Author: University of Oxford
Source: Medical Xpress
Publish date: December 5, 2024
Study reveals machine learning outperforms surgeons in predicting knee replacement outcomes
Unicompartmental (partial) knee replacement (UKR) is a common procedure designed to relieve pain in patients with knee osteoarthritis. However, some patients experience poor outcomes, and it can be challenging for surgeons to determine why these issues arise.
A study from NDORMS, published in The Knee, has found that a machine learning model can better predict which patients will have poor outcomes after undergoing UKR surgery, compared to experienced orthopedic surgeons.
“It is often difficult, even for experienced surgeons, to determine why some patients have poor outcomes after partial knee replacements just by looking at the X-rays,” said lead author Dr. Jack Tu, Research Fellow in Clinical Biomechanics at NDORMS. “So we designed a study to understand whether AI had the potential to enhance surgical decision-making and improve patient care.”
The team trained a machine learning model to identify patterns in images that are associated with poor outcomes, and most of these images look normal to surgeons. They then analysed over 900 radiographs (X-rays) taken one year after surgery and compared the ability of seasoned surgeons to predict outcomes with that of the machine learning model.
The results were significant. The machine learning model accurately identified 71% of patients with poor outcomes, while the surgeons struggled, identifying between 0% and 7% of possible issues.
This approach using AI is different from how machine learning is typically applied in medicine. Rather than just replicating tasks that doctors can already do, the model was able to identify previously unknown visual markers that could indicate complications.
“If this technology was rolled out more widely using routine joint replacement registry data, it has the potential to significantly improve the results of partial knee replacement surgery,” added Dr. Tu.
“By pinpointing the specific imaging features that lead to bad outcomes, we may be able to learn from the result which may eventually lead us to modify surgical techniques or implant designs to reduce the risk of patient dissatisfaction.”
The researchers plan to further analyse the X-ray features the AI model used to make its predictions, which could provide surgeons with new insights to optimise patient selection and surgical technique for partial knee replacements.
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