Guest blog initiative
This week we are introducing our guest blog initiative. Periodically, we will highlight how current Theia3D users and thought leaders are using technology to transform biomechanics and their niche within our community.
Colin Bond is a biomechanist at Sanford Health and has worked in this area for the past six years. Previously, Colin was a varsity swimmer at Towson University and completed his PhD in Exercise Science and Nutrition at North Dakota State University. Colin uses his athletics background and experience as a biomechanist to deliver biomechanical assessments to patients at Sanford Health.
Aaron Trunt is from Goodland Minnesota and is a biomedical engineer at Sanford Health. Aaron is also currently completing a doctoral program for Biomedical Engineering at the University of South Dakota. In addition to working with Theia3D, Aaron has a particular interest in baseball and how biomechanics can be used to quantify performance and prevent injury.
Background
Anterior cruciate ligament (ACL) injury is one of the most common injuries in sport with an estimated 200,000 to 500,000 occurring annually in the United States. ACL injury is often treated surgically with an ACL reconstruction (ACLr), or replacement of the injured ACL with a tendon taken from somewhere else on the patient. This is followed by lengthy rehabilitation lasting 6 to 12 months before the athlete can safely return to playing sports. Despite the exponentially growing body of scientific literature surrounding the treatment and rehabilitation of these injuries, over 20% of young, high-risk athletes will re-injure their ACL. The patient’s neuromuscular control plays an integral role in ACL injury risk as up to 70% of ACL injuries are a result of a non-contact mechanism during routine athletic movements such as a cut, pivot, or jump-landing.
Objective, high-fidelity biomechanical assessments using motion capture and force plates may enhance the identification of athletes at risk for ACL injury or the return to sport decision making process for a patient after ACLr, but these assessments have typically been unrealistic in most orthopedics and sports medicine workflows. Patients rehabilitating from ACL injury are commonly seen in the outpatient clinical setting, where clinics have limited space, finances, and technical knowledge to purchase, utilize, and operate traditional optical motion capture systems. Further, traditional motion capture systems require diligent application of reflective markers, minimal clothing to ensure proper marker adherence on the skin, and meticulous verification of marker labels during post-processing. This significantly increases the time spent producing the biomechanical assessment and possibility for errors. Therefore, clinicians have historically turned to subjective movement analyses or field-based tests, which may not paint the most reliable or accurate picture of an athlete's ACL injury risk. This approach also means that many high-fidelity metrics that have previously shown to be associated prospectively with ACL injury, such as knee abduction moment, are unobtainable.
At Sanford, we have been working on a solution to the above challenges leveraging Theia’s markerless motion capture solution. Not only are we able to provide a much more comprehensive report for our clinicians, athletes, and patients, but we have also reduced the time necessary to collect and process that information by over 80% compared to traditional optical motion capture while collecting the data in the exact setting that the patient is rehabilitating! Below we will look at some examples of what our ACL Return to Play (RTP) process looked like prior to markerless motion capture, and what it looks like now that we’ve integrated Theia into the process.
Before Theia: Prior to obtaining Theia, our clinicians at Sanford were left to a largely subjective movement assessment via 2D video. ACL patients would perform a series of movements recorded by two separate cameras. Pre-determined scoring criteria were then used to assess movement quality via 2D joint angles from the video using an ordinal scale between 0 and 2 with a total possible score of 50, per leg, for all movements combined. Not only did a clinician have to spend time cueing and recording the movements on the front end, they then had to “score” the movement quality by hand on the back end and put together the report. Even though this seems like a time-consuming process, it’s still much quicker than the traditional optical Figure 1. Example of a report from a 2D motion capture solutions many of us ACL RTP session with ordinal scoring. are familiar with.
Given the aforementioned constraints in the clinical setting, this was a reasonable attempt at quantifying potentially injurious biomechanics. However, there are still many obvious flaws to this method, which make justifying it as a clinical decision-making tool difficult and one of the reasons we collect plenty of other information on these patients during their visit.
Figure 2. Theia software being used to track the Triple Hop movement.
Figure 3. Animated .gif of the Triple Hop recorded and tracked by Theia.
After Theia: Fast forward to today where we’re able to offer complete 3D biomechanics assessments of ACL patients from start of movement to printed report with detailed results in 20 minutes or less. This new process gives our team exponentially more data while also significantly reducing the time a clinician needs to spend processing and interpreting the report – a win-win if there ever was one.
As one can see, this 3D report has significantly more information and is actually measuring the biomechanical qualities we were trying to approximate with the old 2D methods. For example, we can look at bilateral differences in kinematics/movement strategy, or look at how the joints above and below the knee might be compensating to take load off the involved knee during dynamic tasks like the triple hop. This combined level of efficiency and quality was previously unachievable in the clinical setting, but the evolution of markerless biomechanics solutions has broken down the prior barriers and made semi-real-time in-clinic assessments a reality.
Figure 4. Example of a 3D ACL RTP report
with continuous and time-series metrics.
However, with these new assessment options and infinitely larger datasets at our fingertips, the priority will be shifted towards researchers to determine how best to sort through and interpret all of this information to make meaningful clinical decisions aimed at improving the outcome of the patients in front of them. Such goals are exactly what our integrated team of clinicians and researchers at Sanford are hoping to accomplish, and being able to bring the biomechanics lab into the clinic is a major milestone towards reducing re-injury risk, and improving rehabilitation outcomes in these patients.
To learn more about Theia3D, please reach out to us here.