Artificial Intelligence Helping to Advance Bone Fracture Treatment, Assessment

Investigators have applied artificial intelligence techniques to gait analyses and medical records data to provide insights about individuals with leg fractures and aspects of their recovery.
 
A study published in the Journal of Orthopaedic Research uncovered a significant association between hospital readmission rates after fracture surgery and underlying medical conditions. Investigators also found correlations between underlying medical conditions and orthopedic complications, although these links were not significant.

“Our findings demonstrate the impact that integrating machine learning and gait analysis into orthopedic practice can have, not only in improving the accuracy of post-injury complication predictions but also in tailoring rehabilitation strategies to individual patient needs,” declared corresponding author Mostafa Rezapour, Ph.D., of Wake Forest University School of Medicine. “This approach represents a pivotal shift towards more personalized, predictive, and ultimately more effective orthopedic care.”
 
It was also apparent that gait analyses in the early post-injury phase offer valuable insights into the injury’s impact on locomotion and recovery. For clinical professionals, these patterns were key to optimizing rehabilitation strategies.
 
Dr. Rezapour said the study underscores the importance of adopting a holistic view that encompasses the mechanical aspects of injury recovery as well as patient health. “This is a step forward in our quest to optimize rehabilitation strategies, reduce recovery times, and improve overall quality of life for patients with lower extremity fractures,” he stated.