The goal of shoulder arthroplasty is to improve comfort and function for a variety of degenerative conditions. Shoulder surgeons who engage an increasingly complex array of clinical problems must understand the factors that may negatively impact results and lead to a higher risk of complications. A better understanding of risk factors will help not only educate patients about potential adverse outcomes, but it also leads to improved ways to mitigate complications by addressing their root cause where possible.
Shoulder Arthroplasty Smart Score: The World’s First Machine Learning-Derived Outcome Measure | Surgeons and researchers worldwide can now quantify shoulder patient outcomes with a new, more efficient measure called “Smart Score”.
Teaming up with KenSci, a data science company located in Seattle, Wash., Exactech has been at the forefront of using ML to better predict outcomes and complications after shoulder arthroplasty. This work is based on Exactech’s clinical database which includes over 11,000 patient visits from 35 centers around the world–all using a standardized data collection tool that records information on demographics, diagnosis, comorbidities, preoperative function, implant information and post-operative function at multiple time points. Predict+ was built on ML algorithms which established a 19-input minimal feature set that was most highly predictive of outcomes and complications after anatomic or reverse shoulder arthroplasty.
Computer navigation leads to more accurate glenoid targeting during Total Shoulder Arthroplasty (TSA) compared with three-dimensional (3D) preoperative planning alone.
The number of total joint replacements is growing rapidly, and the current trend suggests that the number of total joints performed will double by 2030.
With 35 collection sites across the United States and Europe, the Equinoxe database includes information on demographics, comorbidities, implant specifics, 7 PROMs, ROM, radiographic data, and complications—all using standardized forms—for more than 10,000 shoulder cases. This multi-center collection using standardized forms creates the volume of evidence needed to produce the necessary statistical power for accurate analysis of the data.
Multilevel Modeling of Resection Accuracy: Insights from 10,144 Clinical Cases using A Contemporary Computer-Assisted Total Knee Arthroplasty System (Abridged Version)
As a successful treatment for advanced inflammatory and degenerative knee arthritis, total knee arthroplasty (TKA) is projected to expand by 600% to more than three million cases annually by 2030.
A recent technology added CAOS augmentation to conventional mechanical instruments, removing the need for significant instrument relearning. The system has been shown to have a minimal learning curve and offers good usability and has been demonstrated to be non-disruptive to the surgical flow during its early adoption, reported by a subjective survey of users.