Predictive Algorithm for Surgery Recommendation in Thoracolumbar Burst Fractures Without Neurological Deficits

This study explores using artificial intelligence to help doctors decide on the best treatment for thoracolumbar burst fractures (broken bones in the middle back) when there's no nerve damage. The goal is to create a tool that reduces differences in treatment decisions among surgeons. The researchers created a mathematical model that predicts whether surgeons would recommend surgery based on X-ray features like the certainty of posterior ligament damage, how broken the bone is, and the surgeon's location. The model showed that if there's a high certainty of posterior ligament damage, surgeons are very likely to recommend surgery. Also, surgeons in Europe and Asia are more likely to recommend surgery for severely broken bones compared to surgeons in North and South America.

• 1
  The predictive algorithm demonstrated excellent accuracy at 82.4% in determining treatment recommendations for thoracolumbar burst fractures.
• 2
  Certainty of PLC injury above 57.5% was highly predictive of receiving surgery, with a 97.0% chance.
• 3
  A high degree of comminution resulted in a higher chance of receiving surgery in Europe and Asia compared to North/South America.