GAINESVILLE, Fla.--(BUSINESS WIRE)--Exactech, a developer and producer of innovative implants, instrumentation and computer-assisted technologies for joint replacement surgery, and KenSci, a healthcare artificial intelligence (AI) platform company, announced today that a collaborative, foundational study on using machine learning (ML) to predict outcomes after shoulder arthroplasty has been published in Clinical Orthopaedics and Related Research, one of the premier scientific journals in orthopaedics. The research analyzes the potential of ML to use preoperative data to anticipate patients' post-operative results after anatomic total shoulder arthroplasty (aTSA) or reverse total shoulder arthroplasty (rTSA). These results can help surgeons preoperatively identify if a patient will achieve certain clinical improvement thresholds to appropriately risk-stratify patients for these elective procedures. Specifically, this research explores the efficacy of ML to predict the American Shoulder and Elbow Surgery (ASES), Constant, global shoulder function and VAS pain score, as well as to predict a patient's active range of motion in abduction, forward flexion and external rotation. This research also studies the ability of ML to identify if a patient may achieve clinical improvement that exceeds the minimal clinically important difference threshold as well as the substantial clinical benefit threshold for each outcome measure.