective classification and scoring of movement deficiencies in patients with anterior cruciate ligament reconstruction

Quantitative analysis of the dominant external factors influencing elite speed Skaters' performance using BP neural network

Introduction

Speed skating, being a popular winter sport, imposes significant demands on elite skaters, necessitating their effective assessment and adaptation to diverse environmental factors to achieve optimal race performance.

Objective

The aim of this study was to conduct a thorough analysis of the predominant external factors influencing the performance of elite speed skaters.

Methods

A total of 403 races, encompassing various race distances and spanning from the 2013 to the 2022 seasons, were examined for eight high-caliber speed skaters from the Chinese national team. We developed a comprehensive analytical framework utilizing an advanced back-propagation (BP) neural neural network model to assess three key factors on race performance: ice rink altitude, ice surface temperature, and race frequency.

Results

Our research indicated that the performance of all skaters improves with higher rink altitudes, particularly in races of 1,000 m and beyond. The ice surface temperature can either enhance or impaire performance and varies in its influences based on skaters' technical characteristics, which had a perceptible or even important influence on races of 1,500 m and beyond, and a negligible influence in the 500 m and 1,000 m races. An increase in race frequency generally contributed to better performance. The influence was relatively minor in the 500 m race, important in the 3,000 m race, and varied among individuals in the 1,000 m and 1,500 m races.

Conclusion

The study results offer crucial guidelines for speed skaters and coaches, aiding in the optimization of their training and competition strategies, ultimately leading to improved competitive performance levels.

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.

Estimation of horizontal running power using foot-worn inertial measurement units

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of the gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7% ± 12.5% and 3.2% ± 13.4%, respectively. The eccentric phase was considered relevant for downhill running, wherein the elastic net model provided the lowest error of 1.8% ± 14.1%. Results showed a similar performance across a range of different speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for the estimating horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets the requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot-worn IMUs.

Using an Affordable Motion Capture System to Evaluate the Prognostic Value of Drop Vertical Jump Parameters for Noncontact ACL Injury

BACKGROUND

Knee kinematic parameters during a drop vertical jump (DVJ) have been demonstrated to be associated with increased risk of noncontact anterior cruciate ligament (ACL) injury. However, standard motion analysis systems are not practical for routine screening. Affordable and practical motion sensor alternatives exist but require further validation in the context of ACL injury risk assessment.

PURPOSE/HYPOTHESIS

To prospectively study DVJ parameters as predictors of noncontact ACL injury in collegiate athletes using an affordable motion capture system (Kinect; Microsoft). We hypothesized that athletes who sustained noncontact ACL injury would have larger initial and peak contact coronal abduction angles and smaller peak flexion angles at the knee during a DVJ.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

102 participants were prospectively recruited from a collegiate varsity sports program. A total of 101 of the 102 athletes (99%) were followed for an entire season for noncontact ACL injury. Each athlete performed 3 DVJs, and the data were recorded using the motion capture system. Initial coronal, peak coronal, and peak sagittal angles of the knee were identified by our software.

RESULTS

Five of the 101 athletes sustained a noncontact ACL injury. Peak coronal angles were significantly greater and peak sagittal flexion angles were significantly smaller in ACL-injured athletes (P = .049, P = .049, respectively). Receiver operating characteristic (ROC) analysis demonstrated an area under the curve of 0.88, 0.92, and 0.90 for initial coronal, peak coronal, and peak sagittal angle, respectively. An initial coronal angle cutoff of 2.96° demonstrated 80% sensitivity and 72% specificity, a peak coronal angle cutoff of 6.16° demonstrated 80% sensitivity and 72% specificity, and a peak sagittal flexion cutoff of 93.82° demonstrated 80% sensitivity and 74% specificity on the study cohort.

CONCLUSION

Increased peak coronal angle and decreased peak sagittal angle during a DVJ were significantly associated with increased risk for noncontact ACL injury. Based on ROC analysis, initial coronal angle showed good prognostic ability, whereas peak coronal angle and peak sagittal flexion provided excellent prognostic ability. Affordable motion capture systems show promise as cost-effective and practical options for large-scale ACL injury risk screening.

Recovery, Rehabilitation, and Return to Full Duty in a Military Population After a Recent Injury: Differences Between Lower-Extremity and Spine Injuries

Purpose

To compare readiness to return to duty in soldiers following recent lower-extremity versus spine injury. The secondary purposes were to provide normative data for the Selective Functional Movement Assessment Top Tier movements (SFMA-TTM) and assess the association between SFMA-TTM scores and future injury occurrence, comparing injuries of the lower extremity and thoracic/lumbar spine.

Methods

SFMA was rated by trained assessors on 480 U.S. Army soldiers within 2 weeks of being cleared to return to duty after recent lower-extremity or lumbar/thoracic injury. Participants were followed for 1 year to determine incidence of subsequent time-loss injury.

Results

Only 74.4% of soldiers felt 100% mission capable when returning to full duty (73.6% lower-extremity; 76.5% spine). After 1 year, 37.9% had sustained a time-loss injury, and pain with movement at baseline was associated with higher odds for having an injury (odd ratio 1.53 95% confidence interval 1.04-2.24; P = .032). Almost all (99.8%) had at least 1 dysfunctional pattern, and 44.1% had pain with at least 1 movement (40.3% with previous lower-extremity injury; 54.6% with previous spine injury) after being cleared to return to duty.

Conclusions

One in four patients did not feel 100% mission capable upon being cleared for full duty. Pain with movement was also associated with future injury. Regardless of recent injury type, 99.8% of soldiers returned to full unrestricted duty with at least 1dysfunctional movement pattern and 44.1% had pain with at least 1 of the SFMA-TTM movements.

Level of Evidence

Level III, retrospective comparative cohort study.

Kinetic measurement system use in individuals following anterior cruciate ligament reconstruction: a scoping review of methodological approaches

Purpose

Our primary objectives were to (1) describe current approaches for kinetic measurements in individuals following anterior cruciate ligament reconstruction (ACLR) and (2) suggest considerations for methodological reporting. Secondarily, we explored the relationship between kinetic measurement system findings and patient-reported outcome measures (PROMs).

Methods

We followed the PRISMA extension for scoping reviews and Arksey and O’Malley’s 6-stage framework. Seven electronic databases were systematically searched from inception to June 2020. Original research papers reporting parameters measured by kinetic measurement systems in individuals at least 6-months post primary ACLR were included.

Results

In 158 included studies, 7 kinetic measurement systems (force plates, balance platforms, pressure mats, force-measuring treadmills, Wii balance boards, contact mats connected to jump systems, and single-sensor insoles) were identified 4 main movement categories (landing/jumping, standing balance, gait, and other functional tasks). Substantial heterogeneity was noted in the methods used and outcomes assessed; this review highlighted common methodological reporting gaps for essential items related to movement tasks, kinetic system features, justification and operationalization of selected outcome parameters, participant preparation, and testing protocol details. Accordingly, we suggest considerations for methodological reporting in future research. Only 6 studies included PROMs with inconsistency in the reported parameters and/or PROMs.

Conclusion

Clear and accurate reporting is vital to facilitate cross-study comparisons and improve the clinical application of kinetic measurement systems after ACLR. Based on the current evidence, we suggest methodological considerations to guide reporting in future research. Future studies are needed to examine potential correlations between kinetic parameters and PROMs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40634-021-00397-0.

Cross-validation of a machine learning algorithm that determines anterior cruciate ligament rehabilitation status and evaluation of its ability to predict future injury

Classification algorithms determine the similarity of an observation to defined classes, e.g., injured or healthy athletes, and can highlight treatment targets or assess progress of a treatment. The primary aim was to cross-validate a previously developed classification algorithm using a different sample, while a secondary aim was to examine its ability to predict future ACL injuries. The examined outcome measure was 'healthy-limb' class membership probability, which was compared between a cohort of athletes without previous or future (No Injury) previous (PACL) and future ACL injury (FACL). The No Injury group had significantly higher probabilities than the PACL (p < 0.001; medium effect) and FACL group (p ≤ 0.045; small effect). The ability to predict group membership was poor for the PACL (area under curve [AUC]; 0.61<AUC<0.62) and FACL group (0.57<AUC<0.59). The ACL injury incidence proportion was highest in athletes with probabilities below 0.20 (9.4%; +2.7% to baseline), while athletes with probabilities above 0.80 had an incidence proportion of 4.1% (-2.6%). While findings that a low probability might represent an increase in injury risk on a group level, it is not sensitive enough for injury screening to predict a future injury on the individual level.

Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries

Background:

Technological innovation is a key component of orthopaedic surgery. With the integration of powerful technologies in surgery and clinical practice, artificial intelligence (AI) may become an important tool for orthopaedic surgeons in the future. Through adaptive learning and problem solving that serve to constantly increase accuracy, machine learning algorithms show great promise in orthopaedics.

Purpose:

To investigate the current and potential uses of AI in the management of anterior cruciate ligament (ACL) injury.

Study Design:

Systematic review; Level of evidence, 3.

Methods:

A systematic review of the PubMed, MEDLINE, Embase, Web of Science, and SPORTDiscus databases between their start and August 12, 2020, was performed by 2 independent reviewers. Inclusion criteria included application of AI anywhere along the spectrum of predicting, diagnosing, and managing ACL injuries. Exclusion criteria included non-English publications, conference abstracts, review articles, and meta-analyses. Statistical analysis could not be performed because of data heterogeneity; therefore, a descriptive analysis was undertaken.

Results:

A total of 19 publications were included after screening. Applications were divided based on the different stages of the clinical course in ACL injury: prediction (n = 2), diagnosis (n = 12), intraoperative application (n = 1), and postoperative care and rehabilitation (n = 4). AI-based technologies were used in a wide variety of applications, including image interpretation, automated chart review, assistance in the physical examination via optical tracking using infrared cameras or electromagnetic sensors, generation of predictive models, and optimization of postoperative care and rehabilitation.

Conclusion:

There is an increasing interest in AI among orthopaedic surgeons, as reflected by the applications for ACL injury presented in this review. Although some studies showed similar or better outcomes using AI compared with traditional techniques, many challenges need to be addressed before this technology is ready for widespread use.

Can Biomechanical Testing After Anterior Cruciate Ligament Reconstruction Identify Athletes at Risk for Subsequent ACL Injury to the Contralateral Uninjured Limb?

BACKGROUND

Athletes are twice as likely to rupture the anterior cruciate ligament (ACL) on their healthy contralateral knee than the reconstructed graft after ACL reconstruction (ACLR). Although physical testing is commonly used after ACLR to assess injury risk to the operated knee, strength, jump, and change-of-direction performance and biomechanical measures have not been examined in those who go on to experience a contralateral ACL injury, to identify factors that may be associated with injury risk.

PURPOSE

To prospectively examine differences in biomechanical and clinical performance measures in male athletes 9 months after ACLR between those who ruptured their previously uninjured contralateral ACL and those who did not at 2-year follow-up and to examine the ability of these differences to predict contralateral ACL injury.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

A cohort of male athletes returning to level 1 sports after ACLR (N = 1045) underwent isokinetic strength testing and 3-dimensional biomechanical analysis of jump and change-of-direction tests 9 months after surgery. Participants were followed up at 2 years regarding return to play or at second ACL injury. Between-group differences were analyzed in patient-reported outcomes, performance measures, and 3-dimensional biomechanics for the contralateral limb and asymmetry. Logistic regression was applied to determine the ability of identified differences to predict contralateral ACL injury.

RESULTS

Of the cohort, 993 had follow-up at 2 years (95%), with 67 experiencing a contralateral ACL injury and 38 an ipsilateral injury. Male athletes who had a contralateral ACL injury had lower quadriceps strength and biomechanical differences on the contralateral limb during double- and single-leg drop jump tests as compared with those who did not experience an injury. Differences were related primarily to deficits in sagittal plane mechanics and plyometric ability on the contralateral side. These variables could explain group membership with fair to good ability (area under the curve, 0.74-0.80). Patient-reported outcomes, limb symmetry of clinical performance measures, and biomechanical measures in change-of-direction tasks did not differentiate those at risk for contralateral injury.

CONCLUSION

This study highlights the importance of sagittal plane control during drop jump tasks and the limited utility of limb symmetry in performance and biomechanical measures when assessing future contralateral ACL injury risk in male athletes. Targeting the identified differences in quadriceps strength and plyometric ability during late-stage rehabilitation and testing may reduce ACL injury risk in healthy limbs in male athletes playing level 1 sports.

CLINICAL RELEVANCE

This study highlights the importance of assessing the contralateral limb after ACLR and identifies biomechanical differences, particularly in the sagittal plane in drop jump tasks, that may be associated with injury to this limb. These factors could be targeted during assessment and rehabilitation with additional quadriceps strengthening and plyometric exercises after ACLR to potentially reduce the high risk of injury to the previously healthy knee.

REGISTRATION

NCT02771548 (ClinicalTrials.gov identifier).

Whole-Body Change-of-Direction Task Execution Asymmetries After Anterior Cruciate Ligament Reconstruction

Cutting maneuvers can be executed at a range of angles and speeds, and these whole-body task descriptors are closely associated with lower-limb mechanical loading. Asymmetries in angle and speed when changing direction off the operated and nonoperated limbs after anterior cruciate ligament reconstruction may therefore influence the interpretation of interlimb differences in joint-level biomechanical parameters. The authors hypothesized that athletes would reduce center-of-mass heading angle deflection and body rotation during the change-of-direction stance phase when cutting from the operated limb, and would compensate for this by orienting their center-of-mass trajectory more toward the new intended direction of travel prior to touchdown. A total of 144 male athletes 8 to 10 months after anterior cruciate ligament reconstruction performed a maximum-effort sidestep cutting maneuver while kinematic, kinetic, and ground reaction force data were recorded. Peak ground reaction force and knee joint moments were lower when cutting from the operated limb. Center-of-mass heading angle deflection during stance phase was reduced for cuts performed from the operated limb and was negatively correlated with heading angle at touchdown. Between-limb differences in body orientation and horizontal velocity at touchdown were also observed. These systematic asymmetries in cut execution may require consideration when interpreting joint-level interlimb asymmetries after anterior cruciate ligament reconstruction and are suggestive of the use of anticipatory control to co-optimize task achievement and mechanical loading.

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.