Zimmer Biomet Holdings, Inc. (NYSE: ZBH), headquartered in Warsaw, Indiana, is a global medical technology company with a portfolio focused on maximizing mobility and improving musculoskeletal health. It was named to Forbes’ America’s Best Companies 2026 list and employs approximately 18,000 people worldwide.
According to a new study published in the journal Diagnostics, a machine learning algorithm developed by Zimmer Biomet has demonstrated significantly higher accuracy in diagnosing periprosthetic joint infection – one of the most dreaded and expensive complications in orthopedic surgery – than experienced physicians evaluating the same cases.
Periprosthetic joint infection, or PJI, occurs when bacteria or fungi infect the tissues surrounding an artificial joint after knee or hip replacement surgery. It affects roughly 1 to 2 percent of primary joint replacements – a number that sounds small until you consider that more than one million joint replacements are performed in the United States each year. PJI is the leading cause of failure after total knee replacement and the third leading cause after total hip replacement. In severe cases, it can lead to amputation or death. Five-year mortality rates following PJI of the hip have been reported as high as 25 percent.
The core clinical challenge is diagnosis. PJI is notoriously difficult to identify, particularly in culture-negative cases – where standard laboratory cultures fail to grow an organism – and in borderline cases where symptoms and test results are ambiguous. There is no single gold-standard diagnostic test in the United States. Instead, physicians must synthesize multiple laboratory results, clinical findings, and imaging studies to reach a judgment. Published research has shown that even experienced specialists often struggle with diagnostic consistency, leading to delayed treatment when infection is present or unnecessary revision surgery when it is not.
The new tool, called SynTuition, is a machine learning algorithm that analyzes results from Zimmer Biomet’s Synovasure comprehensive PJI test panel – a battery that measures 11 biomarkers in synovial fluid drawn from the suspect joint. Rather than requiring a physician to manually interpret each biomarker result and weigh them against diagnostic criteria, the algorithm identifies patterns across all 11 markers simultaneously and generates a continuous probability score from 0 to 100, estimating the likelihood that the patient has a periprosthetic joint infection.
The model was built using a dataset of more than 104,000 synovial fluid samples collected from nearly 3,000 institutions between 2018 and 2024 — an enormous training dataset by orthopedic research standards.
In the study, 12 physicians were presented with 274 clinical vignettes representing suspected PJI cases and asked to provide diagnoses. Their results were compared against both an expert-adjudicated clinical reference standard and the SynTuition algorithm’s output.
The results were striking. The AI algorithm achieved 96 percent overall agreement with the expert-adjudicated reference, compared to 90.8 percent for the pooled physician group. More importantly, the algorithm showed dramatically lower diagnostic uncertainty. Physicians frequently expressed indecision – particularly in culture-negative and borderline cases, which are precisely the cases that drive the most disputes in clinical practice. The AI tool provided a definitive probability score in every case, eliminating the ambiguity that leads to diagnostic delays.
The study also conducted a cost analysis. Misdiagnoses – both false positives (leading to unnecessary revision surgery) and false negatives (leading to delayed treatment and progression of infection) – carry significant economic consequences. The researchers found that the AI tool’s superior accuracy translated to meaningful cost reductions compared to standard physician-driven diagnosis.
This study is part of a broader trend in orthopedic medicine: the integration of artificial intelligence into clinical decision-making at critical junctures in patient care. Diagnosing PJI is one of the most consequential decisions an orthopedic surgeon makes – get it right, and the patient receives timely, targeted treatment; get it wrong in either direction, and the clinical and financial consequences are severe.